Adanto Boosts Service & Cuts Costs with Big Data Analytics
Adanto’s Real-Time Big Data Analytics solution streamlined operations for a global HR leader, cutting abandoned calls from 18% to 5%, reducing hold times to 4 minutes, and enhancing real-time insights with advanced reporting and dashboards.
Really happy with Adanto’s work and your engineering capabilities in the C#/.Net back end development of our LSX platform. Our team has voted very high marks and would like to keep utilizing your services.
John Crowley
Chief Software Architect
Fujifilm NA Corporation, Imaging Division
Customer service is evolving faster than ever — and Agentic AI is leading the charge.
If you’ve heard buzz about AI handling customer interactions, here’s the truth: within the next 12 months, more than half of all customer service conversations will be managed by agentic AI systems. These aren’t your typical chatbots; they’re autonomous, proactive, and deeply contextual digital agents that understand your needs, make decisions on the fly, and act — all to deliver a seamless, personalized experience.
Table of Contents
What Is Agentic AI — And Why Should You Care?
Agentic AI takes AI-powered customer service to the next level. Unlike rule-based bots that simply respond to scripted prompts, agentic AI:
- Understands context — remembers past interactions and adapts conversations.
- Acts proactively — reaches out before problems arise or needs are voiced.
- Makes decisions autonomously — guiding customers and supporting agents alike.
Simply put, it’s customer service that thinks and acts smarter — like having a supercharged, empathetic team member available 24/7.
The AI Shift Is Happening — Fast
According to Cisco’s latest research:
- 56% of all customer interactions will be AI-handled within a year.
- 75% of business leaders believe proactive AI support will reduce customer churn.
- 65% expect to boost customer lifetime value through AI-driven insights.
That’s not just technology hype. It’s a strategic transformation reshaping how companies connect with customers — driving loyalty, satisfaction, and revenue.
How Agentic AI Changes Customer Service
Traditional customer support often struggles with:
- Long wait times
- Repetitive questions
- Burned-out agents
- Reactive responses
- Fragmented experiences
Agentic AI flips this script. This means happier customers and empowered agents — a win-win.
Top Agentic AI Use Cases Transforming Customer Service
Here’s where agentic AI really shines:
- Autonomous Customer Support
Instantly handles routine queries, reducing wait times and deflecting up to 60% of tickets. - Contextual Memory
Keeps track of past conversations so agents respond faster and smarter. - Proactive Outreach
Predicts customer needs — like renewals or potential issues — and acts before you even ask. - Real-Time Assistance
Provides live recommendations on next steps, resources, and tone during customer calls or chats.
Sentiment Detection
Reads emotions to support both customers and agent well-being, tailoring responses with empathy.
Agentic AI Augments Humans — It Doesn’t Replace Them
A common misconception is that AI will replace people. The reality is the opposite. Agentic AI acts as a smart copilot — augmenting human agents to:
- Make better decisions
- Work more efficiently
- Deliver richer, personalized experiences
This collaboration means more productive teams and happier customers.
Conclusion
Agentic AI is revolutionizing the way businesses interact with their customers, making service faster, smarter, and more human-centric. By understanding context, acting proactively, and collaborating with human agents, these intelligent systems turn every customer interaction into an opportunity to build loyalty and drive growth.
Companies that embrace agentic AI now will gain a serious competitive edge — delivering seamless experiences, reducing operational costs, and empowering their teams to focus on what truly matters: creating lasting relationships with customers.
Want to use AI in your business?
Get in touch with the Adanto Software Team today to see how we can help.
“The Adanto team was of the first groups of developers I started working with at Sensaria and really one of the constants during my time here. Thank you for everything over the years – most notably your flexibility and teamwork with the on-shore team, and teaching us key Polish terms along the way. I’m happy to say that the concept of “Little Friday” has spread around Sensaria!”
Polly Tobias
Technical Project Manager
Circle Graphics
Finding the right product online can be frustrating. Search results often miss the mark. Filters don’t help much unless you already know what to look for. And recommendation engines mostly rely on what you or others bought in the past.
What if online shopping felt more like talking to someone who actually helps? Not a chatbot with canned replies, but an intelligent system that understands what you need—even if you don’t know how to ask for it.
That’s the promise of AI agents. They’re changing how product discovery works by guiding users like real assistants.
Table of Contents
What Are AI Agents?
AI agents are not just tools that respond to commands. They act more like helpers that can make decisions, ask questions, and carry out tasks.
In e-commerce, AI agents help customers discover the right products. They don’t just throw recommendations based on past clicks. They listen, ask follow-up questions, and adapt their suggestions in real-time.
Example: A shopper says, “I need a laptop for school.” Instead of showing 100+ random laptops, the AI agent asks, “Do you prefer something lightweight? Any specific software you’ll be using?” Based on that, it narrows down the list to options that actually fit the user’s needs.
How Product Discovery Works Today
Most online stores use filters, search bars, and static recommendation engines.
You type in a keyword like “running shoes,” and get hundreds of results. You try filtering by brand, price, or size—but the process is often slow and confusing.
Traditional recommendation systems use browsing or purchase history. If you bought hiking boots last month, you might see more boots—even if you’re now shopping for sandals.
This leads to a poor experience. People scroll, compare, get overwhelmed, and sometimes give up without buying anything.
Why Traditional Tools Miss the Mark
These older systems assume too much. They expect users to know what they want and how to ask for it in the “right” way. But most people shop with vague goals.
Let’s say someone wants a gift for a tech-savvy friend. They may not know whether to search for smartwatches, headphones, or accessories. A keyword search won’t help much.
Filters also fall short when needs are complex.
For example, “eco-friendly office chair for a small home office” doesn’t map well to standard filters like “material” or “brand.”
Recommendation engines rely heavily on data from past users. But past behavior doesn’t always predict future intent—especially for new or infrequent buyers.
What Makes AI Agents Different
AI agents act more like smart assistants than tools. They combine search, filtering, comparison, and conversation in one experience.
They can:
- Understand natural language questions
- Ask follow-ups to clarify vague inputs
- Compare multiple products based on specific needs
- Adjust results in real time
Example: A shopper says, “I need a laptop for school.” Instead of showing 100+ random laptops, the AI agent asks, “Do you prefer something lightweight? Any specific software you’ll be using?” Based on that, it narrows down the list to options that actually fit the user’s needs.
They’re not just filtering—they’re reasoning.
Challenges to Consider
AI agents aren’t perfect. They need accurate and well-structured data to work properly. If your product catalog is outdated or lacks key details (like noise levels or fabric types), the agent can’t make smart choices.
They also need to respect privacy. Over-personalization can feel invasive.
Not everyone wants a “conversation” when shopping. Some users prefer quick browsing. That’s why AI agents should be optional and easy to exit or skip.
What This Means for Online Retailers
AI agents can improve the shopping experience, but they’re not plug-and-play.
To get them right, retailers need to:
- Understand their customer journey
- Structure their product data well
- Choose the right AI platform or partner
- Test with real users
Done right, AI agents can lower bounce rates, increase conversions, and reduce returns. They help people find what they’re actually looking for.
Conclusion
Product discovery shouldn’t feel like work. AI agents make it easier for people to find what fits their needs—even if they don’t know how to say it.
They’re more than search tools. They’re decision helpers.
And they’re already starting to change how people shop online.
Adanto builds custom AI-driven solutions that improve search, discovery, and customer satisfaction.
Schedule a short call to explore what’s possible.
Adanto delivers multi-function CRM platform in record time
Adanto specializes in delivering powerful CRM platforms for business and national sales teams in utilities, combining rapid deployment with robust frameworks, cloud tech, and seamless integrations—without compromising quality.
Adanto implements BI for Maconomy ERP with SAP BO
Adanto delivers advanced financial planning & reporting tools for a global leader in the professional placement services sector. By deploying SAP BusinessObjects Universes, Adanto streamlined access to critical financial data, enabling custom reporting & dash-boarding.
Adanto has provided superior software engineers we needed to complete our multiple data migration and integration efforts
Krystian Piwowarczyk
Cybersecurity Manager
Vector Synergy
Thank you for putting together with Alvin the architecture, and plan to make it easy for potential customers who are using Shopify for their eCommerce platform to use Fujifilm’s personalization engine by creating a plug-in/extension.
Jim Dolce
Vice President New Business and Software Development
Fujifilm NA Corporation, Imaging Division
Adanto has proven to be an invaluable strategic partner for Cloudify. Having spent many years working with various engineering services company’s Adanto excels not only in the quality and speed of services they deliver but also in their commitment to fairness and transparency
Luca Rajabi
VP, Solutions
Cloudify
Majic (Maciek) and the rest of Adanto team were great to work with. Thank you.
Jeff Keihl
Functional Architect, IT Development, Financial Services Applications
Robert Half
You were awarded a contract based on your ability to deliver unmatched technical innovation skills, solid track record of stable and predictable results, offered via progressive people and results-oriented culture.
Brett Roscoe
GM
Dell Software
You have truly saved our product from a near demise caused by an incompetence of a far-shore supplier
Bob Maeser
CTO
Quest Software
We absolutely appreciate all of Adanto’s help in getting Outside Financial off the ground
Sonia Stainway
CEO
Outside Financial
We have been continually reassured of Adanto’s versatile portfolio of expertise while tasked with deploying a major multi-national pharmaceutical company’s Secure Transit VPC across multiple geographic regions spanning two continents. Adanto’s ability to work with geographically dispersed teams and deliver on the customer’s terms in multiple timezones is a true differentiator few engineering services company’s can offer.
Francesco Alongi
Senior Manager Cloud Strategy
Advanced Informatics and Analytics
Astellas Pharma
Adanto software team with Piotr really rocked.
Scott Francis
Sr. Director Applications
Robert Half
Adanto has helped Robert Half and Protivity accelerate our services deliverry and lower our development costs.
James Johnson
VP of IT
Robert Half
I am giving my highest recommendation for Adanto Software. Having dealt previously with a tech who answered in a day or two, Adanto’s responses were truly impressive.
Paula Miller
CEO
Iconic Idaho
Most e-commerce personalization is still basic. It shows “related items” or “people also bought.” But today’s customers expect more than that. They want help, not suggestions.
Agentic AI makes this possible.
It can understand intent, take action, and guide users through tasks — like a smart assistant inside your store. In this article, we’ll look at how agentic AI is changing the e-commerce experience.
Table of Contents
Why Today’s Personalization Falls Short
Most e-commerce platforms do this:
- You looked at product A → So here’s product B.
- You added one thing → So here’s a “frequently bought together” set.
- You visited twice → Here’s a discount.
It works — until it doesn’t.
These systems rely on patterns, not purpose. They don’t understand what the customer is trying to do.
And when things change — trends, prices, demand, seasons — the system can’t keep up.
What Agentic AI Enables in E-commerce
Imagine you walk into a store. Before you say a word, someone already knows what you’re looking for — not in a creepy way, but because they’ve seen people like you before. They notice what you’re holding, how long you stare at the shelf, and what questions you pause to ask in your head.
Then they say:“Hey, based on what you need — here’s a better way to do this.”
That’s what Agentic AI does. It doesn’t wait for instructions. It watches, learns, and acts — while the shopper is still deciding.
An agent can:
- Understand what the user is trying to do (intent detection)
- Ask questions to fill in missing context
- Decide what steps are needed to help them reach that goal
- Adapt the UI, product options, or offer structure in real time
- Learn from what works or fails — and adjust behavior
Use Case #1: Guided Product Discovery
The problem: Shoppers are overwhelmed. They don’t know what they need — especially with technical or multi-part products.
What happens with an agent:
Someone visits your store looking for gear to film cooking videos. Instead of scrolling through 50 cameras, they answer 3 simple questions. The agent suggests a full setup: camera, tripod, lighting — all matched to their use case. Ready to buy in one click.
Why it matters:
Fewer abandoned sessions. More confident purchases. No guesswork.
Then they say:“Hey, based on what you need — here’s a better way to do this.”
That’s what Agentic AI does. It doesn’t wait for instructions. It watches, learns, and acts — while the shopper is still deciding.
An agent can:
- Understand what the user is trying to do (intent detection)
- Ask questions to fill in missing context
- Decide what steps are needed to help them reach that goal
- Adapt the UI, product options, or offer structure in real time
- Learn from what works or fails — and adjust behavior
Use Case #2: Smart Bundling and Upselling
The problem: Upsells often feel random or pushy. They don’t add real value.
What happens with an agent:
A shopper adds a laptop. The agent builds a remote work bundle — keyboard, monitor, warranty — customized to what makes sense for this model and use. Pricing adapts to stock, season, and margin.
Why it matters:
Higher order value. Smarter promos. Better experience.
Use Case #3: Goal-Based Shopping Journeys
The problem: People often shop with a goal — not a product in mind.
What happens with an agent:
Someone’s planning a 3-day winter hike. The agent builds a checklist: layered clothes, tent, food kits, thermal gear. Checks delivery dates to match their trip. Adds backup options.
Why it matters:
More complete orders. Fewer forgotten items. Real help, not just suggestions.
Use Case #4: Post-Purchase Agents
The problem: After checkout, most brands disappear — or send generic emails.
What happens with an agent:
You buy an espresso machine. The agent follows up with setup help, cleaning tips, offers for beans or accessories, and a reminder to leave a review — timed to when you’ve actually used it.
Why it matters:
Longer retention. More upsells. Better product experience.
Conclusion
Agentic AI won’t replace your team. But it can do what static systems can’t: act in real time, for real people, based on real context.
It turns your store into something smarter. Something that helps users get what they came for — without the friction.
If you care about lifetime value, conversion, and customer experience, this is worth exploring.
Want to use AI in your business?
Get in touch with the Adanto Software Team today to see how we can help.
Adanto Delivers AWS-Powered Job Alert Marketing Automation
Adanto delivers a multilingual marketing automation workflow using AWS, Salesforce, Eloqua, and Drupal for the global web marketing team of a leading Silicon Valley consulting enterprise.
“The Adanto team was of the first groups of developers I started working with at Sensaria and really one of the constants during my time here. Thank you for everything over the years – most notably your flexibility and teamwork with the on-shore team, and teaching us key Polish terms along the way. I’m happy to say that the concept of “Little Friday” has spread around Sensaria!”
Polly Tobias
Technical Project Manager
Circle Graphics
Honeywell Int’l Inc.
Adanto Delivers IoT-Enabled HA Telemetry for Gas Pipelines
Adanto delivered an IoT-enabled HA telemetry solution for gas pipelines, ensuring 99.9% uptime, real-time monitoring, and auto-healing. Operational for over eight years, it provides reliable, scalable, and environmentally friendly gas transportation.
Key Results
$2.7M
Annual savings through reduced maintenance, downtime, & optimized energy usage
$1.5M
Annual costs savings from incidents prevention & ensured compliance
$2.1M
Annual revenue growth from more client value of increased throughput & better utilization
Technologies used
- Infrastructure:
- IBM Power 6 servers running IBM AIX operating system
- Power VM for load separation
- Power HA for Telemetry System HA
- Oracle RAC for database HA
- Moxa Industrial Ethernet switches
- CISCO Catalyst switches and routers
- Brocade SAN switches
- IBM Disk Storage & Tape Storage: 2 tape drives and 48 LTO-3 tapes.
- Tivoli Storage Manager
- Performance:
- 99.9% system availability & 500ms data sampling density
- Full redundancy of hardware and software components
- 6 months data retention for raw data
- 3-year data retention for data reporting
- RTO < 8h & RPO < 48h
- Bare metal recovery
- >3TB of telemetry data
- >1000 data samples read per second
- Max 40ms recovery time after network topology change
Challenge
Our oil and gas pipeline client faced challenges operating in remote, inaccessible terrain, requiring real-time monitoring of gas flow. Manual oversight was impractical, and the client needed a highly available, IoT-enabled solution to process large volumes of telemetry data, ensure reliability, minimize maintenance, and maintain safety and regulatory compliance.
Key goals
Real-Time Monitoring: Ensure continuous, real-time tracking of natural gas flow metrics for safety and efficiency
High Availability: Deliver a 99.9% uptime system with auto-healing and redundancy to prevent disruptions
Scalability: Handle high-density telemetry data with scalable infrastructure and long-term data retention
Low Maintenance: Provide a self-sustaining solution with automated updates for remote, inaccessible locations
Solution
Adanto delivered an IoT-enabled high-availability telemetry solution for real-time monitoring and management of natural gas pipelines in remote and challenging terrains. This solution provided safe, reliable, and efficient operations with minimal manual intervention, meeting the client’s goals for scalability, reliability, and environmental compliance. The solution included:
- High-Availability Clusters: Designed for 99.9% uptime, ensuring continuous monitoring and operational reliability
- IoT-Enabled Sensors and Systems: Integrated real-time data collection and processing of over 1,000 telemetry data samples per second.
- Robust Infrastructure: Leveraged IBM Power servers, Oracle RAC databases, industrial-grade networking equipment, and cloud storage for scalability and performance.
- Automation and Resilience: Implemented auto-healing features for system updates, firmware upgrades, and software patches with minimal maintenance.
- Secure Data Retention: Ensured long-term storage of telemetry data (6 months for raw data, 3 years for reporting) and rapid recovery capabilities.
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In 2024, global fraud losses reached $485 billion, with digital payment fraud rising by 18% year-over-year. The fraud landscape is evolving fast—driven by automation, AI-assisted scams, and synthetic identities. Traditional fraud detection systems struggle to keep pace, largely because they rely on rule-based logic and reactive human workflows.
To respond in real time, organizations need systems that can act on their own. This is where autonomous AI agents come into play. These agents don’t just identify fraud—they execute decisions, trigger actions, and evolve with each case they process. In short, they operationalize intelligence at machine speed.
In this article, I’ll explain how these agents work, what they’re capable of, and where they’re already delivering measurable impact — particularly in financial services, insurance, and e-commerce.
Table of Contents
What Is an Autonomous AI Agent?
Autonomous AI agents are software entities that can perceive data, reason over it, make decisions, and take actions—without human intervention. These agents typically combine:
- Real-time data ingestion
- Machine learning (often anomaly detection, clustering, or reinforcement learning)
- A decision-making engine
- A trigger mechanism (e.g., blocking, alerting, escalating)
Unlike static ML models embedded in a rules-based system, autonomous agents are built for continuous operation. They interact with other systems, manage workflows, and adapt based on outcome feedback.
Why Traditional Fraud Detection Falls Short?
Most fraud detection pipelines today are reactive:
- Transactions are scored based on predefined thresholds
- Alerts are queued for review
- Analysts triage cases manually
- Action is taken hours or days later
This approach introduces delays, fatigue, and inconsistency. Worse, fraudsters exploit these weaknesses with rapid attacks that mimic normal behavior. Static systems can’t detect these dynamic patterns fast enough—and they certainly can’t respond in real time.
How Autonomous Agents Address the Gap?
Autonomous agents are designed to close the decision-action loop. Here’s how:
- Continuous monitoring: They evaluate live data streams rather than periodic batches.
- Pattern learning: They learn over time—detecting not just known fraud, but emerging anomalies.
- Decision execution: They act immediately—freezing accounts, flagging claims, or launching investigation workflows.
- Feedback loops: They learn from past actions, enabling them to refine future decisions.
Agents can also operate across systems—integrating with CRMs, payment processors, document repositories, and third-party data providers.
Key use cases and results
Let’s explore where autonomous agents are already in use, and what kind of value they’re delivering.
Financial Services – End-to-End Case Handling
A credit union partnered with Accelirate to streamline its fraud operations. An AI agent was deployed to:
- Check transactions across Symitar and Extranet
- Match against historical behavior
- Eliminate duplicates
- Trigger escalation workflows
Results:
- 657 analyst hours saved annually
- 98% reduction in processing errors
- $19,800 in direct cost savings
Insurance – Claims Fraud Detection
An insurer used an agent to review low-dollar claims submitted within short timeframes across multiple user profiles. The agent:
- Flagged matching metadata and document reuse
- Pulled historical claims from different user IDs
- Auto-generated fraud reports for the investigation team
Results:
- 245% ROI within the first year
- $320,000+ in savings
- 62% reduction in claim resolution time
E-Commerce – Loyalty Abuse Prevention
Retailers are increasingly targeted by bot-driven attacks—fake signups, coupon abuse, and identity farming. AI agents can:
- Detect fake account clusters (shared IPs, browser fingerprints, timing anomalies)
- Flag attempts to exploit loyalty programs
- Pause reward disbursement and notify risk teams
Impact: Fewer false positives than rules-based systems, with real-time enforcement and reduced operational load on fraud teams.
Fintech Lending – Synthetic Identity Detection
Fintech lenders deal with high volumes and thin data. One client used agents to catch applications that:
- Used slightly altered identity data (e.g., different DOB or SSNs with matching addresses)
- Applied to multiple loan products in rapid sequence
- Reused documents across supposedly unrelated accounts
The agent connected the dots and auto-rejected risky applicants before credit was issued.
Known Challenges And Risks
No system is perfect. There are trade-offs to consider:
- Explainability: Deep-learning agents can make decisions that are hard to justify without traceable logic. This is a concern for regulated industries.
- Bias: If agents are trained on biased data, they may reinforce discrimination (e.g., falsely flagging users based on geography or demographic patterns).
- Overreach: Agents acting too aggressively (e.g., false account freezes) can damage user trust and create compliance risks.
To mitigate these, agents should be built with human-in-the-loop oversight, audit trails, and risk thresholds that define when automation is allowed to act independently.
What To Expect Going Forward
Autonomous AI agents will evolve beyond single-use cases. We’re seeing early adoption of multi-agent systems, where:
- One agent focuses on transaction-level fraud
- Another monitors identity risk over time
- A third handles response orchestration and user communication
This layered approach builds resilience and adaptability.
We’re also likely to see closer integration with identity verification, KYC, behavioral biometrics, and external fraud intelligence feeds.
Conclusion
Autonomous agents are not silver bullets. But they are a necessary shift. As fraud tactics grow more automated, detection and prevention must move at the same pace.
The true value of these agents isn’t just speed—it’s consistency, scalability, and reduced human burden. In areas like fintech, e-commerce, and insurance, the business case is already clear.
Want to use AI in your business?
Get in touch with the Adanto Software Team today to see how we can help.
Talking to a business should feel easy. But long wait times, repeated questions, and poor support can frustrate customers. That’s where Voice AI agents come in. These tools are designed to handle real-time conversations using artificial intelligence. They can understand what people say, figure out what they need, and respond with a voice that sounds natural. Unlike basic phone menus or chatbots, Voice AI agents can hold two-way conversations and offer real help—without involving a human agent every time.
In this article, we’ll walk through what a Voice AI agent is, how it works, what features matter, and how it can help your business. If you’re exploring ways to improve customer service or reduce call center costs, this guide will give you a solid starting point.
Table of Contents
What is a Voice AI Agent?
A Voice AI Agent is a virtual assistant that communicates with users via spoken language. Unlike traditional IVR (Interactive Voice Response) systems that follow rigid scripts and often frustrate users, Voice AI Agents use artificial intelligence to understand, interpret, and respond in a natural, human-like way.
They can handle a wide range of tasks:
- Answering customer support queries
- Booking appointments
- Providing product recommendations
- Processing orders
- Collecting feedback
Think of them as digital team members who never sleep, don’t lose patience, and continuously improve over time.
How AI Voice Agents Work
A Voice AI Agent works through a blend of voice recognition, natural language processing, machine learning, and backend integration. When a user speaks, the AI listens, deciphers intent, and crafts a meaningful response—all in real time.
Here’s a typical flow:
- User Speaks: “I want to know my order status.”
- Voice Input is Captured via microphone or phone call.
- Speech-to-Text (STT): Converts spoken words into written text.
- Natural Language Understanding (NLU): Interprets what the user means.
- Dialog Manager: Decides how to respond.
- Text-to-Speech (TTS): Converts the reply back into speech.
- Voice Output: “Sure, let me check your order. Can I have your order number?”
It all happens in seconds—and gets smarter with every conversation.
Key Components of Voice AI Agent Architecture
To understand how these agents operate, let’s break down their core architecture:
- Automatic Speech Recognition (ASR)
This component transforms spoken language into text. Accuracy here is crucial—especially with different accents, speeds, or background noise. - Natural Language Processing (NLP)
NLP handles two parts: understanding user intent and generating a human-like response. It’s what allows the agent to grasp the meaning behind words. - Dialog Management System (DMS)
The DMS decides how the agent responds. It uses context, previous interactions, and logic flows to ensure the conversation feels natural. - Text-to-Speech (TTS)
Converts the AI’s response from text back to speech. Modern systems now have expressive, natural-sounding voices with varied tones and emotions. - Backend/API Integrations
To be truly useful, Voice AI Agents must connect with CRMs, order systems, databases, calendars, and other business tools.
Training & Analytics Layer
This layer helps the system learn from user interactions, spot friction points, and improve accuracy over time.
Important Features of an AI Voice Agent
What makes a voice AI agent powerful and business-ready? Here are key features to look for:
- Real-Time, Natural Conversations: No awkward pauses or robotic replies. It should talk like a real person.
- Context Retention: Good agents remember previous interactions within a session—sometimes even across sessions.
- Multilingual Support: Serve customers in their native language or dialect.
- Personalization: Greet users by name, remember preferences, and adapt responses.
- 24/7 Availability: AI agents never clock out.
- Scalability: Handle thousands of conversations simultaneously without delays.
Seamless Handover: When needed, they can transfer the conversation to a human agent—complete with conversation history.
Benefits of Voice AI Agents for Your Business
Why should a business invest in a voice AI agent?
Imagine you’re running an e-commerce company. During peak shopping season, your support team is swamped with queries: “Where’s my order?”, “Can I return this item?”, “What’s your exchange policy?”
Instead of hiring and training dozens of temporary agents, a voice AI agent can:
- Handle 80% of repetitive queries automatically
- Reduce customer wait time to near zero
- Free up your human team for complex cases
- Cut operational costs significantly
- Increase customer satisfaction with quick, consistent answers
And in industries like banking, healthcare, and retail, AI voice agents are already showing measurable ROI—from improved conversion rates to reduced churn.
Conclusion
Voice AI Agents are a smart way for businesses to talk to customers quickly and easily. They can answer questions, help with orders, and give support—any time, day or night.
They don’t replace your team, but they help your team work better by handling simple, repetitive tasks. This means faster service for customers and lower costs for your business.
Let’s Talk
At Adanto Software, we help companies build and use Voice AI Agents that fit their needs. If you’re thinking about using AI to improve your customer experience, we’re here to help you get started.
Adanto SOC consultation and proposal was very compelling and on par with the GE proposal. Your security engineers are very caapable.
Mark Hopkins
Security Operations (SOC) Lead
Robert Half
In my continuing work at F/22 Consulting I engage numerous companies that all face the ever-present and escalating challenge of developing and managing software projects in order to remain competitive. Not only did Adanto graciously and professionally rise to every challenge (even the unreasonable!) but they always completed the projects on time and exceeded expectations unfailingly. I am happy to have this resource available to recommend on what I’m sure will be a frequent basis.
Frank Baillargeon
CEO
Iconic Idaho
Adanto and especially Magic were instrumental in getting our iTrack Reporting Workstream Project on track and successful, beyond expectations. Thank you. The whole team was wonderfule to work with in on site in San Ramon, CA and off-site from Poland
Thuy Nguyen
Sr. Manager, IT Development
Robert Half
Adanto has added great value to Brett, our CTO, and his team to get us off the ground in eCommerce
Andrew Cousin
CEO
Circle Graphics/Sensaria
Finding the right product online can be frustrating. Search results often miss the mark. Filters don’t help much unless you already know what to look for. And recommendation engines mostly rely on what you or others bought in the past.
What if online shopping felt more like talking to someone who actually helps? Not a chatbot with canned replies, but an intelligent system that understands what you need—even if you don’t know how to ask for it.
That’s the promise of AI agents. They’re changing how product discovery works by guiding users like real assistants.
Table of Contents
What Are AI Agents?
AI agents are not just tools that respond to commands. They act more like helpers that can make decisions, ask questions, and carry out tasks.
In e-commerce, AI agents help customers discover the right products. They don’t just throw recommendations based on past clicks. They listen, ask follow-up questions, and adapt their suggestions in real-time.
Example: A shopper says, “I need a laptop for school.” Instead of showing 100+ random laptops, the AI agent asks, “Do you prefer something lightweight? Any specific software you’ll be using?” Based on that, it narrows down the list to options that actually fit the user’s needs.
How Product Discovery Works Today
Most online stores use filters, search bars, and static recommendation engines.
You type in a keyword like “running shoes,” and get hundreds of results. You try filtering by brand, price, or size—but the process is often slow and confusing.
Traditional recommendation systems use browsing or purchase history. If you bought hiking boots last month, you might see more boots—even if you’re now shopping for sandals.
This leads to a poor experience. People scroll, compare, get overwhelmed, and sometimes give up without buying anything.
Why Traditional Tools Miss the Mark
These older systems assume too much. They expect users to know what they want and how to ask for it in the “right” way. But most people shop with vague goals.
Let’s say someone wants a gift for a tech-savvy friend. They may not know whether to search for smartwatches, headphones, or accessories. A keyword search won’t help much.
Filters also fall short when needs are complex.
For example, “eco-friendly office chair for a small home office” doesn’t map well to standard filters like “material” or “brand.”
Recommendation engines rely heavily on data from past users. But past behavior doesn’t always predict future intent—especially for new or infrequent buyers.
What Makes AI Agents Different
AI agents act more like smart assistants than tools. They combine search, filtering, comparison, and conversation in one experience.
They can:
- Understand natural language questions
- Ask follow-ups to clarify vague inputs
- Compare multiple products based on specific needs
- Adjust results in real time
Example: A shopper says, “I need a laptop for school.” Instead of showing 100+ random laptops, the AI agent asks, “Do you prefer something lightweight? Any specific software you’ll be using?” Based on that, it narrows down the list to options that actually fit the user’s needs.
They’re not just filtering—they’re reasoning.
Challenges to Consider
AI agents aren’t perfect. They need accurate and well-structured data to work properly. If your product catalog is outdated or lacks key details (like noise levels or fabric types), the agent can’t make smart choices.
They also need to respect privacy. Over-personalization can feel invasive.
Not everyone wants a “conversation” when shopping. Some users prefer quick browsing. That’s why AI agents should be optional and easy to exit or skip.
What This Means for Online Retailers
AI agents can improve the shopping experience, but they’re not plug-and-play.
To get them right, retailers need to:
- Understand their customer journey
- Structure their product data well
- Choose the right AI platform or partner
- Test with real users
Done right, AI agents can lower bounce rates, increase conversions, and reduce returns. They help people find what they’re actually looking for.
Conclusion
Product discovery shouldn’t feel like work. AI agents make it easier for people to find what fits their needs—even if they don’t know how to say it.
They’re more than search tools. They’re decision helpers.
And they’re already starting to change how people shop online.
Adanto builds custom AI-driven solutions that improve search, discovery, and customer satisfaction.
Schedule a short call to explore what’s possible.
I am very excited about how Adanto has helped Circle Graphics to utilize the eCommerce and Magento expertise and very efficient deployment model.
Bret McInnis
CTO
Circle Graphics/Sensaria
Adanto has helped us be more productive and monitor costs of an AWS cloud
James Wetzig
Sr. Manager, Architecture & Infrastructure Platform Delivery
Robert Half
Your engineers have been doing great and are very proactive
Jerry Jarvis
Sr. Director of IT
Protiviti
Great knowledge and quick response in architecting the mobile app with its entire delivery data platform
Harg Malhi
VP, Engineering
American Express
Customer support is changing. For years, businesses relied on scripts and predefined workflows to handle conversations. It worked—up to a point.
Most support interactions still start the same way.
A customer runs into a problem. They reach out for help. And what do they get?
A chatbot that repeats their question. A phone system that loops them around. A support agent stuck reading from a script.
The customer gets frustrated. The agent feels stuck. Nobody wins.
This isn’t how support should work in 2025. And thanks to Agentic AI, it doesn’t have to.
Table of Contents
Why Scripted Support Falls Short
Think about the last time you contacted support. You probably had one clear goal—get something fixed.
But instead, you got a list of steps that didn’t match your issue. Or had to repeat yourself three times. Or got passed between three people who all asked for the same details.
That’s what scripted systems do. They assume every problem is simple. They treat every customer the same.
But real problems are messy. Customers don’t follow scripts. So why should support?
What Agentic AI Does Better
Agentic AI doesn’t follow a script. It follows a goal.
Instead of matching inputs to preset replies, it looks at the bigger picture. It can ask questions, gather missing info, make decisions, and even take action—like updating an account or sending a follow-up email.
It can handle back-and-forth without losing context. It remembers what the customer said earlier. And it can change its approach if the situation shifts.
This makes the conversation feel more natural. And it gets things done faster.
Real-World Use Cases
Here’s how companies are already using Agentic AI in support:
- In e-commerce, Agentic AI is handling returns, tracking packages, and even flagging repeat fraud attempts.
- In fintech, it’s guiding users through document verification, clarifying account rules, or escalating flagged transactions.
- In travel, it’s helping passengers rebook flights, offer options, and reissue tickets—all while dealing with weather delays.
- In retail, it’s solving problems before they reach a live agent, or freeing up agents to focus on escalations.
These aren’t just FAQs. They’re real tasks that usually need human input. Agentic AI can now handle many of them—end to end.
What This Means for Support Teams
Agentic AI isn’t here to replace support teams. It’s here to take care of the boring stuff.
Agents don’t need to answer the same password-reset question 100 times a day. They don’t need to copy-paste policy links. Or route simple issues to other teams.
Instead, they can focus on what matters—complex cases, sensitive topics, or customers who really need a human touch.
Support roles will shift. But they won’t disappear. Teams will need new skills, like prompt design, oversight, and exception handling.
Conclusion
Scripted support had its time. But it’s no longer enough.
Customers want faster, smarter, more flexible help. Agentic AI can deliver that. It works with goals, not rigid flows. It handles complexity better. And it gets closer to how people actually talk.
We’re moving toward a support system that’s more intelligent, more efficient, and less frustrating.
Adanto Software helps businesses design and build intelligent support systems using real Agentic AI.
Artificial intelligence has come a long way – from basic automation to powerful language models. But the real revolution is happening now with Agentic AI: a new class of intelligent systems that can autonomously reason, plan, and act in pursuit of goals.
In this blog, we’ll break down what Agentic AI is, why it matters, and what makes it the most powerful AI architecture yet.
Table of Contents
What Is Agentic AI?
Agentic AI refers to intelligent software agents capable of setting goals, making decisions, and acting autonomously in dynamic environments—without the need for step-by-step human instruction.
These AI agents can:
- Interpret high-level objectives
- Break them down into tasks
- Decide on a course of action
- Execute and adapt in real time
In simple terms: Agentic AI thinks, acts, and learns independently—delivering proactive value rather than passively responding to commands.
Why Agentic AI Is a Paradigm Shift
Artificial Intelligence has evolved in waves:
- Traditional AI was built on rigid rules, statistical models, and pattern recognition—great for automation, but limited to narrow, pre-defined tasks.
- Generative AI brought creativity and contextual understanding, producing human-like text, images, and code from natural language prompts—but it still depends on static inputs and doesn’t take initiative.
Now, Agentic AI is redefining the entire model. It blends the best of previous AI generations—pattern recognition, generative capability, and contextual reasoning—with a new layer of autonomy and intentionality. These AI agents don’t just respond to prompts or follow workflows. They set goals, make decisions, and adapt their strategies dynamically—much like a human employee or collaborator would.
This shift is architectural.
Shift in Capabilities
1. From Reactive to Proactive
Traditional AI and chatbots wait for user input before acting. Agentic AI, by contrast, can anticipate needs, detect opportunities or risks, and take action—sometimes before a human even notices the problem.
🧠 Example: An AI sales agent notices a drop in pipeline velocity and automatically re-engages cold leads or recommends campaign changes—without waiting for a prompt.
2. From Static Rules to Dynamic Learning
Earlier AI systems were rule-based—requiring constant tuning, training, and human oversight. Agentic AI evolves through feedback loops, reinforcement learning, and real-time environmental cues, continuously improving its own performance.
🔁 Example: A customer support agent learns that certain ticket types lead to high churn. It starts escalating those tickets faster and suggesting new macros to human reps.
3. From Task Automation to Strategic Autonomy
Automation solves isolated tasks. Agentic AI tackles complex, multi-step goals that require reasoning, prioritization, and cross-functional coordination.
🎯 Example: In e-commerce, instead of just recommending a product, an agent can run a sequence of actions: detect cart abandonment, tailor follow-up offers, test different discount levels, and adapt based on conversion success—all autonomously.
Core Capabilities of Agentic AI
1. 🛰 Autonomy
Autonomy is the defining trait of Agentic AI. These agents can initiate actions, orchestrate processes, and make decisions independently—without waiting for step-by-step instructions or constant human intervention.
Rather than being reactive, autonomous agents observe the environment, recognize triggers, and take meaningful actions—all on their own.
Example in Fintech:
An agent monitoring financial transactions identifies a suspicious pattern indicative of potential fraud. Instead of just flagging it, the agent pauses the transaction, initiates a real-time compliance review, and notifies relevant teams—all autonomously.
2. 🧠 Reasoning
Reasoning gives agents the ability to analyze data, weigh alternatives, and make logic-driven decisions – even in ambiguous or dynamic environments.
Agents aren’t just rule-followers—they’re decision-makers. They can process unstructured inputs, understand context, and take the most appropriate next step based on situational logic and learned patterns.
Example in E-commerce:
An AI agent reviews a shopper’s recent behavior: abandoned cart, high browsing time, and frequent returns. It decides whether to offer a discount, recommend an alternative product, or trigger a loyalty email—based not on a script, but on intelligent evaluation of buyer intent.
3. 🎯 Goal-Setting and Planning
Agentic AI doesn’t just execute tasks—it can set goals, break them down into subtasks, choose how to proceed, and dynamically re-plan based on progress.
Agents understand objectives, create strategies to achieve them, and adapt those strategies over time. This makes them capable of managing multi-step processes and aligning with broader business outcomes.
Example in Customer Service:
An AI agent receives an overarching goal: “Reduce response time for high-priority tickets.” It analyzes ticket flow, identifies bottlenecks, reorganizes queues, and prioritizes escalations—while adjusting tactics as volume shifts or issues evolve.
4. 🔄 Learning and Adaptation
Through continuous exposure to new data and feedback, Agentic AI can learn from experience, refine its models, and improve performance over time.
These agents develop memory and evolve behavior. They analyze outcomes, learn what works (and what doesn’t), and apply those learnings the next time—without requiring reprogramming.
Example in Retail Planning:
An inventory optimization agent tracks real-time sales, seasonal fluctuations, and local trends. Over time, it learns that certain items sell better during specific events or weather patterns—and adjusts stocking and pricing strategies accordingly.
Conclusion
Agentic AI isn’t just the next step in automation — it’s a shift in how work gets done.
These systems don’t wait for instructions. They make decisions, take action, and improve with every cycle. That means less micromanagement, faster execution, and smarter results.
If your business runs on speed, scale, or complexity — that’s the future of staying competitive.
Want to use AI in your business?
If you’re curious how Agentic AI could work in your business — whether that’s improving support, spotting risks early, or making operations more efficient — we’d be glad to walk you through it.
The quality and skills of your engineers are very impressive. You have helped us accelerate the delivery of our fin tech prototype that has turned heads at Zions Bank
Joel Schwartz
CEO
DoubleCheck Solutions
Glad that Adanto could get us started with IAM Automation Tool and Security metrics for our CISO and my Information Security Services Organization.
Jason Zirkelbach
Sr. Director - Enterprise Information Security
Robert Half
Thank you for your help in migration to the Dell infrastructure.
Richard Leurig
SVP, GM Innovation & Technology
Core Logic
Adanto speed in responding to concerns is great. You are really great to work with.” CG ProPrints Team
AJ McDonald
Director, Brand Marketing, Art Division
Circle Graphics/Sensaria
Adanto has performed the project on time and to our complete satisfaction. We have achieved our goal of improved visibility in our global call centers and could fix issues much quicker for our internal clients
Eddie Borrero
Chief Information Security Officer
Robert Half
Adanto Powers Big Data Democratization for Robert Half
Adanto delivers a cloud-based Big Data Lake solution for a Silicon Valley consulting leader, eliminating data silos, reducing costs, and enabling seamless access to raw data. The solution fosters a data-driven culture and empowers users with scalable, flexible analytics.
Chetan Ghai, our Chief Product Officer, and I know how fast Adanto has created an application for Robert Half that intergates our patented Quill technology and so we are very convinced how strong your team is.
Mauro Mujica-Parodi III
VP, Product
Narrative Science
Thank you for the design plan of Fujifilm eCommerce Plug-ins integration of our Simple Ordering Platform (SOP) with Shopify and MediaClip that adds a product builder functionality in the Shopping Cart, then submit it via SPA API.
Alvin Scott
Senior Software Product Manager
Fujifilm NA Corporation, Imaging Division
Customer service is evolving at lightning speed. As customers demand faster, smarter, and more personalized support, traditional call centers and help desks struggle to keep up. Enter Agentic AI — autonomous, proactive, and context-aware systems that don’t just respond to requests but think, decide, and take action on your behalf.
If you’re wondering how to bring Agentic AI into your customer service, here are three game-changing ideas — each tackling a real challenge and driving measurable results.
Table of Contents
Autonomous Handling of Routine Support Tickets
The Challenge:
Your support team is swamped with repetitive, low-complexity queries — order tracking, password resets, return policies. These take time, frustrate customers with wait times, and drain resources that could be better spent on complex issues.
How Agentic AI Helps:
Agentic AI can autonomously manage these routine requests without human intervention. It understands the context, provides instant, accurate answers, and escalates only when needed — all while remembering customer history to keep conversations smooth.
The Outcome:
- Slash ticket volume by up to 60%
- Cut average response times drastically
- Free up your agents to focus on what truly matters
- Boost customer satisfaction with faster, smarter support
Proactive Customer Engagement to Reduce Churn
The Challenge:
Many companies wait too long to engage customers about renewals, upgrades, or potential issues. The result? Missed opportunities, avoidable churn, and lost revenue.
How Agentic AI Helps:
Agentic AI monitors customer behavior and subscription timelines, predicting when customers need outreach. It sends personalized messages or offers before a customer even realizes they might leave or have a problem.
The Outcome:
- Cut churn rates by 20–30% or more
- Increase upsell and cross-sell conversions
- Build stronger, proactive relationships with your customers
- Lighten the load on your human agents
Real-Time AI Assistance to Empower Agents
The Challenge:
Customer service agents often face complex or emotionally charged interactions without real-time guidance. This can lead to slower resolutions, inconsistent experiences, and agent burnout.
How Agentic AI Helps:
Imagine an AI sitting beside your agent during calls or chats — suggesting the best responses, surfacing relevant knowledge, and even detecting customer emotions to adapt the approach on the fly.
The Outcome:
- Speed up call resolution times by 15% or more
- Improve customer sentiment and satisfaction
- Reduce agent stress and turnover
- Ensure consistent, on-brand communication every time
Ready to Bring Agentic AI Into Your Customer Service?
The future of customer service is proactive, personalized, and powered by intelligent AI that augments your team — not replaces it. Whether you start with automating routine tickets, proactive outreach, or real-time agent support, Agentic AI can transform your customer experience while boosting efficiency.
Reach out to the experts at Adanto Software.
Imagine a mid-sized bank overwhelmed with millions of transactions, thousands of loan applications, and tens of thousands of customer queries every day — all demanding lightning-fast decisions and razor-sharp accuracy. The stakes couldn’t be higher: a single missed fraud alert or a delayed credit decision could cost millions or destroy client trust overnight.
This is the reality fintech faces, and it’s why Agentic AI has emerged as a game-changer. Today, financial institutions wield AI not only to automate routine tasks but to drive complex decisions, personalize client experiences at scale, and navigate turbulent markets in real time.
Table of Contents
Why Agentic AI Matters in Financial Services — By the Numbers
According to recent studies, AI adoption in financial services has grown by over 70% in the last three years, with the global AI in fintech market projected to reach $26 billion by 2027, growing at a CAGR of 23%. Firms investing in advanced AI systems report a 30-40% improvement in operational efficiency and significantly faster turnaround times on credit decisions, compliance checks, and customer service.
These statistics represent measurable impact across the financial ecosystem, from retail banking to asset management:
- 70% of financial firms have integrated AI into at least one business function, primarily fraud detection and customer service (Source: Deloitte).
- AI-driven credit assessments reduce loan default rates by up to 25% through better risk profiling (Source: McKinsey).
- Automated transaction monitoring powered by AI can detect suspicious activity with 95% accuracy, cutting false positives by 50% (Source: Accenture).
- Firms using AI for customer engagement report up to 20% increase in client retention thanks to personalized financial coaching and real-time advice (Source: PwC).
The financial industry saves an estimated $447 billion annually through AI-enabled automation in compliance and operational tasks (Source: Business Insider Intelligence).
Use Cases of Agentic AI in Financial Services
Enhancing Customer Interaction
In a sector where customer trust and engagement are paramount, Agentic AI transforms how firms interact with their clients.
- Client Engagement: Agentic AI automates personalized financial planning tools, creating tailored app interfaces that adjust dynamically to individual client profiles and needs.
- Relationship Management: By optimizing communication channels and tailoring engagement strategies, financial institutions can maintain stronger client relationships and increase satisfaction.
- Personal Financial Advisory: Real-time coaching and financial advice based on clients’ spending patterns enable more proactive and relevant financial guidance.
This not only improves client experiences but also drives loyalty and retention.
Innovating Product and Pricing Strategies
Agentic AI enables smarter product offerings and dynamic pricing models, giving firms an edge in competitive markets.
- Credit Assessment & Loan Origination: AI evaluates creditworthiness with higher precision, customizes loan offers, and autonomously manages portfolios with high-risk profiles, reducing defaults and improving underwriting efficiency.
- Dynamic Pricing: Pricing models adjust in real time according to client behavior, market conditions, and risk factors, optimizing retention offers and revenue.
Such adaptability results in more competitive, customer-centric products.
Strengthening Compliance and Fraud Prevention
Regulatory compliance and fraud detection are among the most complex and critical challenges for financial firms. Agentic AI excels in these areas by continuously monitoring and reacting to emerging threats.
- Transaction Monitoring: Detects anti-money laundering (AML) risks by flagging suspicious transactions and dynamically intervening before damage occurs.
- Claims and Underwriting: Automates triaging processes and refines risk models to improve accuracy and speed.
- Financial Risk Surveillance: Tracks market threats in real time and recommends mitigation strategies.
- Software Compliance Testing: Identifies bugs, ensures regulatory compliance, and deploys seamless updates.
- Process Automation and Quality: Triages complaints efficiently and detects operational anomalies to maintain service excellence.
By automating these critical processes, Agentic AI helps reduce operational costs and mitigate legal and reputational risks.
Driving Market Intelligence and Competitive Advantage
Agentic AI empowers financial institutions with deeper market insights and strategic agility.
- Competitive Market Analysis: Continuously tracks competitor strategies and uncovers actionable insights for tactical decision-making.
- Market Trend Surveillance: Monitors shifts in the financial landscape, alerting analysts to emerging risks or opportunities early.
This intelligence supports proactive strategy development and faster response to market changes.
Conclusion: Why Financial Firms Can’t Afford to Ignore Agentic AI
Financial services face increasing pressure from tighter regulations, rising customer expectations, and intense competition. Agentic AI is proving to be a vital tool that drives faster decisions, improves risk management, and personalizes customer experiences at scale.
The data is clear: firms using Agentic AI gain real advantages in efficiency and retention. Those that hesitate risk falling behind more agile competitors.
Want to use AI in your business?
Get in touch with the Adanto Software Team today to see how we can help.
The Adanto team was one of the first groups of developers I started working with at Sensaria and really one of the constants during my time here. Thank you for everything over the years – most notably your flexibility and teamwork with the on-shore team, and teaching us some key Polish terms along the way. I’m happy to say that the concept of “Little Friday” has spread around Sensaria!
Polly Eron (Tobias)
Technical Project Manager
Circle Graphics/Sensaria
Adanto Team has exceeded my expectations, delivered solid results and offered very valued support in place of several previous suppliers who have only left confusion and lots of bugs
Jeanne McDonald
CFO
Tangible Investments
Adanto has done a nearly impossible task of replacing the previous bankig-as-a-service provider with a new one SYNAPSE in just five weeks; for a platform they had never seen before. Adanto pulled it off
Mark Vanderbeek
CTO
Rego Payment Architectures, Inc.
Key Results
$2.6M
Annual Cost Savings from IT infrastructure optimization and automation & efficiency
$1M+
Annual productivity gains from time & effort savings and faster decision-making
$500k+
Business Enablement gains from improved SLA compliance & new insights, innovation & agility
Services performed
- Data Science
- Data Analytics & Business Intelligence
- Data Warehousing
- Big Data
- Machine Learning
- Artificial Intelligence
- DevOps
- Security
- Infrastructure Services
- Salesforce
- Amazon Cloud
- Azure Cloud
Technologies used
Data Sources/Silos
- 60+ data sources
- 200+ GB of new data per day
- One Data Store (Data in different AWS data stores based on data type)
- Amazon S3
- Amazon EC2
- Amazon Redshift (data warehouse for standard SQL queries & BI tools)
- Amazon RDS (relational database for many instance types)
- Apache Sqoop (O/S tool for bulk data transfers)
- Amazon HDFS (Parquet) (Hadoop Cluster with EMR – Elastic MapReduce)
Query Tools & Analytics
- Apache Hive, Pig, Spark (O/S database query interface tools to HDFS & processing engine)
- R (O/S statistical programming language for data mining and statistical computing)
- Mahout/scikit-learn (O/S tools for building Machine Learning apps)
- QlikView, PowerBI, SAS (data analytics, business intelligence and reporting tools)
Challenge
Robert Half was challenged with lack of easy access to company’s enterprise data. The company faced multiple challenges for which it was seeking a solution:
- Limited agility and accessibility for data analysis.
- Data silos preventing effective information sharing.
- High costs due to server and license proliferation and IT complexity (shadow IT)
- Expensive scalability and lack of flexibility for new systems.
Key goals
Create a centralized repository for raw data accessible across departments
Implement incremental load processes and data governance procedures
Develop thematic, departmental, and business line-focused data marts
Build analytic applications tailored to specific business needs
Solution
Big Data Lakes are enterprise-wide data management platforms that store disparate data sources in their native format until queried for analysis. Unlike purpose-built data stores, data lakes consolidate raw data in its original form, eliminating information silos and enabling better data sharing. This approach reduces server and licensing costs, provides scalable and flexible storage, and ensures data accessibility for both programmers and business users
Adanto implemented a scalable and cost-effective cloud-based data lake infrastructure:
- Stored data in Amazon S3 Buckets for cost efficiency.
- Utilized parquet file format with HDFS/Hive for structured querying.
- Established a Hadoop/Spark cluster in AWS with autoscaling capabilities.
- Set up incremental data load processes using Apache Sqoop on an EMR cluster for daily data ingestion.
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Thank you for your expertise and fast, quality delivery
Mike Perry
VP, Software Development
Ria Financial
Imagine walking into a supermarket where every aisle seems perfectly stocked with products you want. No empty shelves, no clutter of unpopular items. Behind this seamless experience is a complex decision-making process about what products to offer and how much space they deserve. For retailers, these decisions are tough and often based on guesswork or outdated information. But AI agents are changing that. By analyzing large amounts of data and continuously learning, these tools optimize product mix and shelf space in ways that were impossible before. This article explores how AI helps retailers make smarter choices, reduce waste, and meet customer demand more effectively.
Table of Contents
The Challenge of Merchandising
Retailers typically carry thousands of products. Each SKU competes for shelf space, which is a limited and costly resource. According to a Nielsen study, retailers lose nearly 10-15% of sales due to out-of-stock items or poor shelf placement. Meanwhile, excess inventory ties up capital and increases waste, especially for perishable goods. Traditionally, store managers use sales history and manual adjustments to plan product displays. But consumer preferences shift quickly, and competitors constantly change their offerings. This often results in overstocking slow movers or missing out on fast sellers, impacting both revenue and customer satisfaction.
How AI Agents Support Product Mix Decisions
AI agents dig deeper than basic sales reports. They analyze customer buying patterns, seasonal trends, promotional impacts, and even social media buzz. For example, an AI agent might detect an emerging trend for plant-based snacks before traditional methods catch on. These agents test various product combinations virtually, learning which mix drives the highest sales and margin. Retailers who use AI for product assortment have reported up to a 20% increase in sales and a 15% reduction in inventory costs. AI’s continuous learning means it adapts when new products arrive or when consumer habits shift, helping stores stay aligned with current demand.
Shelf Space Optimization Explained
Shelf space is a valuable asset that directly influences sales. Research by the POPAI Group found that 72% of purchase decisions happen in-store, making shelf placement crucial. AI agents recommend space allocation by weighing factors like product size, profitability, turnover speed, and customer preference.
For example, a fast-selling premium coffee brand may get more shelf space than a slow-moving generic. The AI can also adjust layouts quickly during promotional campaigns or new product launches. This flexibility reduces lost sales from poor product placement and improves overall store profitability.
Benefits for Retailers and Consumers
For retailers, the benefits are clear. AI reduces manual effort and guesswork, lowers inventory holding costs, and improves sales efficiency. Staff can focus on customer engagement and store experience rather than spreadsheet crunching. For consumers, this means shopping in stores that are better stocked and easier to navigate. They find the products they want more consistently, reducing frustration and improving satisfaction. Ultimately, smart merchandising creates a smoother shopping experience and a healthier bottom line.
Conclusion
Managing product mix and shelf space is difficult but essential for retail success. AI agents offer a smarter way to handle these challenges. They use data to make faster, more accurate decisions that keep stores stocked with the right products. This leads to better sales, lower costs, and happier customers. As retail becomes more competitive, adopting AI-based merchandising tools will help businesses stay ahead.
Want to use AI in your business?
Get in touch with the Adanto Software Team today to see how we can help.
Key Results
$1.5M
Annual savings from server consolidation & cloud migration
$400k
Annual costs savings from email workflow automation
$300k
Annual savings from AWS autoscaling that reduces unnecessary cloud resource use
Technologies used
- Java as core backend development for scalable workflows, API integration, and business logic.
- Jenkins for automated CI/CD pipelines for seamless builds, testing, and deployments.
- Kibana for visualized system logs and metrics for real-time monitoring and optimization.
- Drupal to Build a multilingual, user-friendly web interface supporting 19 languages.
- Amazon Web Services (AWS)
- SQS: Handled scalable subscription workflows.
- SNS: Delivered job alerts and notifications.
- S3 Bucket: Cost-effective storage for static assets.
- RDS: Managed relational databases for user data.
- KMS: Secured sensitive data with encryption.
- Docker for containerized deployments ensured consistency across environments.
- Oracle Eloqua Marketing Cloud Service
- Salesforce as centralized lead and subscription management
Challenge
This project was designed to enhance the job-seeking and recruitment process by providing users with the most accurate and up-to-date information about available job openings. The solution was to be tailored to align job postings with the user’s preferences and qualifications, as provided during their subscription process. A critical component of the project was the usage of multilingual email templates, enabling effective and personalized communication across a wide range of regions.
Key goals
Attracting New Talent: Recruiting new candidates in a competitive job market requires highly targeted outreach and personalized communication to build trust
Precision Matching: The platform efficiently connects users with relevant job openings, maximizing opportunities for both employers and job seekers
Scalability: Delivering consistent, high-quality job alerts in 16 countries and 19 languages while addressing translation and cultural nuances
Data Accuracy: Ensuring reliable integration and synchronization of user data with job openings for precise alerts
Solution
This solution exemplifies how advanced technology, automation, and localization can work together to deliver a seamless and scalable user experience while meeting the strategic goals of a global organization.
- Enhanced User Engagement: The multilingual platform and email templates ensure users receive relevant and personalized updates, improving overall satisfaction and engagement.
- Efficient Automation: The AWS-based workflow eliminates manual intervention in subscription management, reducing errors and increasing operational efficiency.
- Scalability and Flexibility: The cloud-based architecture supports high volumes of data and user interactions, allowing the solution to scale seamlessly with business growth.
- Data-Driven Insights: Oracle Eloqua’s analytics capabilities provide valuable insights into campaign performance, enabling continuous improvement in user communication strategies.
- Global Reach: Multilingual support ensures the solution meets the needs of a diverse audience across multiple regions and languages.
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Agentic AI is no longer theoretical. It’s here, and it’s already reshaping how organizations approach automation, decision-making, and operational efficiency.
Unlike traditional AI models, Agentic AI systems act with autonomy. They don’t just provide predictions or outputs—they perceive their environment, plan actions, make decisions, and interact with other systems to achieve a goal. This makes them especially valuable in complex business environments where dynamic adaptation is required.
But the question most enterprise leaders ask is: “Where do we even begin?”
This guide lays out a clear, practical framework for adopting Agentic AI at the enterprise level.
Table of Contents
What is Agentic AI?
Agentic AI refers to intelligent software systems—known as AI agents—that operate with a degree of autonomy. These agents can:
- Observe an environment or data stream
- Make sense of the information (reasoning)
- Decide on a course of action
- Execute that action
- Learn from the outcome to improve over time
These agents may operate alone or as part of a multi-agent system, where multiple agents collaborate or specialize in sub-tasks.
Agentic AI differs from traditional ML models because it doesn’t just offer insight—it acts. And in enterprise environments, action is often where the value lies.
Why It Matters for Enterprise Systems
Most enterprises already use AI in some form—predictive models, recommendation engines, or RPA (robotic process automation). But these solutions are:
- Static: Models predict, but don’t act
- Siloed: They live inside one system, not across workflows
- Rule-bound: Automation follows fixed logic, which breaks under change
Agentic AI overcomes this by creating systems that think and act, not just compute. This enables:
- Faster decision-making without human bottlenecks
- Workflow orchestration across platforms
- Adaptation to changing business conditions
- Reduction in manual exception handling
Use Cases with High ROI Potential
Agentic AI is best applied where there’s a need for judgment, coordination, or autonomy. Here are proven high-ROI use cases:
Fraud Detection and Response
Autonomous agents can monitor transactions, detect anomalies, take preventive actions (e.g., block payments), and notify users—within milliseconds.
Customer Service Automation
Multi-agent systems can manage tickets, escalate intelligently, handle sentiment-based routing, and even generate personalized follow-ups.
IT and Cloud Ops
Agents can auto-resolve known issues, allocate resources, monitor performance, and coordinate across infrastructure tools like Datadog, AWS, or Azure.
Financial Reconciliation and Audit
Agents validate transactions, cross-check records, and flag inconsistencies across multiple financial systems.
Procurement & Vendor Onboarding
AI agents handle document collection, background checks, compliance scoring, and auto-approval routing.
Readiness Checklist: Before You Start
Ask these questions first:
- Do you have high-quality, accessible data across your systems?
- Are key business workflows well-defined and documented?
- Do you already use automation or AI tools (e.g., ML models, RPA)?
- Is your IT architecture API-friendly and modular?
- Do you have stakeholder alignment on AI governance and ethics?
If the answer is “no” to several of these, it’s wise to start with AI strategy consulting or data infrastructure improvements before diving into agent development.
Step-by-Step Adoption Roadmap
Step 1: Identify a target use case
Start with a narrow, high-impact task. Example: automated KYC document verification or refund fraud detection. Ensure it has clear KPIs.
Step 2: Build a proof of concept (PoC)
Use open-source frameworks (e.g., LangChain, AutoGen) or a managed platform. Integrate with internal systems via APIs or test data.
Step 3: Define agent architecture
Decide on:
- Observation method (event stream, polling, direct input)
- Reasoning approach (rules, ML, hybrid)
- Action interface (REST APIs, system hooks, human handoffs)
- Memory (short-term vs. long-term context)
Step 4: Pilot in a limited environment
Run the agent in a sandbox. Track metrics like response time, error rate, decision quality, and human override frequency.
Step 5: Expand and integrate
Move the agent into production environments with safeguards. Connect to other agents or business systems. Monitor continuously.
Technical Architecture Overview
A typical agentic AI system includes:
- Environment: The system or data space the agent interacts with
- Perception Layer: Ingests data (via logs, APIs, streams)
- Reasoning Engine: Logic, ML models, or fine-tuned LLMs
- Action Layer: Interfaces for executing tasks
- Memory Store: For context persistence and learning
- Governance Layer: Human-in-the-loop, audit logs, fallback policies
Agents can be cloud-native, containerized, and deployed alongside existing microservices.
Final Thoughts
Agentic AI is not a trend—it’s a capability shift. It allows systems to adapt, act, and scale decisions faster than humans ever could. For enterprises, it’s not about replacing people—it’s about augmenting them with agents that can handle complexity, speed, and scale.
Early adopters will gain a real advantage—not just by reducing costs, but by building more adaptive, intelligent, and responsive operations.
Adanto helps enterprise teams design, deploy, and manage autonomous AI agents. From pilot projects to full-scale production systems, we partner with clients across fintech, retail, and cloud operations to deliver results.
In finance, compliance is not optional. It’s a requirement—and a costly one. Banks, insurers, and investment firms spend billions each year to keep up with changing regulations. And the pressure is only growing.
But there’s a shift happening. Agentic AI is quietly changing how compliance work gets done. It’s not about replacing compliance officers. It’s about giving them smarter tools to handle the growing complexity, faster and with fewer errors.
This article explores how Agentic AI is helping financial institutions automate compliance. We’ll look at what’s driving the need, how the technology works in real situations, and what to consider before moving forward.
Table of Contents
The Rising Cost of Compliance
Regulatory pressure has increased steadily since the 2008 financial crisis. From anti-money laundering (AML) to data privacy laws, institutions face a maze of rules—many of which change frequently and differ across regions.
In 2023, major banks in the U.S. spent an average of 10–15% of their operating budget on compliance. It’s not just about cost. It’s about the time lost to manual reviews, document collection, and reporting. Mistakes are expensive too. Fines for non-compliance have totaled over $400 billion globally since 2009.
The current model isn’t sustainable. The pace of regulatory change is faster than human teams can reasonably handle. That’s why automation is no longer a “nice to have.” It’s necessary.
Why Traditional Methods Fall Short
Compliance teams often rely on rule-based systems. These are good for tasks with clear, static logic. But regulations aren’t static. They change. They differ between jurisdictions. They sometimes contradict each other.
Manual processes add another layer of risk. Humans miss things—especially when reviewing hundreds of pages of legal text or thousands of transaction records. And as data volumes grow, human teams can’t scale fast enough to keep up.
Spreadsheets and legacy systems can’t carry this weight anymore. They’re too rigid. They’re slow to adapt. They don’t learn or improve over time.
How Agentic AI Fits In
Agentic AI changes the approach. Instead of following fixed rules, it can interpret patterns, reason through steps, and act independently within set boundaries. It’s goal-oriented. It doesn’t just process data—it understands tasks in context and decides how to complete them.
In compliance, that means:
- Reading regulations and extracting relevant obligations
- Monitoring transactions in real time to flag suspicious activity
- Checking policies against regulatory frameworks
- Creating audit trails without manual input
- Filling out reports with accuracy and traceability
Agentic AI doesn’t operate in isolation. It works with people. A compliance officer may set the goal—like checking transactions for sanctions risk—and the AI figures out the best steps to take, asks for feedback, and updates its process over time.
This makes it more flexible than past automation tools, but also more accountable than traditional AI models that function like black boxes.
Real-World Applications
AML Transaction Monitoring
A global bank used Agentic AI to overhaul its AML process. Instead of flagging transactions based on fixed thresholds, the AI evaluated behavior over time, cross-checked it with external data sources, and adapted its logic as new risk patterns emerged. False positives dropped by 40%. Investigators could focus on real threats, not noise.
Regulatory Change Management
A European investment firm used Agentic AI to track regulatory updates across 15 jurisdictions. The system scanned official documents, matched them to internal policies, and alerted the legal team when adjustments were needed. What used to take weeks now happens in hours.
KYC Automation
A fintech startup applied Agentic AI to its KYC onboarding flow. Instead of fixed forms, the AI led dynamic interviews with customers, asked only the necessary questions, and validated documents in real time. This reduced drop-off rates and cut verification time by 70%.
Challenges and Risks
This isn’t plug-and-play technology. There are serious risks to consider:
- Oversight: Agentic systems need constant human supervision. They’re smart, but not infallible.
- Auditability: Regulators require clear, traceable records. The AI must show why it made a decision.
- Bias and fairness: If the training data is flawed, the system can make biased or inconsistent calls.
- Data privacy: Handling sensitive financial and customer data demands strict safeguards.
Trust is built through transparency, testing, and clear limits on what the system is allowed to do.
What to Do Next
If you’re exploring Agentic AI for compliance, start small. Identify a specific pain point—like transaction monitoring or document review—and build a pilot with clear goals.
Make sure your compliance, legal, and tech teams work together from the start. This isn’t just a software project. It’s a shift in how work gets done.
Finally, choose a vendor or partner with deep experience in both AI and finance. The right expertise makes a big difference in managing risk and getting results.
Conclusion
Compliance isn’t getting easier. The volume of regulations, the speed of change, and the cost of mistakes are all going up. Traditional tools can’t keep up.
Agentic AI offers a smarter way forward. Not by replacing people, but by helping them work faster, smarter, and with more confidence. It can read, reason, act, and improve. That’s a big shift for finance—and one that’s already underway.
Want to use AI in your business?
Get in touch with the Adanto Software Team today to see how we can help.
Customer service is evolving faster than ever — and Agentic AI is leading the charge.
If you’ve heard buzz about AI handling customer interactions, here’s the truth: within the next 12 months, more than half of all customer service conversations will be managed by agentic AI systems. These aren’t your typical chatbots; they’re autonomous, proactive, and deeply contextual digital agents that understand your needs, make decisions on the fly, and act — all to deliver a seamless, personalized experience.
Table of Contents
What Is Agentic AI — And Why Should You Care?
Agentic AI takes AI-powered customer service to the next level. Unlike rule-based bots that simply respond to scripted prompts, agentic AI:
- Understands context — remembers past interactions and adapts conversations.
- Acts proactively — reaches out before problems arise or needs are voiced.
- Makes decisions autonomously — guiding customers and supporting agents alike.
Simply put, it’s customer service that thinks and acts smarter — like having a supercharged, empathetic team member available 24/7.
The AI Shift Is Happening — Fast
According to Cisco’s latest research:
- 56% of all customer interactions will be AI-handled within a year.
- 75% of business leaders believe proactive AI support will reduce customer churn.
- 65% expect to boost customer lifetime value through AI-driven insights.
That’s not just technology hype. It’s a strategic transformation reshaping how companies connect with customers — driving loyalty, satisfaction, and revenue.
How Agentic AI Changes Customer Service
Traditional customer support often struggles with:
- Long wait times
- Repetitive questions
- Burned-out agents
- Reactive responses
- Fragmented experiences
Agentic AI flips this script. This means happier customers and empowered agents — a win-win.
Top Agentic AI Use Cases Transforming Customer Service
Here’s where agentic AI really shines:
- Autonomous Customer Support
Instantly handles routine queries, reducing wait times and deflecting up to 60% of tickets. - Contextual Memory
Keeps track of past conversations so agents respond faster and smarter. - Proactive Outreach
Predicts customer needs — like renewals or potential issues — and acts before you even ask. - Real-Time Assistance
Provides live recommendations on next steps, resources, and tone during customer calls or chats.
Sentiment Detection
Reads emotions to support both customers and agent well-being, tailoring responses with empathy.
Agentic AI Augments Humans — It Doesn’t Replace Them
A common misconception is that AI will replace people. The reality is the opposite. Agentic AI acts as a smart copilot — augmenting human agents to:
- Make better decisions
- Work more efficiently
- Deliver richer, personalized experiences
This collaboration means more productive teams and happier customers.
Conclusion
Agentic AI is revolutionizing the way businesses interact with their customers, making service faster, smarter, and more human-centric. By understanding context, acting proactively, and collaborating with human agents, these intelligent systems turn every customer interaction into an opportunity to build loyalty and drive growth.
Companies that embrace agentic AI now will gain a serious competitive edge — delivering seamless experiences, reducing operational costs, and empowering their teams to focus on what truly matters: creating lasting relationships with customers.
Want to use AI in your business?
Get in touch with the Adanto Software Team today to see how we can help.
Thank you Adanto Team for the foundational architecture decisions and deliveries to get us going with Web Alerts!
Jim Falls
Sr. Director, IT - Marketing/Corporate Communication Services
Robert Half
Adanto has helped us in our first phase of creating DataLake and gathering data in centralised location
Sean Perry
CIO
Robert Half
Adanto was pivotal in getting our iTrack Reporting Workstream Project on track and successful. Sheila Santana, VP, IT
Sheila Santana
VP, IT Field Services
Robert Half
We all greatly appreciate the time you’ve spent with Adanto and for your various softwareservices
Jason Fiber
SVP/GM Mobile Group
THX
Key Results
95%
Accuracy achieved in data reporting
$1M+
Annual IT cost savings achieved from reduction of need for multiple systems
25%+
Accuracy improvement in budget &forecasting cycles
Services performed
- Data Analytics & Business Intelligence
- Software Development & Maintenance
Technologies used
- Deltek Maconomy
- SAP BusinessObjects Web
- PL-SQL, SQL
- Oracle, SQL Server
- AWS EC2
- AWS RDS
- Java
Project size
- 5 SAP BusinesObject Universes
- 200+ financial and management reports
- 20+ key performance indicators
Challenge
Robert Half IT faced significant challenges in gaining clear insights into spending, departmental performance, and Service Level Agreement (SLA) compliance. These issues led to inaccurate forecasting, budget overruns, and underspending. To address these challenges, the company engaged Adanto to help implement a new (ERP) system, Deltek Maconomy, and develop an advanced enterprise data analytics, visualization, and reporting platform utilizing SAP BusinessObjects Universes.
Key goals
Enhance Decision-Making with Reliable Data: Ensure access to accurate, consistent, and comprehensive data across departments to enable informed decision-making at all organizational levels.
Streamline Data Access and Reporting: Implement a centralized platform to provide quick and intuitive access to critical data, reducing the time and effort required for reporting and analysis.
Improve Data Visualization and Insights: Utilize advanced graphical representations and analytical tools to identify hidden patterns and interdependencies in data for better strategic planning.
Boost Operational Efficiency: Leverage accurate data and automated reporting processes to optimize workflows and minimize errors caused by outdated or inconsistent information.
Solution
The BI SAP BusinessObjects Solution for Maconomy ERP involved integrating SAP BusinessObjects as the Business Intelligence (BI) and reporting layer for the financial and operational data managed within Maconomy ERP for the client. This solution provides advanced analytics, reporting, and visualization capabilities, enabling Robert Half to gain deeper insights into their Maconomy data and optimize decision-making.
- Customizable Reports and Dashboards
- Data Integration (ERP & SAP with ETL)
- Ad-Hoc Reporting
- Enterprise Reporting
- Advanced Analytics
- Governance and Security
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We are tremendously pleased with Adanto’s quality and speed of delivery for AWS Security engineering services to our clients in the Financial Services sector. Your expertise was sorely needed by our organization. I only wish we’d engaged you earlier!
Chris Miller
Manager of Information Security Operations
ECMC
Adanto Boosts Efficiency, Security at US Walmart stores
Adanto reduces cost and operational downtime by improving efficiency and security at 4,000 Walmart’s Photo Centers operating by Fujifilm’s mobile image printing kiosk services.
The team presented a solid strategy with beacons for our mobile application”\
Glen Wilson
Director of Engineering
American Express, Credit Cards
I have had the pleasure of collaborating with Mike on several projects as a client during my career. Adanto stands out as a premier IT professional software services and solutions company. I can attest to their dedication to delivering innovative and robust solutions tailored to the unique needs of their clients. The software development teams at Adanto are not only technically proficient but also deeply committed to achieving the best outcomes for their clients. They are incredibly fun and engaging to work with. Their proactiveness and adaptability helps make Adanto a trusted business advisor and innovation partner.
Hans Bakker
Director of Web Development
Circle Graphics/Sensaria
Inventory management is a core challenge for businesses across industries. Holding too much stock ties up capital and storage space. Too little stock risks lost sales and unhappy customers. Striking the right balance requires insight into demand patterns, supply chains, and operational constraints.
Traditional methods often rely on static rules or human judgment, which can struggle to keep up with fast-changing markets. That’s where self-learning AI agents come into play. These systems continuously observe data, adjust strategies, and make decisions that improve inventory management over time — without needing constant manual tuning.
This article explores how self-learning AI agents enhance inventory optimization, reduce costs, and improve service levels. We’ll walk through their key benefits, how they operate, and what businesses should consider when adopting them.
Table of Contents
The Inventory Challenge
Inventory optimization is about balancing supply and demand. Companies must forecast future demand, manage lead times, and respond to disruptions like supplier delays or sudden spikes in sales. Inaccurate forecasts or rigid inventory policies lead to overstock, stockouts, or obsolete inventory.
According to a survey by Gartner, companies typically spend 20-30% of their operating costs on inventory. Inefficiencies can easily add millions of dollars in unnecessary expenses. At the same time, 43% of retailers report frequent stockouts, which damage customer loyalty and sales.
The complexity and volume of data make manual approaches less effective. Businesses need tools that can quickly learn from new information and adjust inventory decisions accordingly.
How Self-Learning AI Agents Work
Self-learning AI agents monitor a wide range of data points: historical sales, current inventory levels, supplier performance, market trends, and even external factors like weather or promotions. Using algorithms inspired by reinforcement learning, these agents test different inventory policies and learn which actions yield better outcomes.
Over time, the agents improve their predictions and decision-making by constantly evaluating the results of their choices. This means they adapt to changes in demand patterns or supply chain disruptions without requiring manual reprogramming.
Unlike traditional systems that rely on fixed rules, self-learning agents evolve. They can optimize reorder points, quantities, and timing, balancing costs with service levels dynamically.
Key Benefits of AI-Driven Inventory Optimization
- Reduced Holding Costs: By minimizing excess stock, businesses free up capital and reduce storage expenses.
- Lower Stockouts: Adaptive inventory policies help maintain service levels, reducing lost sales and backorders.
- Improved Forecast Accuracy: Continuous learning refines demand predictions, even with volatile market conditions.
- Faster Response to Disruptions: AI agents detect and react to supply chain changes more quickly than manual processes.
Scalability: AI systems handle large, complex product portfolios without additional human effort.
Considerations for Implementation
Introducing self-learning AI agents requires data readiness and clear objectives. Companies should:
- Ensure quality, consistent data from sales, inventory, and suppliers.
- Define KPIs like inventory turnover, fill rate, or cost targets.
- Plan integration with existing ERP or supply chain systems.
- Start with pilot projects before scaling.
- Maintain human oversight to validate AI recommendations, especially early on.
Adopting these agents is not a plug-and-play solution. It takes time, expertise, and alignment across teams.
Conclusion
Inventory optimization remains a critical business challenge. Self-learning AI agents offer a practical approach to managing inventory more efficiently by adapting to changing conditions without manual intervention. They help reduce costs, improve service levels, and handle complex data at scale.
Companies willing to invest in data quality and thoughtful implementation can benefit from more agile and accurate inventory management. As markets grow more dynamic, these AI-driven systems become increasingly valuable tools.
Want to use AI in your business?
If your business is looking to improve inventory management, consider exploring AI-driven solutions tailored to your operations. Reach out to Adanto Software for insights on integrating self-learning AI agents into your supply chain.
Global sales force App powered by the data science algorithm
Adanto improves the productivity of sales and marketing teams at the global leader in the Professional Staffing global leader by delivering targeted realtime leads via AI/NLP powered enterprise application to their mobile or PC device of choice.
Peter and the Adanto Software team has come through for us and at the lightening speed
Joao Bettencourt
IT Software Project Manager
Robert Half
Retail forecasting has always been a tough challenge. Businesses want to predict demand, manage inventory, and optimize pricing to stay competitive. Traditionally, companies have relied on Business Intelligence (BI) tools — dashboards, historical data analysis, and static reports — to guide decisions. But now, AI agents are becoming more common in retail. They can process data continuously, adapt to changes, and even automate decisions.
Which approach works better for retail forecasting? This article compares AI agents with traditional BI to see which one delivers more value, especially in a fast-moving retail environment.
Table of Contents
The Basics: Traditional BI in Retail Forecasting
Traditional BI focuses on gathering historical data and presenting it through reports or dashboards. Retailers use this data to identify patterns, track sales trends, and plan stock levels. The advantage is clear visibility into what has happened and some indication of what might come next based on past trends.
However, this approach has limits. Traditional BI often depends on manual data updates and fixed reports that may become outdated quickly. It doesn’t always react well to sudden market shifts like supply chain disruptions, seasonal changes, or emerging consumer trends.
What AI Agents Bring to the Table
AI agents take forecasting a step further. Instead of waiting for a human to interpret reports, these systems analyze data continuously, learn from new information, and adjust predictions in real time. They can integrate data from multiple sources—sales, weather, social media, even competitor pricing—to refine their forecasts.
For example, an AI agent might detect a sudden rise in demand for a product due to a viral trend and automatically suggest changes in inventory orders. This dynamic response is hard to achieve with traditional BI alone.
Strengths and Weaknesses of Both Approaches
Traditional BI tools are straightforward and familiar to many businesses. They provide useful snapshots but often struggle to keep pace with rapid changes.
AI agents offer flexibility and speed. But they depend heavily on accurate, diverse data and may require investment in technology and skills.
Real-World Impact: Examples and Data
Research from McKinsey shows that companies using advanced analytics for forecasting see inventory reductions of up to 20% and service level improvements by 10%. Retailers applying AI-driven forecasting report better responsiveness to demand spikes and lower costs related to overstock or stockouts.
Consider a fashion retailer that implemented an AI agent system. The AI monitored social media trends and past sales, adjusting orders weekly instead of quarterly. This shift led to a 15% increase in sell-through rates and a 12% reduction in excess inventory within a year.
Conclusion: Which Method Fits Your Business?
If your retail operation handles relatively stable demand and values straightforward insights, traditional BI may suffice. But if you face frequent market shifts or complex data streams, AI agents provide a more responsive, flexible approach to forecasting.
No single method is perfect. Many businesses benefit from combining both—using traditional BI for foundational analysis and AI agents for ongoing, adaptive forecasting.
Want to use AI in your business?
At Adanto Software, we help retailers integrate advanced forecasting tools that fit their unique needs. Reach out to learn how you can make forecasting smarter.
Adanto has helped us be more productive and monitor costs of an AWS cloud
Dan Powers
Director, IT Shared Services
Robert Half
Adanto accelerates Software Delivery with CI/CD Automation
Adanto’s CI/CD DevOps solution streamlined software delivery by automating workflows, integrating 13 siloed projects, and enabling continuous deployment on AWS.
I want to thank the entire Adanto team for all your efforts and help with ITMCC and Robert Half. I want to thank the team for your efforts on the project, providing the resources so quickly and being so flexible and nimble during the development cycle. Adanto is our go to partner for our new initiatives in our Marketing vertical
Frank Ficken
IT Portfolio Manager
Robert Half
Really happy with Adanto’s work and your engineering capabilities in the C#/.Net back end development of our LSX platform. Our team has voted very high marks and would like to keep utilizing your services.
John Crowley
Chief Software Architect
Fujifilm NA Corporation, Imaging Division
Adanto delivers Mazoola digital e-wallet platform
Adanto Mazoola – the only for kids and families. This , , and robust platform delivers exceptional , , and you can trust unfailingly.
Key Results
5 weeks
Integration time it took for the first Mazoola’s Payment Platform
21 services
Total number of best of breed financial services Integrated with Mazoola
+466%
REGO’s stock value increase (as of Dec’2024) since Adanto’s first release of its first product version on the market
Technologies used
- Cloud Platform hosting – Azure
- iOS mobile app written in Swift
- Android mobile app written in Java
- Frontend services – React Native
- Backend services written in C#/.NET
- Microsoft SQL Server
- Web app written in Node.js
- Password vault – Dashlane
- DevOps tools – Azure Repos, Boards, Pipelines
- Penetration & Performance tests – Jasmine
Challenge
The challenge faced by Rego Payments in 2020 centered around the evolving regulatory landscape, technological demands, and the increasing emphasis on data privacy, particularly concerning children and families.
Key goals
Develop a cloud-based digital wallet that could meet high standards and integrating it with financial systems
Build a highly scalable, cloud-native architecture using advanced security measures and AI-driven fraud detection to support seamless and safe user experiences.
Enable Seamless Digital Payments to support online and in-store purchases via virtual cards, merchant category filtering, and integrations with popular platforms like Apple Pay.
Develop API integrations to expand functionality and interoperability with third-party financial tools and platforms and create a modular framework that allows for future expansion, including partnerships with educational or banking services.
Solution
Mazoola’s technical solution represents a sophisticated blend of fintech innovation and strict regulatory adherence, offering a safe, scalable, and family-friendly digital wallet experience featuring:
- Cloud-Native Architecture with regional redundancy, global scalability, performance, multi-region deployment with low latency and high throughput to handle large volumes of transactions.
- Secure Wallet Infrastructure with tokenized virtual cards support for online and in-store purchases, real-time fraud detection, and multi-layer authentication
- Seamless Payment Integration compatible with major payment networks
- Extensibility that exposes a suite of RESTful APIs for integration with best-of breed third-party platforms, financial services or additional tools.
- Strictest Regulatory COPPA and GDPR Compliance and Privacy, anonymized data handling of children’s PII and end-to-end encryption
- Parental Control and User Management with role-based access with distinct permissions , and real-time transaction monitoring.
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Key Results
$600k
Annual reduction in errors & downtime through automating infrastructure management and integration testing
$325k
Reduction in Developer Downtime
with automated testing, error alerts, and real-time monitoring, annually
$65k
Annual savings from reduced manual testing and deployment scripts and CI tools by 50%
Technologies used
- Metrics:
- Source of projects from 3 different version control systems
- 13 silo projects integrated in one
- Technologies used:
- AWS AMI (Amazon Machine Images)
- AWS EC2 (Elastic Compute Cloud)
- AWS S3 (Simple Storage Service)
- AWS CloudFormation
- AWS Cloudwatch
- AWS CLI (Command Line Interface)
- Jenkins Continuous Integration
- Sonarqube
- Maven
- Nexus
- HashiCorp Packer
- Bash scripting
- PowerShell
- Chef (Infrastructure Automation)
- RabbitMQ
- HA Cluster
- Angular, Python, Java, NodeJS, Drupal,
- Unix,
- Git, Bitbucket, SVN,
- Slack – notification of an error for configured groups
Challenge
Robert Half struggled to manage multiple concurrent software projects built by different teams using various programming languages on a shared cloud infrastructure and on-premise databases. The lack of interoperability between siloed projects caused frequent errors, extensive testing, and infrastructure-related issues. Misaligned changes to the shared environment led to disruptions, frustrating developers and causing severe data center outages that impacted thousands of users, emphasizing the need for a unified, automated solution to streamline development and deployment.
Key goals
Enable Cross-Project Integration: Ensure seamless collaboration between siloed projects to prevent disruptions and errors
Automate Testing: Reduce errors and costs by implementing automated testing during development and deployment
Improve Monitoring: Introduce monitoring and alerts to quickly detect and resolve issues during development
Streamline Deployment: Build a stable, automated platform for faster, more efficient continuous integration and deployment
Solution
Adanto implemented a Cross-Project Cloud Integration Platform (CP-CIP) to address the client’s challenges in managing multiple siloed projects within a shared cloud infrastructure. This solution introduced automation, monitoring, and integration tools to streamline development, reduce errors, and ensure seamless collaboration across projects.
- Automated Infrastructure: Scripts automated project-specific environment creation, ensuring consistency and eliminating manual setup errors.
- Integration Testing: Automated cross-project tests ensured compatibility during development and retrofitted for production, preventing conflicts.
- Continuous Integration: A CI platform using Jenkins and SonarQube automated builds, testing, and deployments, supporting frequent releases.
- Monitoring and Alerts: AWS CloudWatch and Slack alerts provided real-time error detection and faster issue resolution.
- Streamlined Workflows: Trigger-based scripts automated environment builds, deployments, and testing, reducing manual effort.
- Unified System: Integrated 13 siloed projects into a single platform, improving visibility and reducing inefficiencies.
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Key Results
$10M+
Estimated annual cost savings from automation and operational efficiancies
15%
Annual revenue growth from much more precise lead gen
40%+
Reduction in recruitment cycle times enabling the company to fill client needs faster.
Services performed
- Data Science
- Data Analytics & Business Intelligence
- Data Warehousing
- Machine Learning
- Artificial Intelligence
- Natural Language Processing
- Web & Mobile Apps
- UX/UI
- Custom Application
- Development
- DevOps
- Security
- Infrastructure Services
- Administration Services
- Azure Cloud
Technologies used
- C#/.Net Application Framework
- Enterprise SOA Platform
- SOA-connected external services
- Natural Language Processing algorithms for data correlation and analytics of many file formats (*.pdf, *.rft, *.doc, *.txt)
- Amazon AWS Cloud
Challenge
- Sales teams dissatisfaction with the leads process, lead quality and lead management.
- Very poor sales productivity as measured by the sales effectiveness index.
- Non-standard individual lead generation activity leading to poor quality, wasted time
- Stagnant quarterly revenue and market share loss to niche players
- Hard to find information while on-the-go
- Complexity.
Key goals
Improve lead conversion rate
by 30%
Establish more predictable
and more sustainable profitable revenue growth, Empower sales and marketing while on-the-go
Significantly Improve Sales Effectiveness, Sales Productivity, and Sales Satisfaction.
Deliver targeted, hot leads to the right sales specialist in a simple form, at the time of need
Solution
- The Profile Writer application installation package
- Quill Data Science App connector
- Sovren connector
- S3Bucket connector
- RDS connector
- Amazon connector
- SHIM connector
- SHIM services implementation
- Positive final security remediation report
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Key Results
100k+
Total number of micro-service apps automated at all US centers
85%
Costs reduction, for the new process currently requires only minimal manual intervention.
$1.83M
Estimated annual savings, brought about by the automation.
Technologies used
- Infrastructure:
- ASP.NET Web Application
- Micro-services architecture
- C#/.NET Back-end
- Cloud Platform hosting – Azure
- Express, Vue.JS, Electron/Chromium
- Azure DevOps pipelines
- Microsoft SQL Server
- Micro-Services:
- RESTful API services
- Node.JS service
- MongoDB as a Windows service
- Redis as a Windows service
- Nginx as a Windows services
- LSX App services
- Print services
- Order services
- Data Ingest services
- Remote upload data service
- Data:
- Windows Event Logs
- Windows System Information
- Windows Registry Logs
- Print Service Logs
- Windows Services Logs
- LSX Services Logs
- LSX Installer Logs
Challenge
Photo Centers faced several challenges, including extensive manual effort required to analyze system activities, the lack of remote access to logs across all photo servers, and slow, costly error detection processes. Additionally, manual shutdowns of individual centers posed risks such as data loss and disruption of image orders. The absence of automatic system update capabilities further exacerbated operational inefficiencies.
Key goals
Reduce Manual Work: Automate data collection, monitoring, and reporting to eliminate the need for manual system analysis in each Photo Center.
Ensure Safe Shutdowns: Implement a secure, automated shutdown protocol for micro-service apps to prevent data loss & preserve the integrity of image orders.
Enable Remote Access to Logs: Provide remote access to logs across all photo servers to improve the speed and efficiency of error detection and resolution.
Improve Error Detection & Resolution: Enhance error detection speed and reduce costs using real-time monitoring and automated alert systems.
Solution
Centralized Logging System:
- ASP.NET Web Application acts as the core of the centralized logging system, aggregating logs from all micro-services across photo centers, regardless of programming language. It also gathers Windows system logs, including services, registry, event logs, and system information.
- Log Collection and Aggregation: Consolidates real-time logs from various micro-services into a unified repository, enabling efficient monitoring and analysis of system activities across photo centers.
Remote Shutdown System:
- ASP.NET Web Application: Manages the orderly shutdown of all micro-services per photo center , ensuring data integrity and preventing image order loss.
- Automated Shutdown Protocol: Executes a step-by-step shutdown, prioritizing service dependencies for graceful completion.
- System Update Automation: Seamless deployment of system updates.
- Data Integrity Assurance: Ensures all transactions are completed and data securely stored to prevent corruption or loss
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Adanto Boosts Service & Cuts Costs with Big Data Analytics
Adanto’s Real-Time Big Data Analytics solution streamlined operations for a global HR leader, cutting abandoned calls from 18% to 5%, reducing hold times to 4 minutes, and enhancing real-time insights with advanced reporting and dashboards.
Key Results
72%
Reduction of the abandoned calls (from 18% to 5%), translating to thousands of retained customer interactions annually
73%
Decrease in average call hold time (from 15 to 4 min), significantly improving customer experience and reducing costs
20-30%
Estimated annual savings in operating costs due to reduced manual reporting, improved SLA
Technologies used
- ETL and Data Integration:
- Pentaho Data Integrator (ETL tool)
- MySQL (for data warehousing)
- APIs and Real-Time Data Access:
- REST APIs (for real-time data access and updates)
- Cloud Infrastructure:
- Amazon AWS (cloud storage and processing)
- Microsoft Azure (cloud storage and analytics platform)
- Reporting and Dashboards:
- Microsoft Power BI (for advanced reporting and interactive dashboards)
- Machine Learning and Analytics:
- Custom machine learning algorithms (for data correlation and advanced analytics)
- Source Systems for Data Extraction:
- ShoreTel (PBX business phone systems)
- CIC (Customer Interaction Center)
- ServiceNow Cloud (service management system)
Challenge
Environment: A complex global operation with 15 contact support centers across North America, EMEA, and APAC regions, handling 2.5 million monthly events across 30 disparate databases, using systems like ShoreTel, CIC, and ServiceNow Cloud.
Challenge: Manual reporting, data silos, and lack of real-time insights led to poor SLA performance, delayed issue resolution, high operating costs, and customer dissatisfaction.
Key goals
Enable real-time analysis of caller behavior for management decision-making
Provide accurate, real-time reports for call center management and stakeholders
Deliver predefined executive dashboards accessible via cloud on mobile and desktop
Streamline operations by integrating data from multiple systems and improving reporting accuracy
Solution
Adanto implemented a powerful real-time Big Data Analytics solution to transform a global HR leader’s contact center operations. By integrating advanced technologies, cloud platforms, and machine learning, Adanto streamlined a complex, siloed environment into a real-time analytics system:
- Data Integration: Adanto centralized data from 30 disparate databases using Pentaho ETL and MySQL, integrating sources like ShoreTel, CIC, and ServiceNow
- Real-Time Access: REST APIs enabled continuous data updates, providing stakeholders with up-to-date insights for decision-making.
- Machine Learning: Advanced algorithms correlated data across systems, achieving 89% accuracy by using time as a common key.
- Cloud Infrastructure: AWS and Azure ensured scalable, secure storage and accessibility for global users.
Reporting and - Dashboards: Microsoft Power BI delivered real-time dashboards and reports, enabling data-driven decisions from any devices.
- Optimization: Custom tools enhanced data collection and accuracy, resolving inefficiencies and boosting operational performance.
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Key Results
+100%
Sales growth in 24 months after release of new CRM platform
+4%
Sales growth in 12 months prior to releasing new CRM platform
12 months
Time it took Adanto from understanding client needs to a launch of its first release.
Technologies used
- Back-end developed in C#/ASP.NET Core, storing its data in a RDB PostgreSQL.
- UI is a JavaScript SPA (Single Page Application) based on React, Angular & Vue.
- The system is deployed using CI/CD pipelines to the Cloud (AWS & Azure) and rely on managed cloud services.
- The Infrastructure as Code approach is used, automating the setup of required cloud resources.
- Service layer implementation is deployed using REST and JSON
Challenge
The myUtilities company faced many challenges, before approaching Adanto. They were challenged with fragmented customer management, billing inefficiencies, low customer engagement, and outdated and faulty systems based on old technology that hindered scalability and operational workflows. Additionally, they struggled with integrating modern tools like phone, phone messaging, text messaging, poor and confusing user experience which caused dissatisfaction with sales teams that translated in very lackluster sales.
Key goals
Customizable multi-Tenancy and Integrations that support licensee-specific configurations, commission structures, for insurance, energy providers, and phone systems (e.g., RingCentral, Cisco).
Enhanced User Experience and workflow with redesigned front-end for streamlined lead-to-sale workflows, role-based customizable dashboards, and auto-generated activity-based tags for account status visibility.
Advanced Reporting and Automation w/near real-time reporting integrated with PowerBI/Tableau and a flexible commission engine with API extract capabilities. Automated marketing campaigns with event triggers for customer engagement
Robust Modern Cloud Architecture and Security with built-in disaster recovery (BC/DR) capabilities with periodic testing and architecture aligned with SOC2 Type 2 cybersecurity standards for scalability and compliance.
Solution
- Multi-Tenancy: Supports feature and functionality configuration for different licensees, along with tailored commission structures and integrations.
- Workflow Optimization: Offers a redesigned, logical flow from lead management to sales and processing, with automation and real-time status updates.
- Scalable and Secure: Built on a robust architecture with disaster recovery (BC/DR) capabilities, compliance with SOC2 Type 2 standards, and integration options for APIs and third-party systems.
- Advanced Analytics and Engagement: Incorporates near real-time reporting, marketing automation, and tools for improved customer engagement and retention.
Key Features
The myUtilities Multi-Function CRM Solution is a comprehensive, customizable platform designed to streamline customer relationship management, enhance operational efficiency, and integrate seamlessly with utility services and partner ecosystems. It provides tools for multi-tenancy, role-specific dashboards, automated workflows, and advanced reporting, enabling businesses to manage customer data, billing, and communications effectively.
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We are tremendously pleased with Adanto’s quality and speed of delivery for AWS Security engineering services to our clients in the Financial Services sector. Your expertise was sorely needed by our organization. I only wish we’d engaged you earlier!
Chris Miller
Manager of Information Security Operations
ECMC
Adanto Powers Big Data Democratization for Robert Half
Adanto delivers a cloud-based Big Data Lake solution for a Silicon Valley consulting leader, eliminating data silos, reducing costs, and enabling seamless access to raw data. The solution fosters a data-driven culture and empowers users with scalable, flexible analytics.
Adanto and especially Magic were instrumental in getting our iTrack Reporting Workstream Project on track and successful, beyond expectations. Thank you. The whole team was wonderfule to work with in on site in San Ramon, CA and off-site from Poland
Thuy Nguyen
Sr. Manager, IT Development
Robert Half
Adanto Team has exceeded my expectations, delivered solid results and offered very valued support in place of several previous suppliers who have only left confusion and lots of bugs
Jeanne McDonald
CFO
Tangible Investments
Adanto accelerates Software Delivery with CI/CD Automation
Adanto’s CI/CD DevOps solution streamlined software delivery by automating workflows, integrating 13 siloed projects, and enabling continuous deployment on AWS.
Talking to a business should feel easy. But long wait times, repeated questions, and poor support can frustrate customers. That’s where Voice AI agents come in. These tools are designed to handle real-time conversations using artificial intelligence. They can understand what people say, figure out what they need, and respond with a voice that sounds natural. Unlike basic phone menus or chatbots, Voice AI agents can hold two-way conversations and offer real help—without involving a human agent every time.
In this article, we’ll walk through what a Voice AI agent is, how it works, what features matter, and how it can help your business. If you’re exploring ways to improve customer service or reduce call center costs, this guide will give you a solid starting point.
Table of Contents
What is a Voice AI Agent?
A Voice AI Agent is a virtual assistant that communicates with users via spoken language. Unlike traditional IVR (Interactive Voice Response) systems that follow rigid scripts and often frustrate users, Voice AI Agents use artificial intelligence to understand, interpret, and respond in a natural, human-like way.
They can handle a wide range of tasks:
- Answering customer support queries
- Booking appointments
- Providing product recommendations
- Processing orders
- Collecting feedback
Think of them as digital team members who never sleep, don’t lose patience, and continuously improve over time.
How AI Voice Agents Work
A Voice AI Agent works through a blend of voice recognition, natural language processing, machine learning, and backend integration. When a user speaks, the AI listens, deciphers intent, and crafts a meaningful response—all in real time.
Here’s a typical flow:
- User Speaks: “I want to know my order status.”
- Voice Input is Captured via microphone or phone call.
- Speech-to-Text (STT): Converts spoken words into written text.
- Natural Language Understanding (NLU): Interprets what the user means.
- Dialog Manager: Decides how to respond.
- Text-to-Speech (TTS): Converts the reply back into speech.
- Voice Output: “Sure, let me check your order. Can I have your order number?”
It all happens in seconds—and gets smarter with every conversation.
Key Components of Voice AI Agent Architecture
To understand how these agents operate, let’s break down their core architecture:
- Automatic Speech Recognition (ASR)
This component transforms spoken language into text. Accuracy here is crucial—especially with different accents, speeds, or background noise. - Natural Language Processing (NLP)
NLP handles two parts: understanding user intent and generating a human-like response. It’s what allows the agent to grasp the meaning behind words. - Dialog Management System (DMS)
The DMS decides how the agent responds. It uses context, previous interactions, and logic flows to ensure the conversation feels natural. - Text-to-Speech (TTS)
Converts the AI’s response from text back to speech. Modern systems now have expressive, natural-sounding voices with varied tones and emotions. - Backend/API Integrations
To be truly useful, Voice AI Agents must connect with CRMs, order systems, databases, calendars, and other business tools.
Training & Analytics Layer
This layer helps the system learn from user interactions, spot friction points, and improve accuracy over time.
Important Features of an AI Voice Agent
What makes a voice AI agent powerful and business-ready? Here are key features to look for:
- Real-Time, Natural Conversations: No awkward pauses or robotic replies. It should talk like a real person.
- Context Retention: Good agents remember previous interactions within a session—sometimes even across sessions.
- Multilingual Support: Serve customers in their native language or dialect.
- Personalization: Greet users by name, remember preferences, and adapt responses.
- 24/7 Availability: AI agents never clock out.
- Scalability: Handle thousands of conversations simultaneously without delays.
Seamless Handover: When needed, they can transfer the conversation to a human agent—complete with conversation history.
Benefits of Voice AI Agents for Your Business
Why should a business invest in a voice AI agent?
Imagine you’re running an e-commerce company. During peak shopping season, your support team is swamped with queries: “Where’s my order?”, “Can I return this item?”, “What’s your exchange policy?”
Instead of hiring and training dozens of temporary agents, a voice AI agent can:
- Handle 80% of repetitive queries automatically
- Reduce customer wait time to near zero
- Free up your human team for complex cases
- Cut operational costs significantly
- Increase customer satisfaction with quick, consistent answers
And in industries like banking, healthcare, and retail, AI voice agents are already showing measurable ROI—from improved conversion rates to reduced churn.
Conclusion
Voice AI Agents are a smart way for businesses to talk to customers quickly and easily. They can answer questions, help with orders, and give support—any time, day or night.
They don’t replace your team, but they help your team work better by handling simple, repetitive tasks. This means faster service for customers and lower costs for your business.
Let’s Talk
At Adanto Software, we help companies build and use Voice AI Agents that fit their needs. If you’re thinking about using AI to improve your customer experience, we’re here to help you get started.
Artificial intelligence has come a long way – from basic automation to powerful language models. But the real revolution is happening now with Agentic AI: a new class of intelligent systems that can autonomously reason, plan, and act in pursuit of goals.
In this blog, we’ll break down what Agentic AI is, why it matters, and what makes it the most powerful AI architecture yet.
Table of Contents
What Is Agentic AI?
Agentic AI refers to intelligent software agents capable of setting goals, making decisions, and acting autonomously in dynamic environments—without the need for step-by-step human instruction.
These AI agents can:
- Interpret high-level objectives
- Break them down into tasks
- Decide on a course of action
- Execute and adapt in real time
In simple terms: Agentic AI thinks, acts, and learns independently—delivering proactive value rather than passively responding to commands.
Why Agentic AI Is a Paradigm Shift
Artificial Intelligence has evolved in waves:
- Traditional AI was built on rigid rules, statistical models, and pattern recognition—great for automation, but limited to narrow, pre-defined tasks.
- Generative AI brought creativity and contextual understanding, producing human-like text, images, and code from natural language prompts—but it still depends on static inputs and doesn’t take initiative.
Now, Agentic AI is redefining the entire model. It blends the best of previous AI generations—pattern recognition, generative capability, and contextual reasoning—with a new layer of autonomy and intentionality. These AI agents don’t just respond to prompts or follow workflows. They set goals, make decisions, and adapt their strategies dynamically—much like a human employee or collaborator would.
This shift is architectural.
Shift in Capabilities
1. From Reactive to Proactive
Traditional AI and chatbots wait for user input before acting. Agentic AI, by contrast, can anticipate needs, detect opportunities or risks, and take action—sometimes before a human even notices the problem.
🧠 Example: An AI sales agent notices a drop in pipeline velocity and automatically re-engages cold leads or recommends campaign changes—without waiting for a prompt.
2. From Static Rules to Dynamic Learning
Earlier AI systems were rule-based—requiring constant tuning, training, and human oversight. Agentic AI evolves through feedback loops, reinforcement learning, and real-time environmental cues, continuously improving its own performance.
🔁 Example: A customer support agent learns that certain ticket types lead to high churn. It starts escalating those tickets faster and suggesting new macros to human reps.
3. From Task Automation to Strategic Autonomy
Automation solves isolated tasks. Agentic AI tackles complex, multi-step goals that require reasoning, prioritization, and cross-functional coordination.
🎯 Example: In e-commerce, instead of just recommending a product, an agent can run a sequence of actions: detect cart abandonment, tailor follow-up offers, test different discount levels, and adapt based on conversion success—all autonomously.
Core Capabilities of Agentic AI
1. 🛰 Autonomy
Autonomy is the defining trait of Agentic AI. These agents can initiate actions, orchestrate processes, and make decisions independently—without waiting for step-by-step instructions or constant human intervention.
Rather than being reactive, autonomous agents observe the environment, recognize triggers, and take meaningful actions—all on their own.
Example in Fintech:
An agent monitoring financial transactions identifies a suspicious pattern indicative of potential fraud. Instead of just flagging it, the agent pauses the transaction, initiates a real-time compliance review, and notifies relevant teams—all autonomously.
2. 🧠 Reasoning
Reasoning gives agents the ability to analyze data, weigh alternatives, and make logic-driven decisions – even in ambiguous or dynamic environments.
Agents aren’t just rule-followers—they’re decision-makers. They can process unstructured inputs, understand context, and take the most appropriate next step based on situational logic and learned patterns.
Example in E-commerce:
An AI agent reviews a shopper’s recent behavior: abandoned cart, high browsing time, and frequent returns. It decides whether to offer a discount, recommend an alternative product, or trigger a loyalty email—based not on a script, but on intelligent evaluation of buyer intent.
3. 🎯 Goal-Setting and Planning
Agentic AI doesn’t just execute tasks—it can set goals, break them down into subtasks, choose how to proceed, and dynamically re-plan based on progress.
Agents understand objectives, create strategies to achieve them, and adapt those strategies over time. This makes them capable of managing multi-step processes and aligning with broader business outcomes.
Example in Customer Service:
An AI agent receives an overarching goal: “Reduce response time for high-priority tickets.” It analyzes ticket flow, identifies bottlenecks, reorganizes queues, and prioritizes escalations—while adjusting tactics as volume shifts or issues evolve.
4. 🔄 Learning and Adaptation
Through continuous exposure to new data and feedback, Agentic AI can learn from experience, refine its models, and improve performance over time.
These agents develop memory and evolve behavior. They analyze outcomes, learn what works (and what doesn’t), and apply those learnings the next time—without requiring reprogramming.
Example in Retail Planning:
An inventory optimization agent tracks real-time sales, seasonal fluctuations, and local trends. Over time, it learns that certain items sell better during specific events or weather patterns—and adjusts stocking and pricing strategies accordingly.
Conclusion
Agentic AI isn’t just the next step in automation — it’s a shift in how work gets done.
These systems don’t wait for instructions. They make decisions, take action, and improve with every cycle. That means less micromanagement, faster execution, and smarter results.
If your business runs on speed, scale, or complexity — that’s the future of staying competitive.
Want to use AI in your business?
If you’re curious how Agentic AI could work in your business — whether that’s improving support, spotting risks early, or making operations more efficient — we’d be glad to walk you through it.
Retail forecasting has always been a tough challenge. Businesses want to predict demand, manage inventory, and optimize pricing to stay competitive. Traditionally, companies have relied on Business Intelligence (BI) tools — dashboards, historical data analysis, and static reports — to guide decisions. But now, AI agents are becoming more common in retail. They can process data continuously, adapt to changes, and even automate decisions.
Which approach works better for retail forecasting? This article compares AI agents with traditional BI to see which one delivers more value, especially in a fast-moving retail environment.
Table of Contents
The Basics: Traditional BI in Retail Forecasting
Traditional BI focuses on gathering historical data and presenting it through reports or dashboards. Retailers use this data to identify patterns, track sales trends, and plan stock levels. The advantage is clear visibility into what has happened and some indication of what might come next based on past trends.
However, this approach has limits. Traditional BI often depends on manual data updates and fixed reports that may become outdated quickly. It doesn’t always react well to sudden market shifts like supply chain disruptions, seasonal changes, or emerging consumer trends.
What AI Agents Bring to the Table
AI agents take forecasting a step further. Instead of waiting for a human to interpret reports, these systems analyze data continuously, learn from new information, and adjust predictions in real time. They can integrate data from multiple sources—sales, weather, social media, even competitor pricing—to refine their forecasts.
For example, an AI agent might detect a sudden rise in demand for a product due to a viral trend and automatically suggest changes in inventory orders. This dynamic response is hard to achieve with traditional BI alone.
Strengths and Weaknesses of Both Approaches
Traditional BI tools are straightforward and familiar to many businesses. They provide useful snapshots but often struggle to keep pace with rapid changes.
AI agents offer flexibility and speed. But they depend heavily on accurate, diverse data and may require investment in technology and skills.
Real-World Impact: Examples and Data
Research from McKinsey shows that companies using advanced analytics for forecasting see inventory reductions of up to 20% and service level improvements by 10%. Retailers applying AI-driven forecasting report better responsiveness to demand spikes and lower costs related to overstock or stockouts.
Consider a fashion retailer that implemented an AI agent system. The AI monitored social media trends and past sales, adjusting orders weekly instead of quarterly. This shift led to a 15% increase in sell-through rates and a 12% reduction in excess inventory within a year.
Conclusion: Which Method Fits Your Business?
If your retail operation handles relatively stable demand and values straightforward insights, traditional BI may suffice. But if you face frequent market shifts or complex data streams, AI agents provide a more responsive, flexible approach to forecasting.
No single method is perfect. Many businesses benefit from combining both—using traditional BI for foundational analysis and AI agents for ongoing, adaptive forecasting.
Want to use AI in your business?
At Adanto Software, we help retailers integrate advanced forecasting tools that fit their unique needs. Reach out to learn how you can make forecasting smarter.
Adanto Boosts Efficiency, Security at US Walmart stores
Adanto reduces cost and operational downtime by improving efficiency and security at 4,000 Walmart’s Photo Centers operating by Fujifilm’s mobile image printing kiosk services.
Customer service is evolving at lightning speed. As customers demand faster, smarter, and more personalized support, traditional call centers and help desks struggle to keep up. Enter Agentic AI — autonomous, proactive, and context-aware systems that don’t just respond to requests but think, decide, and take action on your behalf.
If you’re wondering how to bring Agentic AI into your customer service, here are three game-changing ideas — each tackling a real challenge and driving measurable results.
Table of Contents
Autonomous Handling of Routine Support Tickets
The Challenge:
Your support team is swamped with repetitive, low-complexity queries — order tracking, password resets, return policies. These take time, frustrate customers with wait times, and drain resources that could be better spent on complex issues.
How Agentic AI Helps:
Agentic AI can autonomously manage these routine requests without human intervention. It understands the context, provides instant, accurate answers, and escalates only when needed — all while remembering customer history to keep conversations smooth.
The Outcome:
- Slash ticket volume by up to 60%
- Cut average response times drastically
- Free up your agents to focus on what truly matters
- Boost customer satisfaction with faster, smarter support
Proactive Customer Engagement to Reduce Churn
The Challenge:
Many companies wait too long to engage customers about renewals, upgrades, or potential issues. The result? Missed opportunities, avoidable churn, and lost revenue.
How Agentic AI Helps:
Agentic AI monitors customer behavior and subscription timelines, predicting when customers need outreach. It sends personalized messages or offers before a customer even realizes they might leave or have a problem.
The Outcome:
- Cut churn rates by 20–30% or more
- Increase upsell and cross-sell conversions
- Build stronger, proactive relationships with your customers
- Lighten the load on your human agents
Real-Time AI Assistance to Empower Agents
The Challenge:
Customer service agents often face complex or emotionally charged interactions without real-time guidance. This can lead to slower resolutions, inconsistent experiences, and agent burnout.
How Agentic AI Helps:
Imagine an AI sitting beside your agent during calls or chats — suggesting the best responses, surfacing relevant knowledge, and even detecting customer emotions to adapt the approach on the fly.
The Outcome:
- Speed up call resolution times by 15% or more
- Improve customer sentiment and satisfaction
- Reduce agent stress and turnover
- Ensure consistent, on-brand communication every time
Ready to Bring Agentic AI Into Your Customer Service?
The future of customer service is proactive, personalized, and powered by intelligent AI that augments your team — not replaces it. Whether you start with automating routine tickets, proactive outreach, or real-time agent support, Agentic AI can transform your customer experience while boosting efficiency.
Reach out to the experts at Adanto Software.
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Protiviti
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Most e-commerce personalization is still basic. It shows “related items” or “people also bought.” But today’s customers expect more than that. They want help, not suggestions.
Agentic AI makes this possible.
It can understand intent, take action, and guide users through tasks — like a smart assistant inside your store. In this article, we’ll look at how agentic AI is changing the e-commerce experience.
Table of Contents
Why Today’s Personalization Falls Short
Most e-commerce platforms do this:
- You looked at product A → So here’s product B.
- You added one thing → So here’s a “frequently bought together” set.
- You visited twice → Here’s a discount.
It works — until it doesn’t.
These systems rely on patterns, not purpose. They don’t understand what the customer is trying to do.
And when things change — trends, prices, demand, seasons — the system can’t keep up.
What Agentic AI Enables in E-commerce
Imagine you walk into a store. Before you say a word, someone already knows what you’re looking for — not in a creepy way, but because they’ve seen people like you before. They notice what you’re holding, how long you stare at the shelf, and what questions you pause to ask in your head.
Then they say:“Hey, based on what you need — here’s a better way to do this.”
That’s what Agentic AI does. It doesn’t wait for instructions. It watches, learns, and acts — while the shopper is still deciding.
An agent can:
- Understand what the user is trying to do (intent detection)
- Ask questions to fill in missing context
- Decide what steps are needed to help them reach that goal
- Adapt the UI, product options, or offer structure in real time
- Learn from what works or fails — and adjust behavior
Use Case #1: Guided Product Discovery
The problem: Shoppers are overwhelmed. They don’t know what they need — especially with technical or multi-part products.
What happens with an agent:
Someone visits your store looking for gear to film cooking videos. Instead of scrolling through 50 cameras, they answer 3 simple questions. The agent suggests a full setup: camera, tripod, lighting — all matched to their use case. Ready to buy in one click.
Why it matters:
Fewer abandoned sessions. More confident purchases. No guesswork.
Then they say:“Hey, based on what you need — here’s a better way to do this.”
That’s what Agentic AI does. It doesn’t wait for instructions. It watches, learns, and acts — while the shopper is still deciding.
An agent can:
- Understand what the user is trying to do (intent detection)
- Ask questions to fill in missing context
- Decide what steps are needed to help them reach that goal
- Adapt the UI, product options, or offer structure in real time
- Learn from what works or fails — and adjust behavior
Use Case #2: Smart Bundling and Upselling
The problem: Upsells often feel random or pushy. They don’t add real value.
What happens with an agent:
A shopper adds a laptop. The agent builds a remote work bundle — keyboard, monitor, warranty — customized to what makes sense for this model and use. Pricing adapts to stock, season, and margin.
Why it matters:
Higher order value. Smarter promos. Better experience.
Use Case #3: Goal-Based Shopping Journeys
The problem: People often shop with a goal — not a product in mind.
What happens with an agent:
Someone’s planning a 3-day winter hike. The agent builds a checklist: layered clothes, tent, food kits, thermal gear. Checks delivery dates to match their trip. Adds backup options.
Why it matters:
More complete orders. Fewer forgotten items. Real help, not just suggestions.
Use Case #4: Post-Purchase Agents
The problem: After checkout, most brands disappear — or send generic emails.
What happens with an agent:
You buy an espresso machine. The agent follows up with setup help, cleaning tips, offers for beans or accessories, and a reminder to leave a review — timed to when you’ve actually used it.
Why it matters:
Longer retention. More upsells. Better product experience.
Conclusion
Agentic AI won’t replace your team. But it can do what static systems can’t: act in real time, for real people, based on real context.
It turns your store into something smarter. Something that helps users get what they came for — without the friction.
If you care about lifetime value, conversion, and customer experience, this is worth exploring.
Want to use AI in your business?
Get in touch with the Adanto Software Team today to see how we can help.
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THX
We are tremendously pleased with Adanto’s quality and speed of delivery for AWS Security engineering services to our clients in the Financial Services sector. Your expertise was sorely needed by our organization. I only wish we’d engaged you earlier!
Chris Miller
Manager of Information Security Operations
ECMC
I am giving my highest recommendation for Adanto Software. Having dealt previously with a tech who answered in a day or two, Adanto’s responses were truly impressive.
Paula Miller
CEO
Iconic Idaho
In 2024, global fraud losses reached $485 billion, with digital payment fraud rising by 18% year-over-year. The fraud landscape is evolving fast—driven by automation, AI-assisted scams, and synthetic identities. Traditional fraud detection systems struggle to keep pace, largely because they rely on rule-based logic and reactive human workflows.
To respond in real time, organizations need systems that can act on their own. This is where autonomous AI agents come into play. These agents don’t just identify fraud—they execute decisions, trigger actions, and evolve with each case they process. In short, they operationalize intelligence at machine speed.
In this article, I’ll explain how these agents work, what they’re capable of, and where they’re already delivering measurable impact — particularly in financial services, insurance, and e-commerce.
Table of Contents
What Is an Autonomous AI Agent?
Autonomous AI agents are software entities that can perceive data, reason over it, make decisions, and take actions—without human intervention. These agents typically combine:
- Real-time data ingestion
- Machine learning (often anomaly detection, clustering, or reinforcement learning)
- A decision-making engine
- A trigger mechanism (e.g., blocking, alerting, escalating)
Unlike static ML models embedded in a rules-based system, autonomous agents are built for continuous operation. They interact with other systems, manage workflows, and adapt based on outcome feedback.
Why Traditional Fraud Detection Falls Short?
Most fraud detection pipelines today are reactive:
- Transactions are scored based on predefined thresholds
- Alerts are queued for review
- Analysts triage cases manually
- Action is taken hours or days later
This approach introduces delays, fatigue, and inconsistency. Worse, fraudsters exploit these weaknesses with rapid attacks that mimic normal behavior. Static systems can’t detect these dynamic patterns fast enough—and they certainly can’t respond in real time.
How Autonomous Agents Address the Gap?
Autonomous agents are designed to close the decision-action loop. Here’s how:
- Continuous monitoring: They evaluate live data streams rather than periodic batches.
- Pattern learning: They learn over time—detecting not just known fraud, but emerging anomalies.
- Decision execution: They act immediately—freezing accounts, flagging claims, or launching investigation workflows.
- Feedback loops: They learn from past actions, enabling them to refine future decisions.
Agents can also operate across systems—integrating with CRMs, payment processors, document repositories, and third-party data providers.
Key use cases and results
Let’s explore where autonomous agents are already in use, and what kind of value they’re delivering.
Financial Services – End-to-End Case Handling
A credit union partnered with Accelirate to streamline its fraud operations. An AI agent was deployed to:
- Check transactions across Symitar and Extranet
- Match against historical behavior
- Eliminate duplicates
- Trigger escalation workflows
Results:
- 657 analyst hours saved annually
- 98% reduction in processing errors
- $19,800 in direct cost savings
Insurance – Claims Fraud Detection
An insurer used an agent to review low-dollar claims submitted within short timeframes across multiple user profiles. The agent:
- Flagged matching metadata and document reuse
- Pulled historical claims from different user IDs
- Auto-generated fraud reports for the investigation team
Results:
- 245% ROI within the first year
- $320,000+ in savings
- 62% reduction in claim resolution time
E-Commerce – Loyalty Abuse Prevention
Retailers are increasingly targeted by bot-driven attacks—fake signups, coupon abuse, and identity farming. AI agents can:
- Detect fake account clusters (shared IPs, browser fingerprints, timing anomalies)
- Flag attempts to exploit loyalty programs
- Pause reward disbursement and notify risk teams
Impact: Fewer false positives than rules-based systems, with real-time enforcement and reduced operational load on fraud teams.
Fintech Lending – Synthetic Identity Detection
Fintech lenders deal with high volumes and thin data. One client used agents to catch applications that:
- Used slightly altered identity data (e.g., different DOB or SSNs with matching addresses)
- Applied to multiple loan products in rapid sequence
- Reused documents across supposedly unrelated accounts
The agent connected the dots and auto-rejected risky applicants before credit was issued.
Known Challenges And Risks
No system is perfect. There are trade-offs to consider:
- Explainability: Deep-learning agents can make decisions that are hard to justify without traceable logic. This is a concern for regulated industries.
- Bias: If agents are trained on biased data, they may reinforce discrimination (e.g., falsely flagging users based on geography or demographic patterns).
- Overreach: Agents acting too aggressively (e.g., false account freezes) can damage user trust and create compliance risks.
To mitigate these, agents should be built with human-in-the-loop oversight, audit trails, and risk thresholds that define when automation is allowed to act independently.
What To Expect Going Forward
Autonomous AI agents will evolve beyond single-use cases. We’re seeing early adoption of multi-agent systems, where:
- One agent focuses on transaction-level fraud
- Another monitors identity risk over time
- A third handles response orchestration and user communication
This layered approach builds resilience and adaptability.
We’re also likely to see closer integration with identity verification, KYC, behavioral biometrics, and external fraud intelligence feeds.
Conclusion
Autonomous agents are not silver bullets. But they are a necessary shift. As fraud tactics grow more automated, detection and prevention must move at the same pace.
The true value of these agents isn’t just speed—it’s consistency, scalability, and reduced human burden. In areas like fintech, e-commerce, and insurance, the business case is already clear.
Want to use AI in your business?
Get in touch with the Adanto Software Team today to see how we can help.
Thank you for the design plan of Fujifilm eCommerce Plug-ins integration of our Simple Ordering Platform (SOP) with Shopify and MediaClip that adds a product builder functionality in the Shopping Cart, then submit it via SPA API.
Alvin Scott
Senior Software Product Manager
Fujifilm NA Corporation, Imaging Division
Adanto Boosts Service & Cuts Costs with Big Data Analytics
Adanto’s Real-Time Big Data Analytics solution streamlined operations for a global HR leader, cutting abandoned calls from 18% to 5%, reducing hold times to 4 minutes, and enhancing real-time insights with advanced reporting and dashboards.
Thank you for your expertise and fast, quality delivery
Mike Perry
VP, Software Development
Ria Financial
I have had the pleasure of collaborating with Mike on several projects as a client during my career. Adanto stands out as a premier IT professional software services and solutions company. I can attest to their dedication to delivering innovative and robust solutions tailored to the unique needs of their clients. The software development teams at Adanto are not only technically proficient but also deeply committed to achieving the best outcomes for their clients. They are incredibly fun and engaging to work with. Their proactiveness and adaptability helps make Adanto a trusted business advisor and innovation partner.
Hans Bakker
Director of Web Development
Circle Graphics/Sensaria
Thank you for putting together with Alvin the architecture, and plan to make it easy for potential customers who are using Shopify for their eCommerce platform to use Fujifilm’s personalization engine by creating a plug-in/extension.
Jim Dolce
Vice President New Business and Software Development
Fujifilm NA Corporation, Imaging Division
Adanto has helped Robert Half and Protivity accelerate our services deliverry and lower our development costs.
James Johnson
VP of IT
Robert Half
Imagine a mid-sized bank overwhelmed with millions of transactions, thousands of loan applications, and tens of thousands of customer queries every day — all demanding lightning-fast decisions and razor-sharp accuracy. The stakes couldn’t be higher: a single missed fraud alert or a delayed credit decision could cost millions or destroy client trust overnight.
This is the reality fintech faces, and it’s why Agentic AI has emerged as a game-changer. Today, financial institutions wield AI not only to automate routine tasks but to drive complex decisions, personalize client experiences at scale, and navigate turbulent markets in real time.
Table of Contents
Why Agentic AI Matters in Financial Services — By the Numbers
According to recent studies, AI adoption in financial services has grown by over 70% in the last three years, with the global AI in fintech market projected to reach $26 billion by 2027, growing at a CAGR of 23%. Firms investing in advanced AI systems report a 30-40% improvement in operational efficiency and significantly faster turnaround times on credit decisions, compliance checks, and customer service.
These statistics represent measurable impact across the financial ecosystem, from retail banking to asset management:
- 70% of financial firms have integrated AI into at least one business function, primarily fraud detection and customer service (Source: Deloitte).
- AI-driven credit assessments reduce loan default rates by up to 25% through better risk profiling (Source: McKinsey).
- Automated transaction monitoring powered by AI can detect suspicious activity with 95% accuracy, cutting false positives by 50% (Source: Accenture).
- Firms using AI for customer engagement report up to 20% increase in client retention thanks to personalized financial coaching and real-time advice (Source: PwC).
The financial industry saves an estimated $447 billion annually through AI-enabled automation in compliance and operational tasks (Source: Business Insider Intelligence).
Use Cases of Agentic AI in Financial Services
Enhancing Customer Interaction
In a sector where customer trust and engagement are paramount, Agentic AI transforms how firms interact with their clients.
- Client Engagement: Agentic AI automates personalized financial planning tools, creating tailored app interfaces that adjust dynamically to individual client profiles and needs.
- Relationship Management: By optimizing communication channels and tailoring engagement strategies, financial institutions can maintain stronger client relationships and increase satisfaction.
- Personal Financial Advisory: Real-time coaching and financial advice based on clients’ spending patterns enable more proactive and relevant financial guidance.
This not only improves client experiences but also drives loyalty and retention.
Innovating Product and Pricing Strategies
Agentic AI enables smarter product offerings and dynamic pricing models, giving firms an edge in competitive markets.
- Credit Assessment & Loan Origination: AI evaluates creditworthiness with higher precision, customizes loan offers, and autonomously manages portfolios with high-risk profiles, reducing defaults and improving underwriting efficiency.
- Dynamic Pricing: Pricing models adjust in real time according to client behavior, market conditions, and risk factors, optimizing retention offers and revenue.
Such adaptability results in more competitive, customer-centric products.
Strengthening Compliance and Fraud Prevention
Regulatory compliance and fraud detection are among the most complex and critical challenges for financial firms. Agentic AI excels in these areas by continuously monitoring and reacting to emerging threats.
- Transaction Monitoring: Detects anti-money laundering (AML) risks by flagging suspicious transactions and dynamically intervening before damage occurs.
- Claims and Underwriting: Automates triaging processes and refines risk models to improve accuracy and speed.
- Financial Risk Surveillance: Tracks market threats in real time and recommends mitigation strategies.
- Software Compliance Testing: Identifies bugs, ensures regulatory compliance, and deploys seamless updates.
- Process Automation and Quality: Triages complaints efficiently and detects operational anomalies to maintain service excellence.
By automating these critical processes, Agentic AI helps reduce operational costs and mitigate legal and reputational risks.
Driving Market Intelligence and Competitive Advantage
Agentic AI empowers financial institutions with deeper market insights and strategic agility.
- Competitive Market Analysis: Continuously tracks competitor strategies and uncovers actionable insights for tactical decision-making.
- Market Trend Surveillance: Monitors shifts in the financial landscape, alerting analysts to emerging risks or opportunities early.
This intelligence supports proactive strategy development and faster response to market changes.
Conclusion: Why Financial Firms Can’t Afford to Ignore Agentic AI
Financial services face increasing pressure from tighter regulations, rising customer expectations, and intense competition. Agentic AI is proving to be a vital tool that drives faster decisions, improves risk management, and personalizes customer experiences at scale.
The data is clear: firms using Agentic AI gain real advantages in efficiency and retention. Those that hesitate risk falling behind more agile competitors.
Want to use AI in your business?
Get in touch with the Adanto Software Team today to see how we can help.
The Adanto team was one of the first groups of developers I started working with at Sensaria and really one of the constants during my time here. Thank you for everything over the years – most notably your flexibility and teamwork with the on-shore team, and teaching us some key Polish terms along the way. I’m happy to say that the concept of “Little Friday” has spread around Sensaria!
Polly Eron (Tobias)
Technical Project Manager
Circle Graphics/Sensaria
Inventory management is a core challenge for businesses across industries. Holding too much stock ties up capital and storage space. Too little stock risks lost sales and unhappy customers. Striking the right balance requires insight into demand patterns, supply chains, and operational constraints.
Traditional methods often rely on static rules or human judgment, which can struggle to keep up with fast-changing markets. That’s where self-learning AI agents come into play. These systems continuously observe data, adjust strategies, and make decisions that improve inventory management over time — without needing constant manual tuning.
This article explores how self-learning AI agents enhance inventory optimization, reduce costs, and improve service levels. We’ll walk through their key benefits, how they operate, and what businesses should consider when adopting them.
Table of Contents
The Inventory Challenge
Inventory optimization is about balancing supply and demand. Companies must forecast future demand, manage lead times, and respond to disruptions like supplier delays or sudden spikes in sales. Inaccurate forecasts or rigid inventory policies lead to overstock, stockouts, or obsolete inventory.
According to a survey by Gartner, companies typically spend 20-30% of their operating costs on inventory. Inefficiencies can easily add millions of dollars in unnecessary expenses. At the same time, 43% of retailers report frequent stockouts, which damage customer loyalty and sales.
The complexity and volume of data make manual approaches less effective. Businesses need tools that can quickly learn from new information and adjust inventory decisions accordingly.
How Self-Learning AI Agents Work
Self-learning AI agents monitor a wide range of data points: historical sales, current inventory levels, supplier performance, market trends, and even external factors like weather or promotions. Using algorithms inspired by reinforcement learning, these agents test different inventory policies and learn which actions yield better outcomes.
Over time, the agents improve their predictions and decision-making by constantly evaluating the results of their choices. This means they adapt to changes in demand patterns or supply chain disruptions without requiring manual reprogramming.
Unlike traditional systems that rely on fixed rules, self-learning agents evolve. They can optimize reorder points, quantities, and timing, balancing costs with service levels dynamically.
Key Benefits of AI-Driven Inventory Optimization
- Reduced Holding Costs: By minimizing excess stock, businesses free up capital and reduce storage expenses.
- Lower Stockouts: Adaptive inventory policies help maintain service levels, reducing lost sales and backorders.
- Improved Forecast Accuracy: Continuous learning refines demand predictions, even with volatile market conditions.
- Faster Response to Disruptions: AI agents detect and react to supply chain changes more quickly than manual processes.
Scalability: AI systems handle large, complex product portfolios without additional human effort.
Considerations for Implementation
Introducing self-learning AI agents requires data readiness and clear objectives. Companies should:
- Ensure quality, consistent data from sales, inventory, and suppliers.
- Define KPIs like inventory turnover, fill rate, or cost targets.
- Plan integration with existing ERP or supply chain systems.
- Start with pilot projects before scaling.
- Maintain human oversight to validate AI recommendations, especially early on.
Adopting these agents is not a plug-and-play solution. It takes time, expertise, and alignment across teams.
Conclusion
Inventory optimization remains a critical business challenge. Self-learning AI agents offer a practical approach to managing inventory more efficiently by adapting to changing conditions without manual intervention. They help reduce costs, improve service levels, and handle complex data at scale.
Companies willing to invest in data quality and thoughtful implementation can benefit from more agile and accurate inventory management. As markets grow more dynamic, these AI-driven systems become increasingly valuable tools.
Want to use AI in your business?
If your business is looking to improve inventory management, consider exploring AI-driven solutions tailored to your operations. Reach out to Adanto Software for insights on integrating self-learning AI agents into your supply chain.
Glad that Adanto could get us started with IAM Automation Tool and Security metrics for our CISO and my Information Security Services Organization.
Jason Zirkelbach
Sr. Director - Enterprise Information Security
Robert Half
“The Adanto team was of the first groups of developers I started working with at Sensaria and really one of the constants during my time here. Thank you for everything over the years – most notably your flexibility and teamwork with the on-shore team, and teaching us key Polish terms along the way. I’m happy to say that the concept of “Little Friday” has spread around Sensaria!”
Polly Tobias
Technical Project Manager
Circle Graphics
Key Results
72%
Reduction of the abandoned calls (from 18% to 5%), translating to thousands of retained customer interactions annually
73%
Decrease in average call hold time (from 15 to 4 min), significantly improving customer experience and reducing costs
20-30%
Estimated annual savings in operating costs due to reduced manual reporting, improved SLA
Technologies used
- ETL and Data Integration:
- Pentaho Data Integrator (ETL tool)
- MySQL (for data warehousing)
- APIs and Real-Time Data Access:
- REST APIs (for real-time data access and updates)
- Cloud Infrastructure:
- Amazon AWS (cloud storage and processing)
- Microsoft Azure (cloud storage and analytics platform)
- Reporting and Dashboards:
- Microsoft Power BI (for advanced reporting and interactive dashboards)
- Machine Learning and Analytics:
- Custom machine learning algorithms (for data correlation and advanced analytics)
- Source Systems for Data Extraction:
- ShoreTel (PBX business phone systems)
- CIC (Customer Interaction Center)
- ServiceNow Cloud (service management system)
Challenge
Environment: A complex global operation with 15 contact support centers across North America, EMEA, and APAC regions, handling 2.5 million monthly events across 30 disparate databases, using systems like ShoreTel, CIC, and ServiceNow Cloud.
Challenge: Manual reporting, data silos, and lack of real-time insights led to poor SLA performance, delayed issue resolution, high operating costs, and customer dissatisfaction.
Key goals
Enable real-time analysis of caller behavior for management decision-making
Provide accurate, real-time reports for call center management and stakeholders
Deliver predefined executive dashboards accessible via cloud on mobile and desktop
Streamline operations by integrating data from multiple systems and improving reporting accuracy
Solution
Adanto implemented a powerful real-time Big Data Analytics solution to transform a global HR leader’s contact center operations. By integrating advanced technologies, cloud platforms, and machine learning, Adanto streamlined a complex, siloed environment into a real-time analytics system:
- Data Integration: Adanto centralized data from 30 disparate databases using Pentaho ETL and MySQL, integrating sources like ShoreTel, CIC, and ServiceNow
- Real-Time Access: REST APIs enabled continuous data updates, providing stakeholders with up-to-date insights for decision-making.
- Machine Learning: Advanced algorithms correlated data across systems, achieving 89% accuracy by using time as a common key.
- Cloud Infrastructure: AWS and Azure ensured scalable, secure storage and accessibility for global users.
Reporting and - Dashboards: Microsoft Power BI delivered real-time dashboards and reports, enabling data-driven decisions from any devices.
- Optimization: Custom tools enhanced data collection and accuracy, resolving inefficiencies and boosting operational performance.
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Customer support is changing. For years, businesses relied on scripts and predefined workflows to handle conversations. It worked—up to a point.
Most support interactions still start the same way.
A customer runs into a problem. They reach out for help. And what do they get?
A chatbot that repeats their question. A phone system that loops them around. A support agent stuck reading from a script.
The customer gets frustrated. The agent feels stuck. Nobody wins.
This isn’t how support should work in 2025. And thanks to Agentic AI, it doesn’t have to.
Table of Contents
Why Scripted Support Falls Short
Think about the last time you contacted support. You probably had one clear goal—get something fixed.
But instead, you got a list of steps that didn’t match your issue. Or had to repeat yourself three times. Or got passed between three people who all asked for the same details.
That’s what scripted systems do. They assume every problem is simple. They treat every customer the same.
But real problems are messy. Customers don’t follow scripts. So why should support?
What Agentic AI Does Better
Agentic AI doesn’t follow a script. It follows a goal.
Instead of matching inputs to preset replies, it looks at the bigger picture. It can ask questions, gather missing info, make decisions, and even take action—like updating an account or sending a follow-up email.
It can handle back-and-forth without losing context. It remembers what the customer said earlier. And it can change its approach if the situation shifts.
This makes the conversation feel more natural. And it gets things done faster.
Real-World Use Cases
Here’s how companies are already using Agentic AI in support:
- In e-commerce, Agentic AI is handling returns, tracking packages, and even flagging repeat fraud attempts.
- In fintech, it’s guiding users through document verification, clarifying account rules, or escalating flagged transactions.
- In travel, it’s helping passengers rebook flights, offer options, and reissue tickets—all while dealing with weather delays.
- In retail, it’s solving problems before they reach a live agent, or freeing up agents to focus on escalations.
These aren’t just FAQs. They’re real tasks that usually need human input. Agentic AI can now handle many of them—end to end.
What This Means for Support Teams
Agentic AI isn’t here to replace support teams. It’s here to take care of the boring stuff.
Agents don’t need to answer the same password-reset question 100 times a day. They don’t need to copy-paste policy links. Or route simple issues to other teams.
Instead, they can focus on what matters—complex cases, sensitive topics, or customers who really need a human touch.
Support roles will shift. But they won’t disappear. Teams will need new skills, like prompt design, oversight, and exception handling.
Conclusion
Scripted support had its time. But it’s no longer enough.
Customers want faster, smarter, more flexible help. Agentic AI can deliver that. It works with goals, not rigid flows. It handles complexity better. And it gets closer to how people actually talk.
We’re moving toward a support system that’s more intelligent, more efficient, and less frustrating.
Adanto Software helps businesses design and build intelligent support systems using real Agentic AI.
Adanto software team with Piotr really rocked.
Scott Francis
Sr. Director Applications
Robert Half
Imagine walking into a supermarket where every aisle seems perfectly stocked with products you want. No empty shelves, no clutter of unpopular items. Behind this seamless experience is a complex decision-making process about what products to offer and how much space they deserve. For retailers, these decisions are tough and often based on guesswork or outdated information. But AI agents are changing that. By analyzing large amounts of data and continuously learning, these tools optimize product mix and shelf space in ways that were impossible before. This article explores how AI helps retailers make smarter choices, reduce waste, and meet customer demand more effectively.
Table of Contents
The Challenge of Merchandising
Retailers typically carry thousands of products. Each SKU competes for shelf space, which is a limited and costly resource. According to a Nielsen study, retailers lose nearly 10-15% of sales due to out-of-stock items or poor shelf placement. Meanwhile, excess inventory ties up capital and increases waste, especially for perishable goods. Traditionally, store managers use sales history and manual adjustments to plan product displays. But consumer preferences shift quickly, and competitors constantly change their offerings. This often results in overstocking slow movers or missing out on fast sellers, impacting both revenue and customer satisfaction.
How AI Agents Support Product Mix Decisions
AI agents dig deeper than basic sales reports. They analyze customer buying patterns, seasonal trends, promotional impacts, and even social media buzz. For example, an AI agent might detect an emerging trend for plant-based snacks before traditional methods catch on. These agents test various product combinations virtually, learning which mix drives the highest sales and margin. Retailers who use AI for product assortment have reported up to a 20% increase in sales and a 15% reduction in inventory costs. AI’s continuous learning means it adapts when new products arrive or when consumer habits shift, helping stores stay aligned with current demand.
Shelf Space Optimization Explained
Shelf space is a valuable asset that directly influences sales. Research by the POPAI Group found that 72% of purchase decisions happen in-store, making shelf placement crucial. AI agents recommend space allocation by weighing factors like product size, profitability, turnover speed, and customer preference.
For example, a fast-selling premium coffee brand may get more shelf space than a slow-moving generic. The AI can also adjust layouts quickly during promotional campaigns or new product launches. This flexibility reduces lost sales from poor product placement and improves overall store profitability.
Benefits for Retailers and Consumers
For retailers, the benefits are clear. AI reduces manual effort and guesswork, lowers inventory holding costs, and improves sales efficiency. Staff can focus on customer engagement and store experience rather than spreadsheet crunching. For consumers, this means shopping in stores that are better stocked and easier to navigate. They find the products they want more consistently, reducing frustration and improving satisfaction. Ultimately, smart merchandising creates a smoother shopping experience and a healthier bottom line.
Conclusion
Managing product mix and shelf space is difficult but essential for retail success. AI agents offer a smarter way to handle these challenges. They use data to make faster, more accurate decisions that keep stores stocked with the right products. This leads to better sales, lower costs, and happier customers. As retail becomes more competitive, adopting AI-based merchandising tools will help businesses stay ahead.
Want to use AI in your business?
Get in touch with the Adanto Software Team today to see how we can help.
You were awarded a contract based on your ability to deliver unmatched technical innovation skills, solid track record of stable and predictable results, offered via progressive people and results-oriented culture.
Brett Roscoe
GM
Dell Software
Global sales force App powered by the data science algorithm
Adanto improves the productivity of sales and marketing teams at the global leader in the Professional Staffing global leader by delivering targeted realtime leads via AI/NLP powered enterprise application to their mobile or PC device of choice.
Adanto has helped us be more productive and monitor costs of an AWS cloud
Dan Powers
Director, IT Shared Services
Robert Half
Adanto was pivotal in getting our iTrack Reporting Workstream Project on track and successful. Sheila Santana, VP, IT
Sheila Santana
VP, IT Field Services
Robert Half
We have been continually reassured of Adanto’s versatile portfolio of expertise while tasked with deploying a major multi-national pharmaceutical company’s Secure Transit VPC across multiple geographic regions spanning two continents. Adanto’s ability to work with geographically dispersed teams and deliver on the customer’s terms in multiple timezones is a true differentiator few engineering services company’s can offer.
Francesco Alongi
Senior Manager Cloud Strategy
Advanced Informatics and Analytics
Astellas Pharma
Adanto has helped us in our first phase of creating DataLake and gathering data in centralised location
Sean Perry
CIO
Robert Half
Finding the right product online can be frustrating. Search results often miss the mark. Filters don’t help much unless you already know what to look for. And recommendation engines mostly rely on what you or others bought in the past.
What if online shopping felt more like talking to someone who actually helps? Not a chatbot with canned replies, but an intelligent system that understands what you need—even if you don’t know how to ask for it.
That’s the promise of AI agents. They’re changing how product discovery works by guiding users like real assistants.
Table of Contents
What Are AI Agents?
AI agents are not just tools that respond to commands. They act more like helpers that can make decisions, ask questions, and carry out tasks.
In e-commerce, AI agents help customers discover the right products. They don’t just throw recommendations based on past clicks. They listen, ask follow-up questions, and adapt their suggestions in real-time.
Example: A shopper says, “I need a laptop for school.” Instead of showing 100+ random laptops, the AI agent asks, “Do you prefer something lightweight? Any specific software you’ll be using?” Based on that, it narrows down the list to options that actually fit the user’s needs.
How Product Discovery Works Today
Most online stores use filters, search bars, and static recommendation engines.
You type in a keyword like “running shoes,” and get hundreds of results. You try filtering by brand, price, or size—but the process is often slow and confusing.
Traditional recommendation systems use browsing or purchase history. If you bought hiking boots last month, you might see more boots—even if you’re now shopping for sandals.
This leads to a poor experience. People scroll, compare, get overwhelmed, and sometimes give up without buying anything.
Why Traditional Tools Miss the Mark
These older systems assume too much. They expect users to know what they want and how to ask for it in the “right” way. But most people shop with vague goals.
Let’s say someone wants a gift for a tech-savvy friend. They may not know whether to search for smartwatches, headphones, or accessories. A keyword search won’t help much.
Filters also fall short when needs are complex.
For example, “eco-friendly office chair for a small home office” doesn’t map well to standard filters like “material” or “brand.”
Recommendation engines rely heavily on data from past users. But past behavior doesn’t always predict future intent—especially for new or infrequent buyers.
What Makes AI Agents Different
AI agents act more like smart assistants than tools. They combine search, filtering, comparison, and conversation in one experience.
They can:
- Understand natural language questions
- Ask follow-ups to clarify vague inputs
- Compare multiple products based on specific needs
- Adjust results in real time
Example: A shopper says, “I need a laptop for school.” Instead of showing 100+ random laptops, the AI agent asks, “Do you prefer something lightweight? Any specific software you’ll be using?” Based on that, it narrows down the list to options that actually fit the user’s needs.
They’re not just filtering—they’re reasoning.
Challenges to Consider
AI agents aren’t perfect. They need accurate and well-structured data to work properly. If your product catalog is outdated or lacks key details (like noise levels or fabric types), the agent can’t make smart choices.
They also need to respect privacy. Over-personalization can feel invasive.
Not everyone wants a “conversation” when shopping. Some users prefer quick browsing. That’s why AI agents should be optional and easy to exit or skip.
What This Means for Online Retailers
AI agents can improve the shopping experience, but they’re not plug-and-play.
To get them right, retailers need to:
- Understand their customer journey
- Structure their product data well
- Choose the right AI platform or partner
- Test with real users
Done right, AI agents can lower bounce rates, increase conversions, and reduce returns. They help people find what they’re actually looking for.
Conclusion
Product discovery shouldn’t feel like work. AI agents make it easier for people to find what fits their needs—even if they don’t know how to say it.
They’re more than search tools. They’re decision helpers.
And they’re already starting to change how people shop online.
Adanto builds custom AI-driven solutions that improve search, discovery, and customer satisfaction.
Schedule a short call to explore what’s possible.
Thank you for your help in migration to the Dell infrastructure.
Richard Leurig
SVP, GM Innovation & Technology
Core Logic
Really happy with Adanto’s work and your engineering capabilities in the C#/.Net back end development of our LSX platform. Our team has voted very high marks and would like to keep utilizing your services.
John Crowley
Chief Software Architect
Fujifilm NA Corporation, Imaging Division
Adanto delivers Mazoola digital e-wallet platform
Adanto Mazoola – the only for kids and families. This , , and robust platform delivers exceptional , , and you can trust unfailingly.
I want to thank the entire Adanto team for all your efforts and help with ITMCC and Robert Half. I want to thank the team for your efforts on the project, providing the resources so quickly and being so flexible and nimble during the development cycle. Adanto is our go to partner for our new initiatives in our Marketing vertical
Frank Ficken
IT Portfolio Manager
Robert Half
Honeywell Int’l Inc.
Adanto Delivers IoT-Enabled HA Telemetry for Gas Pipelines
Adanto delivered an IoT-enabled HA telemetry solution for gas pipelines, ensuring 99.9% uptime, real-time monitoring, and auto-healing. Operational for over eight years, it provides reliable, scalable, and environmentally friendly gas transportation.
Key Results
$2.7M
Annual savings through reduced maintenance, downtime, & optimized energy usage
$1.5M
Annual costs savings from incidents prevention & ensured compliance
$2.1M
Annual revenue growth from more client value of increased throughput & better utilization
Technologies used
- Infrastructure:
- IBM Power 6 servers running IBM AIX operating system
- Power VM for load separation
- Power HA for Telemetry System HA
- Oracle RAC for database HA
- Moxa Industrial Ethernet switches
- CISCO Catalyst switches and routers
- Brocade SAN switches
- IBM Disk Storage & Tape Storage: 2 tape drives and 48 LTO-3 tapes.
- Tivoli Storage Manager
- Performance:
- 99.9% system availability & 500ms data sampling density
- Full redundancy of hardware and software components
- 6 months data retention for raw data
- 3-year data retention for data reporting
- RTO < 8h & RPO < 48h
- Bare metal recovery
- >3TB of telemetry data
- >1000 data samples read per second
- Max 40ms recovery time after network topology change
Challenge
Our oil and gas pipeline client faced challenges operating in remote, inaccessible terrain, requiring real-time monitoring of gas flow. Manual oversight was impractical, and the client needed a highly available, IoT-enabled solution to process large volumes of telemetry data, ensure reliability, minimize maintenance, and maintain safety and regulatory compliance.
Key goals
Real-Time Monitoring: Ensure continuous, real-time tracking of natural gas flow metrics for safety and efficiency
High Availability: Deliver a 99.9% uptime system with auto-healing and redundancy to prevent disruptions
Scalability: Handle high-density telemetry data with scalable infrastructure and long-term data retention
Low Maintenance: Provide a self-sustaining solution with automated updates for remote, inaccessible locations
Solution
Adanto delivered an IoT-enabled high-availability telemetry solution for real-time monitoring and management of natural gas pipelines in remote and challenging terrains. This solution provided safe, reliable, and efficient operations with minimal manual intervention, meeting the client’s goals for scalability, reliability, and environmental compliance. The solution included:
- High-Availability Clusters: Designed for 99.9% uptime, ensuring continuous monitoring and operational reliability
- IoT-Enabled Sensors and Systems: Integrated real-time data collection and processing of over 1,000 telemetry data samples per second.
- Robust Infrastructure: Leveraged IBM Power servers, Oracle RAC databases, industrial-grade networking equipment, and cloud storage for scalability and performance.
- Automation and Resilience: Implemented auto-healing features for system updates, firmware upgrades, and software patches with minimal maintenance.
- Secure Data Retention: Ensured long-term storage of telemetry data (6 months for raw data, 3 years for reporting) and rapid recovery capabilities.
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Key Results
5 weeks
Integration time it took for the first Mazoola’s Payment Platform
21 services
Total number of best of breed financial services Integrated with Mazoola
+466%
REGO’s stock value increase (as of Dec’2024) since Adanto’s first release of its first product version on the market
Technologies used
- Cloud Platform hosting – Azure
- iOS mobile app written in Swift
- Android mobile app written in Java
- Frontend services – React Native
- Backend services written in C#/.NET
- Microsoft SQL Server
- Web app written in Node.js
- Password vault – Dashlane
- DevOps tools – Azure Repos, Boards, Pipelines
- Penetration & Performance tests – Jasmine
Challenge
The challenge faced by Rego Payments in 2020 centered around the evolving regulatory landscape, technological demands, and the increasing emphasis on data privacy, particularly concerning children and families.
Key goals
Develop a cloud-based digital wallet that could meet high standards and integrating it with financial systems
Build a highly scalable, cloud-native architecture using advanced security measures and AI-driven fraud detection to support seamless and safe user experiences.
Enable Seamless Digital Payments to support online and in-store purchases via virtual cards, merchant category filtering, and integrations with popular platforms like Apple Pay.
Develop API integrations to expand functionality and interoperability with third-party financial tools and platforms and create a modular framework that allows for future expansion, including partnerships with educational or banking services.
Solution
Mazoola’s technical solution represents a sophisticated blend of fintech innovation and strict regulatory adherence, offering a safe, scalable, and family-friendly digital wallet experience featuring:
- Cloud-Native Architecture with regional redundancy, global scalability, performance, multi-region deployment with low latency and high throughput to handle large volumes of transactions.
- Secure Wallet Infrastructure with tokenized virtual cards support for online and in-store purchases, real-time fraud detection, and multi-layer authentication
- Seamless Payment Integration compatible with major payment networks
- Extensibility that exposes a suite of RESTful APIs for integration with best-of breed third-party platforms, financial services or additional tools.
- Strictest Regulatory COPPA and GDPR Compliance and Privacy, anonymized data handling of children’s PII and end-to-end encryption
- Parental Control and User Management with role-based access with distinct permissions , and real-time transaction monitoring.
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In my continuing work at F/22 Consulting I engage numerous companies that all face the ever-present and escalating challenge of developing and managing software projects in order to remain competitive. Not only did Adanto graciously and professionally rise to every challenge (even the unreasonable!) but they always completed the projects on time and exceeded expectations unfailingly. I am happy to have this resource available to recommend on what I’m sure will be a frequent basis.
Frank Baillargeon
CEO
Iconic Idaho
Adanto implements BI for Maconomy ERP with SAP BO
Adanto delivers advanced financial planning & reporting tools for a global leader in the professional placement services sector. By deploying SAP BusinessObjects Universes, Adanto streamlined access to critical financial data, enabling custom reporting & dash-boarding.
Customer service is evolving faster than ever — and Agentic AI is leading the charge.
If you’ve heard buzz about AI handling customer interactions, here’s the truth: within the next 12 months, more than half of all customer service conversations will be managed by agentic AI systems. These aren’t your typical chatbots; they’re autonomous, proactive, and deeply contextual digital agents that understand your needs, make decisions on the fly, and act — all to deliver a seamless, personalized experience.
Table of Contents
What Is Agentic AI — And Why Should You Care?
Agentic AI takes AI-powered customer service to the next level. Unlike rule-based bots that simply respond to scripted prompts, agentic AI:
- Understands context — remembers past interactions and adapts conversations.
- Acts proactively — reaches out before problems arise or needs are voiced.
- Makes decisions autonomously — guiding customers and supporting agents alike.
Simply put, it’s customer service that thinks and acts smarter — like having a supercharged, empathetic team member available 24/7.
The AI Shift Is Happening — Fast
According to Cisco’s latest research:
- 56% of all customer interactions will be AI-handled within a year.
- 75% of business leaders believe proactive AI support will reduce customer churn.
- 65% expect to boost customer lifetime value through AI-driven insights.
That’s not just technology hype. It’s a strategic transformation reshaping how companies connect with customers — driving loyalty, satisfaction, and revenue.
How Agentic AI Changes Customer Service
Traditional customer support often struggles with:
- Long wait times
- Repetitive questions
- Burned-out agents
- Reactive responses
- Fragmented experiences
Agentic AI flips this script. This means happier customers and empowered agents — a win-win.
Top Agentic AI Use Cases Transforming Customer Service
Here’s where agentic AI really shines:
- Autonomous Customer Support
Instantly handles routine queries, reducing wait times and deflecting up to 60% of tickets. - Contextual Memory
Keeps track of past conversations so agents respond faster and smarter. - Proactive Outreach
Predicts customer needs — like renewals or potential issues — and acts before you even ask. - Real-Time Assistance
Provides live recommendations on next steps, resources, and tone during customer calls or chats.
Sentiment Detection
Reads emotions to support both customers and agent well-being, tailoring responses with empathy.
Agentic AI Augments Humans — It Doesn’t Replace Them
A common misconception is that AI will replace people. The reality is the opposite. Agentic AI acts as a smart copilot — augmenting human agents to:
- Make better decisions
- Work more efficiently
- Deliver richer, personalized experiences
This collaboration means more productive teams and happier customers.
Conclusion
Agentic AI is revolutionizing the way businesses interact with their customers, making service faster, smarter, and more human-centric. By understanding context, acting proactively, and collaborating with human agents, these intelligent systems turn every customer interaction into an opportunity to build loyalty and drive growth.
Companies that embrace agentic AI now will gain a serious competitive edge — delivering seamless experiences, reducing operational costs, and empowering their teams to focus on what truly matters: creating lasting relationships with customers.
Want to use AI in your business?
Get in touch with the Adanto Software Team today to see how we can help.
Adanto delivers multi-function CRM platform in record time
Adanto specializes in delivering powerful CRM platforms for business and national sales teams in utilities, combining rapid deployment with robust frameworks, cloud tech, and seamless integrations—without compromising quality.
Adanto has performed the project on time and to our complete satisfaction. We have achieved our goal of improved visibility in our global call centers and could fix issues much quicker for our internal clients
Eddie Borrero
Chief Information Security Officer
Robert Half
Chetan Ghai, our Chief Product Officer, and I know how fast Adanto has created an application for Robert Half that intergates our patented Quill technology and so we are very convinced how strong your team is.
Mauro Mujica-Parodi III
VP, Product
Narrative Science
Adanto speed in responding to concerns is great. You are really great to work with.” CG ProPrints Team
AJ McDonald
Director, Brand Marketing, Art Division
Circle Graphics/Sensaria
Cart abandonment has been a long-standing problem in e-commerce. No matter how well a store is designed or how good the products are, a large percentage of shoppers still leave without buying. Some estimates put the global cart abandonment rate at nearly 70%. For years, businesses have relied on discount pop-ups, email reminders, and retargeting ads to win customers back. But these methods only scratch the surface.
Now, something is changing. Quietly but steadily, Agentic AI is reshaping how online stores understand and respond to shopper behavior. Instead of reacting after the fact, it works in the moment—proactively assisting, guiding, and even decision-making on behalf of the user. The result? A more responsive, adaptive shopping experience that closes the gap between interest and purchase.
Table of Contents
The Cart Abandonment Problem
Cart abandonment isn’t a mystery. It’s the result of friction—extra steps, unanswered questions, distractions, or doubts. A customer sees something they like. They add it to the cart. Then something gets in the way.
It could be unexpected shipping costs, a slow-loading checkout page, or just a moment of hesitation. And once that moment is lost, the sale usually is too.
Even with retargeting efforts, only a small portion of these shoppers ever return.
Why Traditional Solutions Fall Short
Most e-commerce platforms react to cart abandonment after it happens. They wait until the customer is gone, then try to pull them back with emails, ads, or discounts. These methods depend on timing, guesswork, and sometimes luck.
But they don’t solve the core problem: helping the customer when they are still in the decision-making process.
Also, many of these solutions treat all customers the same. They lack context. They don’t know why a specific person abandoned the cart, so they rely on generic nudges. That’s not enough anymore.
Agentic AI in Action
Agentic AI changes the equation. It doesn’t wait. It watches, learns, and acts during the shopping session—moment by moment.
Imagine a digital assistant that understands the shopper’s intent, not just their clicks. One that can:
- Ask helpful questions
- Offer tailored suggestions
- Resolve confusion about product details
- Highlight delivery options in real-time
- Adjust the checkout flow to reduce friction
It doesn’t just react—it collaborates. And it doesn’t need to escalate to a human unless it’s necessary.
This type of AI can engage in a dialogue with the shopper, anticipate their needs, and even take small decisions off their plate—without being pushy.
Use Cases: From Browsing to Checkout
Let’s say a customer is browsing running shoes. They hover over a size chart but don’t select a size. The system recognizes this as hesitation. Instead of waiting, the AI offers a short interactive fit guide based on their previous purchases or activity.
Or take checkout abandonment. A customer enters a shipping address but pauses at the payment screen. The AI might offer to save their cart, suggest a faster payment method, or even surface a loyalty reward they didn’t know they had.
For larger purchases, the AI can simulate product comparisons or answer detailed questions—without having to leave the page.
These moments are subtle. But when stitched together, they can change the flow of the shopping journey completely.
Real Results: What Businesses Are Seeing
Early adopters are already seeing the impact. One e-commerce brand using agentic flows reported a 22% drop in cart abandonment over a three-month period. Another saw conversion rates rise by 18% after integrating real-time, agent-led product guidance.
The key difference isn’t just better UX—it’s the AI’s ability to act with autonomy. That autonomy creates a more fluid, natural path from interest to purchase.
And when implemented correctly, it doesn’t feel like a chatbot or a sales tool. It feels like part of the store itself.
Risks and What to Watch For
Not all implementations are equal. Poorly trained agents can confuse customers. Overactive ones can be annoying or intrusive. Businesses need to strike a balance between helpfulness and respect for user intent.
There’s also the question of trust. Shoppers need to know who or what they’re interacting with. Clear communication matters. So does transparency in how data is used.
Agentic AI isn’t a magic button. It requires careful design, solid training data, and clear boundaries. But when done well, it works.
Conclusion
Cart abandonment won’t disappear overnight. But the tools to reduce it are evolving fast. Agentic AI is no longer a concept—it’s being deployed now in ways that directly impact conversion, customer experience, and long-term loyalty.
It works by staying present. By responding in real-time. And by helping people make decisions—not pressuring them to.
For e-commerce companies, this is a moment to rethink how the buying experience should feel—not just how it performs.
Start Exploring Agentic AI for E-Commerce
If you’re ready to explore how Agentic AI can improve your e-commerce performance, reach out to the Adanto team. We help businesses design, build, and integrate agentic solutions that fit their goals and workflows.
We absolutely appreciate all of Adanto’s help in getting Outside Financial off the ground
Sonia Stainway
CEO
Outside Financial
Adanto has done a nearly impossible task of replacing the previous bankig-as-a-service provider with a new one SYNAPSE in just five weeks; for a platform they had never seen before. Adanto pulled it off
Mark Vanderbeek
CTO
Rego Payment Architectures, Inc.
The team presented a solid strategy with beacons for our mobile application”\
Glen Wilson
Director of Engineering
American Express, Credit Cards
Adanto has added great value to Brett, our CTO, and his team to get us off the ground in eCommerce
Andrew Cousin
CEO
Circle Graphics/Sensaria
Key Results
+100%
Sales growth in 24 months after release of new CRM platform
+4%
Sales growth in 12 months prior to releasing new CRM platform
12 months
Time it took Adanto from understanding client needs to a launch of its first release.
Technologies used
- Back-end developed in C#/ASP.NET Core, storing its data in a RDB PostgreSQL.
- UI is a JavaScript SPA (Single Page Application) based on React, Angular & Vue.
- The system is deployed using CI/CD pipelines to the Cloud (AWS & Azure) and rely on managed cloud services.
- The Infrastructure as Code approach is used, automating the setup of required cloud resources.
- Service layer implementation is deployed using REST and JSON
Challenge
The myUtilities company faced many challenges, before approaching Adanto. They were challenged with fragmented customer management, billing inefficiencies, low customer engagement, and outdated and faulty systems based on old technology that hindered scalability and operational workflows. Additionally, they struggled with integrating modern tools like phone, phone messaging, text messaging, poor and confusing user experience which caused dissatisfaction with sales teams that translated in very lackluster sales.
Key goals
Customizable multi-Tenancy and Integrations that support licensee-specific configurations, commission structures, for insurance, energy providers, and phone systems (e.g., RingCentral, Cisco).
Enhanced User Experience and workflow with redesigned front-end for streamlined lead-to-sale workflows, role-based customizable dashboards, and auto-generated activity-based tags for account status visibility.
Advanced Reporting and Automation w/near real-time reporting integrated with PowerBI/Tableau and a flexible commission engine with API extract capabilities. Automated marketing campaigns with event triggers for customer engagement
Robust Modern Cloud Architecture and Security with built-in disaster recovery (BC/DR) capabilities with periodic testing and architecture aligned with SOC2 Type 2 cybersecurity standards for scalability and compliance.
Solution
- Multi-Tenancy: Supports feature and functionality configuration for different licensees, along with tailored commission structures and integrations.
- Workflow Optimization: Offers a redesigned, logical flow from lead management to sales and processing, with automation and real-time status updates.
- Scalable and Secure: Built on a robust architecture with disaster recovery (BC/DR) capabilities, compliance with SOC2 Type 2 standards, and integration options for APIs and third-party systems.
- Advanced Analytics and Engagement: Incorporates near real-time reporting, marketing automation, and tools for improved customer engagement and retention.
Key Features
The myUtilities Multi-Function CRM Solution is a comprehensive, customizable platform designed to streamline customer relationship management, enhance operational efficiency, and integrate seamlessly with utility services and partner ecosystems. It provides tools for multi-tenancy, role-specific dashboards, automated workflows, and advanced reporting, enabling businesses to manage customer data, billing, and communications effectively.
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Agentic AI is no longer theoretical. It’s here, and it’s already reshaping how organizations approach automation, decision-making, and operational efficiency.
Unlike traditional AI models, Agentic AI systems act with autonomy. They don’t just provide predictions or outputs—they perceive their environment, plan actions, make decisions, and interact with other systems to achieve a goal. This makes them especially valuable in complex business environments where dynamic adaptation is required.
But the question most enterprise leaders ask is: “Where do we even begin?”
This guide lays out a clear, practical framework for adopting Agentic AI at the enterprise level.
Table of Contents
What is Agentic AI?
Agentic AI refers to intelligent software systems—known as AI agents—that operate with a degree of autonomy. These agents can:
- Observe an environment or data stream
- Make sense of the information (reasoning)
- Decide on a course of action
- Execute that action
- Learn from the outcome to improve over time
These agents may operate alone or as part of a multi-agent system, where multiple agents collaborate or specialize in sub-tasks.
Agentic AI differs from traditional ML models because it doesn’t just offer insight—it acts. And in enterprise environments, action is often where the value lies.
Why It Matters for Enterprise Systems
Most enterprises already use AI in some form—predictive models, recommendation engines, or RPA (robotic process automation). But these solutions are:
- Static: Models predict, but don’t act
- Siloed: They live inside one system, not across workflows
- Rule-bound: Automation follows fixed logic, which breaks under change
Agentic AI overcomes this by creating systems that think and act, not just compute. This enables:
- Faster decision-making without human bottlenecks
- Workflow orchestration across platforms
- Adaptation to changing business conditions
- Reduction in manual exception handling
Use Cases with High ROI Potential
Agentic AI is best applied where there’s a need for judgment, coordination, or autonomy. Here are proven high-ROI use cases:
Fraud Detection and Response
Autonomous agents can monitor transactions, detect anomalies, take preventive actions (e.g., block payments), and notify users—within milliseconds.
Customer Service Automation
Multi-agent systems can manage tickets, escalate intelligently, handle sentiment-based routing, and even generate personalized follow-ups.
IT and Cloud Ops
Agents can auto-resolve known issues, allocate resources, monitor performance, and coordinate across infrastructure tools like Datadog, AWS, or Azure.
Financial Reconciliation and Audit
Agents validate transactions, cross-check records, and flag inconsistencies across multiple financial systems.
Procurement & Vendor Onboarding
AI agents handle document collection, background checks, compliance scoring, and auto-approval routing.
Readiness Checklist: Before You Start
Ask these questions first:
- Do you have high-quality, accessible data across your systems?
- Are key business workflows well-defined and documented?
- Do you already use automation or AI tools (e.g., ML models, RPA)?
- Is your IT architecture API-friendly and modular?
- Do you have stakeholder alignment on AI governance and ethics?
If the answer is “no” to several of these, it’s wise to start with AI strategy consulting or data infrastructure improvements before diving into agent development.
Step-by-Step Adoption Roadmap
Step 1: Identify a target use case
Start with a narrow, high-impact task. Example: automated KYC document verification or refund fraud detection. Ensure it has clear KPIs.
Step 2: Build a proof of concept (PoC)
Use open-source frameworks (e.g., LangChain, AutoGen) or a managed platform. Integrate with internal systems via APIs or test data.
Step 3: Define agent architecture
Decide on:
- Observation method (event stream, polling, direct input)
- Reasoning approach (rules, ML, hybrid)
- Action interface (REST APIs, system hooks, human handoffs)
- Memory (short-term vs. long-term context)
Step 4: Pilot in a limited environment
Run the agent in a sandbox. Track metrics like response time, error rate, decision quality, and human override frequency.
Step 5: Expand and integrate
Move the agent into production environments with safeguards. Connect to other agents or business systems. Monitor continuously.
Technical Architecture Overview
A typical agentic AI system includes:
- Environment: The system or data space the agent interacts with
- Perception Layer: Ingests data (via logs, APIs, streams)
- Reasoning Engine: Logic, ML models, or fine-tuned LLMs
- Action Layer: Interfaces for executing tasks
- Memory Store: For context persistence and learning
- Governance Layer: Human-in-the-loop, audit logs, fallback policies
Agents can be cloud-native, containerized, and deployed alongside existing microservices.
Final Thoughts
Agentic AI is not a trend—it’s a capability shift. It allows systems to adapt, act, and scale decisions faster than humans ever could. For enterprises, it’s not about replacing people—it’s about augmenting them with agents that can handle complexity, speed, and scale.
Early adopters will gain a real advantage—not just by reducing costs, but by building more adaptive, intelligent, and responsive operations.
Adanto helps enterprise teams design, deploy, and manage autonomous AI agents. From pilot projects to full-scale production systems, we partner with clients across fintech, retail, and cloud operations to deliver results.
Adanto has helped us be more productive and monitor costs of an AWS cloud
James Wetzig
Sr. Manager, Architecture & Infrastructure Platform Delivery
Robert Half
Adanto Delivers AWS-Powered Job Alert Marketing Automation
Adanto delivers a multilingual marketing automation workflow using AWS, Salesforce, Eloqua, and Drupal for the global web marketing team of a leading Silicon Valley consulting enterprise.
The quality and skills of your engineers are very impressive. You have helped us accelerate the delivery of our fin tech prototype that has turned heads at Zions Bank
Joel Schwartz
CEO
DoubleCheck Solutions
Adanto SOC consultation and proposal was very compelling and on par with the GE proposal. Your security engineers are very caapable.
Mark Hopkins
Security Operations (SOC) Lead
Robert Half
Peter and the Adanto Software team has come through for us and at the lightening speed
Joao Bettencourt
IT Software Project Manager
Robert Half
Adanto has proven to be an invaluable strategic partner for Cloudify. Having spent many years working with various engineering services company’s Adanto excels not only in the quality and speed of services they deliver but also in their commitment to fairness and transparency
Luca Rajabi
VP, Solutions
Cloudify
Key Results
$1.5M
Annual savings from server consolidation & cloud migration
$400k
Annual costs savings from email workflow automation
$300k
Annual savings from AWS autoscaling that reduces unnecessary cloud resource use
Technologies used
- Java as core backend development for scalable workflows, API integration, and business logic.
- Jenkins for automated CI/CD pipelines for seamless builds, testing, and deployments.
- Kibana for visualized system logs and metrics for real-time monitoring and optimization.
- Drupal to Build a multilingual, user-friendly web interface supporting 19 languages.
- Amazon Web Services (AWS)
- SQS: Handled scalable subscription workflows.
- SNS: Delivered job alerts and notifications.
- S3 Bucket: Cost-effective storage for static assets.
- RDS: Managed relational databases for user data.
- KMS: Secured sensitive data with encryption.
- Docker for containerized deployments ensured consistency across environments.
- Oracle Eloqua Marketing Cloud Service
- Salesforce as centralized lead and subscription management
Challenge
This project was designed to enhance the job-seeking and recruitment process by providing users with the most accurate and up-to-date information about available job openings. The solution was to be tailored to align job postings with the user’s preferences and qualifications, as provided during their subscription process. A critical component of the project was the usage of multilingual email templates, enabling effective and personalized communication across a wide range of regions.
Key goals
Attracting New Talent: Recruiting new candidates in a competitive job market requires highly targeted outreach and personalized communication to build trust
Precision Matching: The platform efficiently connects users with relevant job openings, maximizing opportunities for both employers and job seekers
Scalability: Delivering consistent, high-quality job alerts in 16 countries and 19 languages while addressing translation and cultural nuances
Data Accuracy: Ensuring reliable integration and synchronization of user data with job openings for precise alerts
Solution
This solution exemplifies how advanced technology, automation, and localization can work together to deliver a seamless and scalable user experience while meeting the strategic goals of a global organization.
- Enhanced User Engagement: The multilingual platform and email templates ensure users receive relevant and personalized updates, improving overall satisfaction and engagement.
- Efficient Automation: The AWS-based workflow eliminates manual intervention in subscription management, reducing errors and increasing operational efficiency.
- Scalability and Flexibility: The cloud-based architecture supports high volumes of data and user interactions, allowing the solution to scale seamlessly with business growth.
- Data-Driven Insights: Oracle Eloqua’s analytics capabilities provide valuable insights into campaign performance, enabling continuous improvement in user communication strategies.
- Global Reach: Multilingual support ensures the solution meets the needs of a diverse audience across multiple regions and languages.
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In finance, compliance is not optional. It’s a requirement—and a costly one. Banks, insurers, and investment firms spend billions each year to keep up with changing regulations. And the pressure is only growing.
But there’s a shift happening. Agentic AI is quietly changing how compliance work gets done. It’s not about replacing compliance officers. It’s about giving them smarter tools to handle the growing complexity, faster and with fewer errors.
This article explores how Agentic AI is helping financial institutions automate compliance. We’ll look at what’s driving the need, how the technology works in real situations, and what to consider before moving forward.
Table of Contents
The Rising Cost of Compliance
Regulatory pressure has increased steadily since the 2008 financial crisis. From anti-money laundering (AML) to data privacy laws, institutions face a maze of rules—many of which change frequently and differ across regions.
In 2023, major banks in the U.S. spent an average of 10–15% of their operating budget on compliance. It’s not just about cost. It’s about the time lost to manual reviews, document collection, and reporting. Mistakes are expensive too. Fines for non-compliance have totaled over $400 billion globally since 2009.
The current model isn’t sustainable. The pace of regulatory change is faster than human teams can reasonably handle. That’s why automation is no longer a “nice to have.” It’s necessary.
Why Traditional Methods Fall Short
Compliance teams often rely on rule-based systems. These are good for tasks with clear, static logic. But regulations aren’t static. They change. They differ between jurisdictions. They sometimes contradict each other.
Manual processes add another layer of risk. Humans miss things—especially when reviewing hundreds of pages of legal text or thousands of transaction records. And as data volumes grow, human teams can’t scale fast enough to keep up.
Spreadsheets and legacy systems can’t carry this weight anymore. They’re too rigid. They’re slow to adapt. They don’t learn or improve over time.
How Agentic AI Fits In
Agentic AI changes the approach. Instead of following fixed rules, it can interpret patterns, reason through steps, and act independently within set boundaries. It’s goal-oriented. It doesn’t just process data—it understands tasks in context and decides how to complete them.
In compliance, that means:
- Reading regulations and extracting relevant obligations
- Monitoring transactions in real time to flag suspicious activity
- Checking policies against regulatory frameworks
- Creating audit trails without manual input
- Filling out reports with accuracy and traceability
Agentic AI doesn’t operate in isolation. It works with people. A compliance officer may set the goal—like checking transactions for sanctions risk—and the AI figures out the best steps to take, asks for feedback, and updates its process over time.
This makes it more flexible than past automation tools, but also more accountable than traditional AI models that function like black boxes.
Real-World Applications
AML Transaction Monitoring
A global bank used Agentic AI to overhaul its AML process. Instead of flagging transactions based on fixed thresholds, the AI evaluated behavior over time, cross-checked it with external data sources, and adapted its logic as new risk patterns emerged. False positives dropped by 40%. Investigators could focus on real threats, not noise.
Regulatory Change Management
A European investment firm used Agentic AI to track regulatory updates across 15 jurisdictions. The system scanned official documents, matched them to internal policies, and alerted the legal team when adjustments were needed. What used to take weeks now happens in hours.
KYC Automation
A fintech startup applied Agentic AI to its KYC onboarding flow. Instead of fixed forms, the AI led dynamic interviews with customers, asked only the necessary questions, and validated documents in real time. This reduced drop-off rates and cut verification time by 70%.
Challenges and Risks
This isn’t plug-and-play technology. There are serious risks to consider:
- Oversight: Agentic systems need constant human supervision. They’re smart, but not infallible.
- Auditability: Regulators require clear, traceable records. The AI must show why it made a decision.
- Bias and fairness: If the training data is flawed, the system can make biased or inconsistent calls.
- Data privacy: Handling sensitive financial and customer data demands strict safeguards.
Trust is built through transparency, testing, and clear limits on what the system is allowed to do.
What to Do Next
If you’re exploring Agentic AI for compliance, start small. Identify a specific pain point—like transaction monitoring or document review—and build a pilot with clear goals.
Make sure your compliance, legal, and tech teams work together from the start. This isn’t just a software project. It’s a shift in how work gets done.
Finally, choose a vendor or partner with deep experience in both AI and finance. The right expertise makes a big difference in managing risk and getting results.
Conclusion
Compliance isn’t getting easier. The volume of regulations, the speed of change, and the cost of mistakes are all going up. Traditional tools can’t keep up.
Agentic AI offers a smarter way forward. Not by replacing people, but by helping them work faster, smarter, and with more confidence. It can read, reason, act, and improve. That’s a big shift for finance—and one that’s already underway.
Want to use AI in your business?
Get in touch with the Adanto Software Team today to see how we can help.
Majic (Maciek) and the rest of Adanto team were great to work with. Thank you.
Jeff Keihl
Functional Architect, IT Development, Financial Services Applications
Robert Half
Key Results
95%
Accuracy achieved in data reporting
$1M+
Annual IT cost savings achieved from reduction of need for multiple systems
25%+
Accuracy improvement in budget &forecasting cycles
Services performed
- Data Analytics & Business Intelligence
- Software Development & Maintenance
Technologies used
- Deltek Maconomy
- SAP BusinessObjects Web
- PL-SQL, SQL
- Oracle, SQL Server
- AWS EC2
- AWS RDS
- Java
Project size
- 5 SAP BusinesObject Universes
- 200+ financial and management reports
- 20+ key performance indicators
Challenge
Robert Half IT faced significant challenges in gaining clear insights into spending, departmental performance, and Service Level Agreement (SLA) compliance. These issues led to inaccurate forecasting, budget overruns, and underspending. To address these challenges, the company engaged Adanto to help implement a new (ERP) system, Deltek Maconomy, and develop an advanced enterprise data analytics, visualization, and reporting platform utilizing SAP BusinessObjects Universes.
Key goals
Enhance Decision-Making with Reliable Data: Ensure access to accurate, consistent, and comprehensive data across departments to enable informed decision-making at all organizational levels.
Streamline Data Access and Reporting: Implement a centralized platform to provide quick and intuitive access to critical data, reducing the time and effort required for reporting and analysis.
Improve Data Visualization and Insights: Utilize advanced graphical representations and analytical tools to identify hidden patterns and interdependencies in data for better strategic planning.
Boost Operational Efficiency: Leverage accurate data and automated reporting processes to optimize workflows and minimize errors caused by outdated or inconsistent information.
Solution
The BI SAP BusinessObjects Solution for Maconomy ERP involved integrating SAP BusinessObjects as the Business Intelligence (BI) and reporting layer for the financial and operational data managed within Maconomy ERP for the client. This solution provides advanced analytics, reporting, and visualization capabilities, enabling Robert Half to gain deeper insights into their Maconomy data and optimize decision-making.
- Customizable Reports and Dashboards
- Data Integration (ERP & SAP with ETL)
- Ad-Hoc Reporting
- Enterprise Reporting
- Advanced Analytics
- Governance and Security
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Key Results
$10M+
Estimated annual cost savings from automation and operational efficiancies
15%
Annual revenue growth from much more precise lead gen
40%+
Reduction in recruitment cycle times enabling the company to fill client needs faster.
Services performed
- Data Science
- Data Analytics & Business Intelligence
- Data Warehousing
- Machine Learning
- Artificial Intelligence
- Natural Language Processing
- Web & Mobile Apps
- UX/UI
- Custom Application
- Development
- DevOps
- Security
- Infrastructure Services
- Administration Services
- Azure Cloud
Technologies used
- C#/.Net Application Framework
- Enterprise SOA Platform
- SOA-connected external services
- Natural Language Processing algorithms for data correlation and analytics of many file formats (*.pdf, *.rft, *.doc, *.txt)
- Amazon AWS Cloud
Challenge
- Sales teams dissatisfaction with the leads process, lead quality and lead management.
- Very poor sales productivity as measured by the sales effectiveness index.
- Non-standard individual lead generation activity leading to poor quality, wasted time
- Stagnant quarterly revenue and market share loss to niche players
- Hard to find information while on-the-go
- Complexity.
Key goals
Improve lead conversion rate
by 30%
Establish more predictable
and more sustainable profitable revenue growth, Empower sales and marketing while on-the-go
Significantly Improve Sales Effectiveness, Sales Productivity, and Sales Satisfaction.
Deliver targeted, hot leads to the right sales specialist in a simple form, at the time of need
Solution
- The Profile Writer application installation package
- Quill Data Science App connector
- Sovren connector
- S3Bucket connector
- RDS connector
- Amazon connector
- SHIM connector
- SHIM services implementation
- Positive final security remediation report
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Key Results
100k+
Total number of micro-service apps automated at all US centers
85%
Costs reduction, for the new process currently requires only minimal manual intervention.
$1.83M
Estimated annual savings, brought about by the automation.
Technologies used
- Infrastructure:
- ASP.NET Web Application
- Micro-services architecture
- C#/.NET Back-end
- Cloud Platform hosting – Azure
- Express, Vue.JS, Electron/Chromium
- Azure DevOps pipelines
- Microsoft SQL Server
- Micro-Services:
- RESTful API services
- Node.JS service
- MongoDB as a Windows service
- Redis as a Windows service
- Nginx as a Windows services
- LSX App services
- Print services
- Order services
- Data Ingest services
- Remote upload data service
- Data:
- Windows Event Logs
- Windows System Information
- Windows Registry Logs
- Print Service Logs
- Windows Services Logs
- LSX Services Logs
- LSX Installer Logs
Challenge
Photo Centers faced several challenges, including extensive manual effort required to analyze system activities, the lack of remote access to logs across all photo servers, and slow, costly error detection processes. Additionally, manual shutdowns of individual centers posed risks such as data loss and disruption of image orders. The absence of automatic system update capabilities further exacerbated operational inefficiencies.
Key goals
Reduce Manual Work: Automate data collection, monitoring, and reporting to eliminate the need for manual system analysis in each Photo Center.
Ensure Safe Shutdowns: Implement a secure, automated shutdown protocol for micro-service apps to prevent data loss & preserve the integrity of image orders.
Enable Remote Access to Logs: Provide remote access to logs across all photo servers to improve the speed and efficiency of error detection and resolution.
Improve Error Detection & Resolution: Enhance error detection speed and reduce costs using real-time monitoring and automated alert systems.
Solution
Centralized Logging System:
- ASP.NET Web Application acts as the core of the centralized logging system, aggregating logs from all micro-services across photo centers, regardless of programming language. It also gathers Windows system logs, including services, registry, event logs, and system information.
- Log Collection and Aggregation: Consolidates real-time logs from various micro-services into a unified repository, enabling efficient monitoring and analysis of system activities across photo centers.
Remote Shutdown System:
- ASP.NET Web Application: Manages the orderly shutdown of all micro-services per photo center , ensuring data integrity and preventing image order loss.
- Automated Shutdown Protocol: Executes a step-by-step shutdown, prioritizing service dependencies for graceful completion.
- System Update Automation: Seamless deployment of system updates.
- Data Integrity Assurance: Ensures all transactions are completed and data securely stored to prevent corruption or loss
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You have truly saved our product from a near demise caused by an incompetence of a far-shore supplier
Bob Maeser
CTO
Quest Software
Key Results
$600k
Annual reduction in errors & downtime through automating infrastructure management and integration testing
$325k
Reduction in Developer Downtime
with automated testing, error alerts, and real-time monitoring, annually
$65k
Annual savings from reduced manual testing and deployment scripts and CI tools by 50%
Technologies used
- Metrics:
- Source of projects from 3 different version control systems
- 13 silo projects integrated in one
- Technologies used:
- AWS AMI (Amazon Machine Images)
- AWS EC2 (Elastic Compute Cloud)
- AWS S3 (Simple Storage Service)
- AWS CloudFormation
- AWS Cloudwatch
- AWS CLI (Command Line Interface)
- Jenkins Continuous Integration
- Sonarqube
- Maven
- Nexus
- HashiCorp Packer
- Bash scripting
- PowerShell
- Chef (Infrastructure Automation)
- RabbitMQ
- HA Cluster
- Angular, Python, Java, NodeJS, Drupal,
- Unix,
- Git, Bitbucket, SVN,
- Slack – notification of an error for configured groups
Challenge
Robert Half struggled to manage multiple concurrent software projects built by different teams using various programming languages on a shared cloud infrastructure and on-premise databases. The lack of interoperability between siloed projects caused frequent errors, extensive testing, and infrastructure-related issues. Misaligned changes to the shared environment led to disruptions, frustrating developers and causing severe data center outages that impacted thousands of users, emphasizing the need for a unified, automated solution to streamline development and deployment.
Key goals
Enable Cross-Project Integration: Ensure seamless collaboration between siloed projects to prevent disruptions and errors
Automate Testing: Reduce errors and costs by implementing automated testing during development and deployment
Improve Monitoring: Introduce monitoring and alerts to quickly detect and resolve issues during development
Streamline Deployment: Build a stable, automated platform for faster, more efficient continuous integration and deployment
Solution
Adanto implemented a Cross-Project Cloud Integration Platform (CP-CIP) to address the client’s challenges in managing multiple siloed projects within a shared cloud infrastructure. This solution introduced automation, monitoring, and integration tools to streamline development, reduce errors, and ensure seamless collaboration across projects.
- Automated Infrastructure: Scripts automated project-specific environment creation, ensuring consistency and eliminating manual setup errors.
- Integration Testing: Automated cross-project tests ensured compatibility during development and retrofitted for production, preventing conflicts.
- Continuous Integration: A CI platform using Jenkins and SonarQube automated builds, testing, and deployments, supporting frequent releases.
- Monitoring and Alerts: AWS CloudWatch and Slack alerts provided real-time error detection and faster issue resolution.
- Streamlined Workflows: Trigger-based scripts automated environment builds, deployments, and testing, reducing manual effort.
- Unified System: Integrated 13 siloed projects into a single platform, improving visibility and reducing inefficiencies.
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Thank you Adanto Team for the foundational architecture decisions and deliveries to get us going with Web Alerts!
Jim Falls
Sr. Director, IT - Marketing/Corporate Communication Services
Robert Half
Key Results
$2.6M
Annual Cost Savings from IT infrastructure optimization and automation & efficiency
$1M+
Annual productivity gains from time & effort savings and faster decision-making
$500k+
Business Enablement gains from improved SLA compliance & new insights, innovation & agility
Services performed
- Data Science
- Data Analytics & Business Intelligence
- Data Warehousing
- Big Data
- Machine Learning
- Artificial Intelligence
- DevOps
- Security
- Infrastructure Services
- Salesforce
- Amazon Cloud
- Azure Cloud
Technologies used
Data Sources/Silos
- 60+ data sources
- 200+ GB of new data per day
- One Data Store (Data in different AWS data stores based on data type)
- Amazon S3
- Amazon EC2
- Amazon Redshift (data warehouse for standard SQL queries & BI tools)
- Amazon RDS (relational database for many instance types)
- Apache Sqoop (O/S tool for bulk data transfers)
- Amazon HDFS (Parquet) (Hadoop Cluster with EMR – Elastic MapReduce)
Query Tools & Analytics
- Apache Hive, Pig, Spark (O/S database query interface tools to HDFS & processing engine)
- R (O/S statistical programming language for data mining and statistical computing)
- Mahout/scikit-learn (O/S tools for building Machine Learning apps)
- QlikView, PowerBI, SAS (data analytics, business intelligence and reporting tools)
Challenge
Robert Half was challenged with lack of easy access to company’s enterprise data. The company faced multiple challenges for which it was seeking a solution:
- Limited agility and accessibility for data analysis.
- Data silos preventing effective information sharing.
- High costs due to server and license proliferation and IT complexity (shadow IT)
- Expensive scalability and lack of flexibility for new systems.
Key goals
Create a centralized repository for raw data accessible across departments
Implement incremental load processes and data governance procedures
Develop thematic, departmental, and business line-focused data marts
Build analytic applications tailored to specific business needs
Solution
Big Data Lakes are enterprise-wide data management platforms that store disparate data sources in their native format until queried for analysis. Unlike purpose-built data stores, data lakes consolidate raw data in its original form, eliminating information silos and enabling better data sharing. This approach reduces server and licensing costs, provides scalable and flexible storage, and ensures data accessibility for both programmers and business users
Adanto implemented a scalable and cost-effective cloud-based data lake infrastructure:
- Stored data in Amazon S3 Buckets for cost efficiency.
- Utilized parquet file format with HDFS/Hive for structured querying.
- Established a Hadoop/Spark cluster in AWS with autoscaling capabilities.
- Set up incremental data load processes using Apache Sqoop on an EMR cluster for daily data ingestion.
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Honeywell Int’l Inc.
Adanto Delivers IoT-Enabled HA Telemetry for Gas Pipelines
Adanto delivered an IoT-enabled HA telemetry solution for gas pipelines, ensuring 99.9% uptime, real-time monitoring, and auto-healing. Operational for over eight years, it provides reliable, scalable, and environmentally friendly gas transportation.
Key Results
$2.7M
Annual savings through reduced maintenance, downtime, & optimized energy usage
$1.5M
Annual costs savings from incidents prevention & ensured compliance
$2.1M
Annual revenue growth from more client value of increased throughput & better utilization
Technologies used
- Infrastructure:
- IBM Power 6 servers running IBM AIX operating system
- Power VM for load separation
- Power HA for Telemetry System HA
- Oracle RAC for database HA
- Moxa Industrial Ethernet switches
- CISCO Catalyst switches and routers
- Brocade SAN switches
- IBM Disk Storage & Tape Storage: 2 tape drives and 48 LTO-3 tapes.
- Tivoli Storage Manager
- Performance:
- 99.9% system availability & 500ms data sampling density
- Full redundancy of hardware and software components
- 6 months data retention for raw data
- 3-year data retention for data reporting
- RTO < 8h & RPO < 48h
- Bare metal recovery
- >3TB of telemetry data
- >1000 data samples read per second
- Max 40ms recovery time after network topology change
Challenge
Our oil and gas pipeline client faced challenges operating in remote, inaccessible terrain, requiring real-time monitoring of gas flow. Manual oversight was impractical, and the client needed a highly available, IoT-enabled solution to process large volumes of telemetry data, ensure reliability, minimize maintenance, and maintain safety and regulatory compliance.
Key goals
Real-Time Monitoring: Ensure continuous, real-time tracking of natural gas flow metrics for safety and efficiency
High Availability: Deliver a 99.9% uptime system with auto-healing and redundancy to prevent disruptions
Scalability: Handle high-density telemetry data with scalable infrastructure and long-term data retention
Low Maintenance: Provide a self-sustaining solution with automated updates for remote, inaccessible locations
Solution
Adanto delivered an IoT-enabled high-availability telemetry solution for real-time monitoring and management of natural gas pipelines in remote and challenging terrains. This solution provided safe, reliable, and efficient operations with minimal manual intervention, meeting the client’s goals for scalability, reliability, and environmental compliance. The solution included:
- High-Availability Clusters: Designed for 99.9% uptime, ensuring continuous monitoring and operational reliability
- IoT-Enabled Sensors and Systems: Integrated real-time data collection and processing of over 1,000 telemetry data samples per second.
- Robust Infrastructure: Leveraged IBM Power servers, Oracle RAC databases, industrial-grade networking equipment, and cloud storage for scalability and performance.
- Automation and Resilience: Implemented auto-healing features for system updates, firmware upgrades, and software patches with minimal maintenance.
- Secure Data Retention: Ensured long-term storage of telemetry data (6 months for raw data, 3 years for reporting) and rapid recovery capabilities.
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Adanto was pivotal in getting our iTrack Reporting Workstream Project on track and successful. Sheila Santana, VP, IT
Sheila Santana
VP, IT Field Services
Robert Half
We have been continually reassured of Adanto’s versatile portfolio of expertise while tasked with deploying a major multi-national pharmaceutical company’s Secure Transit VPC across multiple geographic regions spanning two continents. Adanto’s ability to work with geographically dispersed teams and deliver on the customer’s terms in multiple timezones is a true differentiator few engineering services company’s can offer.
Francesco Alongi
Senior Manager Cloud Strategy
Advanced Informatics and Analytics
Astellas Pharma
Adanto software team with Piotr really rocked.
Scott Francis
Sr. Director Applications
Robert Half
Adanto and especially Magic were instrumental in getting our iTrack Reporting Workstream Project on track and successful, beyond expectations. Thank you. The whole team was wonderfule to work with in on site in San Ramon, CA and off-site from Poland
Thuy Nguyen
Sr. Manager, IT Development
Robert Half
We all greatly appreciate the time you’ve spent with Adanto and for your various softwareservices
Jason Fiber
SVP/GM Mobile Group
THX
In 2024, global fraud losses reached $485 billion, with digital payment fraud rising by 18% year-over-year. The fraud landscape is evolving fast—driven by automation, AI-assisted scams, and synthetic identities. Traditional fraud detection systems struggle to keep pace, largely because they rely on rule-based logic and reactive human workflows.
To respond in real time, organizations need systems that can act on their own. This is where autonomous AI agents come into play. These agents don’t just identify fraud—they execute decisions, trigger actions, and evolve with each case they process. In short, they operationalize intelligence at machine speed.
In this article, I’ll explain how these agents work, what they’re capable of, and where they’re already delivering measurable impact — particularly in financial services, insurance, and e-commerce.
Table of Contents
What Is an Autonomous AI Agent?
Autonomous AI agents are software entities that can perceive data, reason over it, make decisions, and take actions—without human intervention. These agents typically combine:
- Real-time data ingestion
- Machine learning (often anomaly detection, clustering, or reinforcement learning)
- A decision-making engine
- A trigger mechanism (e.g., blocking, alerting, escalating)
Unlike static ML models embedded in a rules-based system, autonomous agents are built for continuous operation. They interact with other systems, manage workflows, and adapt based on outcome feedback.
Why Traditional Fraud Detection Falls Short?
Most fraud detection pipelines today are reactive:
- Transactions are scored based on predefined thresholds
- Alerts are queued for review
- Analysts triage cases manually
- Action is taken hours or days later
This approach introduces delays, fatigue, and inconsistency. Worse, fraudsters exploit these weaknesses with rapid attacks that mimic normal behavior. Static systems can’t detect these dynamic patterns fast enough—and they certainly can’t respond in real time.
How Autonomous Agents Address the Gap?
Autonomous agents are designed to close the decision-action loop. Here’s how:
- Continuous monitoring: They evaluate live data streams rather than periodic batches.
- Pattern learning: They learn over time—detecting not just known fraud, but emerging anomalies.
- Decision execution: They act immediately—freezing accounts, flagging claims, or launching investigation workflows.
- Feedback loops: They learn from past actions, enabling them to refine future decisions.
Agents can also operate across systems—integrating with CRMs, payment processors, document repositories, and third-party data providers.
Key use cases and results
Let’s explore where autonomous agents are already in use, and what kind of value they’re delivering.
Financial Services – End-to-End Case Handling
A credit union partnered with Accelirate to streamline its fraud operations. An AI agent was deployed to:
- Check transactions across Symitar and Extranet
- Match against historical behavior
- Eliminate duplicates
- Trigger escalation workflows
Results:
- 657 analyst hours saved annually
- 98% reduction in processing errors
- $19,800 in direct cost savings
Insurance – Claims Fraud Detection
An insurer used an agent to review low-dollar claims submitted within short timeframes across multiple user profiles. The agent:
- Flagged matching metadata and document reuse
- Pulled historical claims from different user IDs
- Auto-generated fraud reports for the investigation team
Results:
- 245% ROI within the first year
- $320,000+ in savings
- 62% reduction in claim resolution time
E-Commerce – Loyalty Abuse Prevention
Retailers are increasingly targeted by bot-driven attacks—fake signups, coupon abuse, and identity farming. AI agents can:
- Detect fake account clusters (shared IPs, browser fingerprints, timing anomalies)
- Flag attempts to exploit loyalty programs
- Pause reward disbursement and notify risk teams
Impact: Fewer false positives than rules-based systems, with real-time enforcement and reduced operational load on fraud teams.
Fintech Lending – Synthetic Identity Detection
Fintech lenders deal with high volumes and thin data. One client used agents to catch applications that:
- Used slightly altered identity data (e.g., different DOB or SSNs with matching addresses)
- Applied to multiple loan products in rapid sequence
- Reused documents across supposedly unrelated accounts
The agent connected the dots and auto-rejected risky applicants before credit was issued.
Known Challenges And Risks
No system is perfect. There are trade-offs to consider:
- Explainability: Deep-learning agents can make decisions that are hard to justify without traceable logic. This is a concern for regulated industries.
- Bias: If agents are trained on biased data, they may reinforce discrimination (e.g., falsely flagging users based on geography or demographic patterns).
- Overreach: Agents acting too aggressively (e.g., false account freezes) can damage user trust and create compliance risks.
To mitigate these, agents should be built with human-in-the-loop oversight, audit trails, and risk thresholds that define when automation is allowed to act independently.
What To Expect Going Forward
Autonomous AI agents will evolve beyond single-use cases. We’re seeing early adoption of multi-agent systems, where:
- One agent focuses on transaction-level fraud
- Another monitors identity risk over time
- A third handles response orchestration and user communication
This layered approach builds resilience and adaptability.
We’re also likely to see closer integration with identity verification, KYC, behavioral biometrics, and external fraud intelligence feeds.
Conclusion
Autonomous agents are not silver bullets. But they are a necessary shift. As fraud tactics grow more automated, detection and prevention must move at the same pace.
The true value of these agents isn’t just speed—it’s consistency, scalability, and reduced human burden. In areas like fintech, e-commerce, and insurance, the business case is already clear.
Want to use AI in your business?
Get in touch with the Adanto Software Team today to see how we can help.
In finance, compliance is not optional. It’s a requirement—and a costly one. Banks, insurers, and investment firms spend billions each year to keep up with changing regulations. And the pressure is only growing.
But there’s a shift happening. Agentic AI is quietly changing how compliance work gets done. It’s not about replacing compliance officers. It’s about giving them smarter tools to handle the growing complexity, faster and with fewer errors.
This article explores how Agentic AI is helping financial institutions automate compliance. We’ll look at what’s driving the need, how the technology works in real situations, and what to consider before moving forward.
Table of Contents
The Rising Cost of Compliance
Regulatory pressure has increased steadily since the 2008 financial crisis. From anti-money laundering (AML) to data privacy laws, institutions face a maze of rules—many of which change frequently and differ across regions.
In 2023, major banks in the U.S. spent an average of 10–15% of their operating budget on compliance. It’s not just about cost. It’s about the time lost to manual reviews, document collection, and reporting. Mistakes are expensive too. Fines for non-compliance have totaled over $400 billion globally since 2009.
The current model isn’t sustainable. The pace of regulatory change is faster than human teams can reasonably handle. That’s why automation is no longer a “nice to have.” It’s necessary.
Why Traditional Methods Fall Short
Compliance teams often rely on rule-based systems. These are good for tasks with clear, static logic. But regulations aren’t static. They change. They differ between jurisdictions. They sometimes contradict each other.
Manual processes add another layer of risk. Humans miss things—especially when reviewing hundreds of pages of legal text or thousands of transaction records. And as data volumes grow, human teams can’t scale fast enough to keep up.
Spreadsheets and legacy systems can’t carry this weight anymore. They’re too rigid. They’re slow to adapt. They don’t learn or improve over time.
How Agentic AI Fits In
Agentic AI changes the approach. Instead of following fixed rules, it can interpret patterns, reason through steps, and act independently within set boundaries. It’s goal-oriented. It doesn’t just process data—it understands tasks in context and decides how to complete them.
In compliance, that means:
- Reading regulations and extracting relevant obligations
- Monitoring transactions in real time to flag suspicious activity
- Checking policies against regulatory frameworks
- Creating audit trails without manual input
- Filling out reports with accuracy and traceability
Agentic AI doesn’t operate in isolation. It works with people. A compliance officer may set the goal—like checking transactions for sanctions risk—and the AI figures out the best steps to take, asks for feedback, and updates its process over time.
This makes it more flexible than past automation tools, but also more accountable than traditional AI models that function like black boxes.
Real-World Applications
AML Transaction Monitoring
A global bank used Agentic AI to overhaul its AML process. Instead of flagging transactions based on fixed thresholds, the AI evaluated behavior over time, cross-checked it with external data sources, and adapted its logic as new risk patterns emerged. False positives dropped by 40%. Investigators could focus on real threats, not noise.
Regulatory Change Management
A European investment firm used Agentic AI to track regulatory updates across 15 jurisdictions. The system scanned official documents, matched them to internal policies, and alerted the legal team when adjustments were needed. What used to take weeks now happens in hours.
KYC Automation
A fintech startup applied Agentic AI to its KYC onboarding flow. Instead of fixed forms, the AI led dynamic interviews with customers, asked only the necessary questions, and validated documents in real time. This reduced drop-off rates and cut verification time by 70%.
Challenges and Risks
This isn’t plug-and-play technology. There are serious risks to consider:
- Oversight: Agentic systems need constant human supervision. They’re smart, but not infallible.
- Auditability: Regulators require clear, traceable records. The AI must show why it made a decision.
- Bias and fairness: If the training data is flawed, the system can make biased or inconsistent calls.
- Data privacy: Handling sensitive financial and customer data demands strict safeguards.
Trust is built through transparency, testing, and clear limits on what the system is allowed to do.
What to Do Next
If you’re exploring Agentic AI for compliance, start small. Identify a specific pain point—like transaction monitoring or document review—and build a pilot with clear goals.
Make sure your compliance, legal, and tech teams work together from the start. This isn’t just a software project. It’s a shift in how work gets done.
Finally, choose a vendor or partner with deep experience in both AI and finance. The right expertise makes a big difference in managing risk and getting results.
Conclusion
Compliance isn’t getting easier. The volume of regulations, the speed of change, and the cost of mistakes are all going up. Traditional tools can’t keep up.
Agentic AI offers a smarter way forward. Not by replacing people, but by helping them work faster, smarter, and with more confidence. It can read, reason, act, and improve. That’s a big shift for finance—and one that’s already underway.
Want to use AI in your business?
Get in touch with the Adanto Software Team today to see how we can help.
Key Results
72%
Reduction of the abandoned calls (from 18% to 5%), translating to thousands of retained customer interactions annually
73%
Decrease in average call hold time (from 15 to 4 min), significantly improving customer experience and reducing costs
20-30%
Estimated annual savings in operating costs due to reduced manual reporting, improved SLA
Technologies used
- ETL and Data Integration:
- Pentaho Data Integrator (ETL tool)
- MySQL (for data warehousing)
- APIs and Real-Time Data Access:
- REST APIs (for real-time data access and updates)
- Cloud Infrastructure:
- Amazon AWS (cloud storage and processing)
- Microsoft Azure (cloud storage and analytics platform)
- Reporting and Dashboards:
- Microsoft Power BI (for advanced reporting and interactive dashboards)
- Machine Learning and Analytics:
- Custom machine learning algorithms (for data correlation and advanced analytics)
- Source Systems for Data Extraction:
- ShoreTel (PBX business phone systems)
- CIC (Customer Interaction Center)
- ServiceNow Cloud (service management system)
Challenge
Environment: A complex global operation with 15 contact support centers across North America, EMEA, and APAC regions, handling 2.5 million monthly events across 30 disparate databases, using systems like ShoreTel, CIC, and ServiceNow Cloud.
Challenge: Manual reporting, data silos, and lack of real-time insights led to poor SLA performance, delayed issue resolution, high operating costs, and customer dissatisfaction.
Key goals
Enable real-time analysis of caller behavior for management decision-making
Provide accurate, real-time reports for call center management and stakeholders
Deliver predefined executive dashboards accessible via cloud on mobile and desktop
Streamline operations by integrating data from multiple systems and improving reporting accuracy
Solution
Adanto implemented a powerful real-time Big Data Analytics solution to transform a global HR leader’s contact center operations. By integrating advanced technologies, cloud platforms, and machine learning, Adanto streamlined a complex, siloed environment into a real-time analytics system:
- Data Integration: Adanto centralized data from 30 disparate databases using Pentaho ETL and MySQL, integrating sources like ShoreTel, CIC, and ServiceNow
- Real-Time Access: REST APIs enabled continuous data updates, providing stakeholders with up-to-date insights for decision-making.
- Machine Learning: Advanced algorithms correlated data across systems, achieving 89% accuracy by using time as a common key.
- Cloud Infrastructure: AWS and Azure ensured scalable, secure storage and accessibility for global users.
Reporting and - Dashboards: Microsoft Power BI delivered real-time dashboards and reports, enabling data-driven decisions from any devices.
- Optimization: Custom tools enhanced data collection and accuracy, resolving inefficiencies and boosting operational performance.
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