Agentic AI Platform for Intelligent Customer Self-Service

The Challenge

A large enterprise wanted to move beyond traditional chatbots on its B2C customer portal. The goal was not just FAQ-style automation, but a system that:

• understands customer context,
  
• reasons across multiple business domains (billing, products, processes, policies),
  
• can explain complex situations in plain language, and
  
• supports customers in completing real tasks, not just answering generic questions.
  

The existing landscape consisted of:

• fragmented backend systems,
  
• complex product and pricing logic,
  
• policy and contract documents,
  
• customer data in operational databases.
  

The challenge was to build an AI layer that could coordinate knowledge from multiple domains and expose it in a safe, scalable, customer-facing way.

Our Solution

We designed and implemented an Agentic AI platform based on a multi-agent architecture using modern AI and data engineering components.

Core Architecture

• Agentic framework: Google ADK (Agentic Development Kit)
  
• Backend services & orchestration: Python-based microservices
  
• AI stack: LLMs, RAG, embeddings, vector databases
  
• Data integration: enterprise databases, documents, product and policy sources
  

Each AI agent was specialised for a business domain (e.g. billing, products, policies, processes). Agents were trained and grounded using:

• internal documents,
  
• structured database metadata,
  
• domain-specific knowledge extracted via Python-based data pipelines.
  

Agents could collaborate, delegate tasks, and combine results before generating a final response.

Key B2C Use Cases Implemented

1. Smart Self-Service Assistant

Customers can ask free-form questions such as:

• “Why is my bill higher this month?”
  
• “What is the status of my request?”
  

Multiple domain agents (billing, product, support) analyse the context, retrieve relevant data via RAG, and produce a unified, customer-friendly explanation.

2. Personalised Product & Offer Advisor

Agents analyse:

• customer profile and usage patterns,
  
• product rules and eligibility constraints,
  
• current portfolio.
  

They generate context-aware recommendations instead of static rule-based suggestions.

3. End-to-End Case Explanation

For ongoing requests (e.g. applications, changes), the system explains:

• current process step,
  
• which systems are involved,
  
• expected next steps and timelines.
  

Backend workflow states are translated into human-readable narratives using LLM agents.

4. Billing & Transaction Breakdown

Customers can ask for explanations of specific charges or transactions. The system connects:

• transaction data,
  
• tariff logic,
  
• product configuration,
  
• policy rules,
  

and generates a clear breakdown using coordinated domain agents.

Technical Implementation Highlights

• Python-based AI services orchestrating agent workflows and tool usage
  
• Multi-agent coordination using Google ADK
  
• RAG pipelines built with embeddings and vector search
  
• Integration with structured data via secure data access layers
  
• Prompt engineering, schema-constrained outputs and guardrails for safe customer-facing responses
  
• Evaluation of agent performance (accuracy, grounding, hallucination control)
  

The Result

The organisation moved from a simple chatbot model to an agentic, domain-aware AI layer that:

• provides more accurate and contextual customer answers,
  
• reduces load on human support,
  
• increases transparency of complex processes,
  
• and creates a scalable foundation for future AI-powered customer journeys.