Case Study: Transforming Support with Agentic AI

Client Overview

Client A public listed Non-Banking Financial Company (NBFC) in India
Industry Financial Services
Technology Used RAG (Retrieval-Augmented Generation), Document Vector Stores, LLM, AI Agents

Objective: Develop an AI-powered support bot to streamline and enhance employee support across administrative, HR, and IT domains.

Background

With a workforce exceeding 30,000 employees, the NBFC faced increasing demands for efficient internal support. Employees relied heavily on traditional ticketing systems for resolving queries related to HR, IT, and administrative issues. This led to:

  • High Ticket Volume: Support teams were overwhelmed by repetitive queries, causing delays in response times.
  • Low Satisfaction Ratings: The manual processes contributed to a 3.8 average customer satisfaction score, leaving room for improvement.
  • Fragmented Knowledge Access: Employees struggled to locate relevant information from policy documents, SOPs, and databases.

The client needed an intelligent, scalable solution to address these challenges, reduce ticket load, and improve employee satisfaction.

Results & Impact

  1. 25% Reduction in Ticket Volume: The bot automated responses to repetitive queries, significantly lightening the load on support teams.
  2. Improved Employee Satisfaction: Average customer ratings soared from 3.8 to 4.6 within three months, reflecting faster and more accurate support.
  3. Seamless Information Retrieval: Employees now access policies, SOPs, and real-time database records in seconds, enhancing productivity.
  4. Scalability: The AI bot easily supports a workforce of 30,000+, handling thousands of queries daily with minimal latency.

By the Numbers

  • 25% decrease in support tickets.
  • 3.8 → 4.6 improvement in satisfaction scores in 3 months.
  • 30,000+ employees supported across HR, IT, and admin functions.
  • Thousands of queries resolved daily with near-instant response times.

Key Deliverables and Challenges

  1. Reduce Support Ticket Volume: Automate responses to common queries and empower employees with self-service tools.
  2. Enhance Employee Experience: Provide accurate, timely information to improve satisfaction ratings.
  3. Seamlessly Integrate with Existing Systems: Ensure compatibility with the company's knowledge repositories and ticketing systems.
  4. Leverage AI for Precision: Develop a conversational AI capable of retrieving information from structured and unstructured data.

Solution

Our team developed an agentic AI support bot using Retrieval-Augmented Generation (RAG) architecture, designed to address the client’s unique challenges.

Key Components

  1. Tree-Based Interface + AI Agent
    • Employees interact with a tree-based interface for predefined queries or escalate to an AI-powered conversational agent for complex issues.
  2. RAG Architecture for Accurate Retrieval
    • The bot uses document vector stores to index and retrieve relevant information from:
      • Policy documents and SOPs
      • Database records
      • Support ticketing systems
  3. AI Agents for Specialized Support
    • AI Ticketing Agent: Automates ticket creation and status updates.
    • Database Agent: Retrieves structured data (e.g., leave balances, IT inventory status).
  4. LLM for Conversational Support
    • A large language model (LLM) processes retrieved data to generate precise and contextual responses, delivering human-like assistance.
  5. Real-Time Insights and Analytics
    • The bot tracks usage patterns and employee satisfaction to continually refine its performance.

Conclusion

The implementation of the RAG-based AI support bot revolutionized the NBFC’s internal support systems, delivering faster, more reliable assistance to employees. By automating repetitive tasks and enabling intelligent retrieval of complex information, the bot not only reduced workload on support teams but also significantly improved employee satisfaction. With its scalable and robust architecture, the solution is poised to evolve alongside the company’s growing workforce, setting a new standard for internal support efficiency.