DareData Use Case: Beyond Simple Chatbots

by Ivo Bernardo

How a Multi-Agent GenAI Architecture can Reshape Customer Support

Customer service teams deal with thousands of repetitive and routine questions every day: take a company in the telco sector, it can receive queries from mobile plan details to subscription issues, FAQs or service issues. Or a company from online retail that may receive millions of requests regarding product devolutions.

With all this complexity, is it possible to automate most of the interactions intelligently, without sacrificing quality, and even provide better answers than traditional support flows?

Yes, it is! In this blog post, we'll navigate how multi-agent GenAI systems can help you automate a large portion of your customer support queries.

The Challenge

Companies across industries face a flood of customer inquiries every single day.

In telecom, it’s:
“What’s my data balance?”
“How do I change my subscription?”
“Where’s my latest bill?”

In banking, it’s:
“Can you send me my last 3 transactions?”
“How do I activate my credit card?”
“What’s the status of my loan application?”

In retail, it’s:
“Where is my order?”
“Can I return this item?”
“Do you have this in stock?”

In utilities, it’s:
“Why is my bill higher this month?”
“How do I submit a meter reading?”
“When is my next payment due?”

While many of these questions are predictable and repetitive, most support systems (whether contact centers, legacy chatbots, or rule-based flows) struggle to scale, personalize responses, or adapt to changing customer needs. The traditional support model struggles with scale, consistency, and personalization.

That's where GenAI comes in.

What It Is: A Multi-Agent AI System Built for Scale

Let's take an example of how a GenAI chatbot can help you. To build a production-level chatbot, you mostly need three things:

  • Integration with current IT systems of the company
  • A way to flag cases where human-in-the-loop (HIIL) is needed
  • Improvement of performance with past interaction

Our AI architecture is built with Gen-OS, combining Retrieval-Augmented Generation (RAG) with API-based agents to handle both generic and highly personalized queries. Here’s how it works:

✅ RAG Agent:

Answers frequently asked questions using a curated FAQ database, powered by real-time business data and managed by business users.

✅ API Agents:

Plug directly into internal systems (mobile, subscription, and issue data) so users get precise, real-time answers tailored to their account.

✅ Agent Orchestrator:

Acts like a conductor in an orchestra, routing the query to the right agent and stitching the answers together seamlessly.

This multi-agent setup means AI agents don't guess and work side-by-side with humans to achieve enterprise automation.

The Architecture in Action

When a user sends a question (via web, app, or other channels), here’s what happens:

  • The Agent Orchestrator detects intent and routes the query.
  • Agent FAQs responds to general business inquiries using RAG.
  • Agent Mobile, Agent Subs, and Agent Issues tap into APIs to retrieve customer-specific data.

The system replies instantly, often without human intervention. But when one of the agents fail, HIIL process kicks in.

Business Impact

The million dollar question: is the tech generating business value? Let's see:

  • +50,000 conversations managed per month
  • 73% of queries resolved without human intervention
  • Over €400,000 in value generated since launch (August 2024)

Our customers are radically transforming their business support processes, unlocking tangible business value through increased efficiency, simplified operations, and enhanced decision-making capabilities

The Future of Customer Service

Customer Service Interaction with Gen-OS

You really want to avoid "just another chatbot". You want an AI system that's modular, that scales with the business and evolves with your customers and business.

You want to blend RAG and API agents into one orchestrated AI experience. With Gen-AI, companies can finally move beyond scripted responses and into real conversation.

If your customer support still relies on outdated rules or disconnected tools, now’s the time to rethink.

Want to see Gen-OS in action or explore how a similar solution could work for your business?


Let’s talk — ivo@daredata.ai