About Client

The client is one of India’s largest life insurance companies and the largest non-bank private-sector life insurer in the country. It is a joint venture between a prominent Indian conglomerate and a major Indian multinational bank, offering a wide range of life insurance products to millions of customers.

Objective 

The client sought to enhance customer experience and eliminate the confusion and mistrust caused by lengthy, complex policy documents by introducing an AI-driven tool that delivers clear summaries and instant answers to customer queries. The initiative aimed to strengthen transparency, improve customer engagement, and reduce the burden on service teams. 

Solution 

Blazeclan developed the Policy Simplifier Application, powered by advanced document parsing and Agentic AI, with the following key features: 

Automated Policy Analysis 

• Scans the complete policy document and generates a comprehensive summary including insured details, premium, maturity amount, payment terms, payout style, beneficiary details, and plan specifics. 

• Identifies advantages and disadvantages of the policy, highlighting hidden clauses, rules, exclusions, and fraud/misstatement clauses. 

• Provides personalized product recommendations based on the customer’s profile and needs. 

Bima Buddy – AI-Powered Virtual Assistant 

• Delivers real-time responses to customer queries using a knowledge base combined with the uploaded policy document. 

• Supports both basic (e.g. Who is the nominee?) to how-to (e.g. How do I change my nominee?) queries with step-by-step guidance. 

FAQ & Knowledge Base Integration 

• Offers a centralized repository of commonly asked questions for quick self-service. 

Outcome 

• Enhanced Customer Clarity: Customers can now understand complex policy terms in minutes. 

• Boosted Trust & Transparency: Clear visibility into hidden clauses and terms improved customer confidence and satisfaction. 

• Faster Query Resolution: Real-time AI assistant reduced dependency on call centers, improving service turnaround time. 

• Personalized Experience: AI-driven recommendations increased cross-selling and up-selling opportunities. 

• Operational Efficiency: Reduced manual effort for policy explanations, freeing up agent bandwidth for higher-value interactions. 

Tech Stack

Front End:  

• React js – Used to build the interactive, responsive web interface 

Infrastructure (Frontend): 

• AWS S3 -> Cloudfront – Hosting and fast content delivery for the frontend 

Backend: 

• Middleware: Fast API – Layer to handle API requests between the front-end and  

Backend Logic: 

• DB: RDS Postgres 15.7 [pgvector] – Stores policy data and AI embeddings 

• Model: OpenAI – Powers Summarization, Q&A, and recommendations 

• Agentic Framework – Agno AGI: Manages AI agents’ workflows 

Infrastructure (Backend): 

• Amazon S3 – Stores uploaded policy documents for processing. 

• API Gateway – Secure API access and routing 

• RDS – Primary database hosting for structured policy and customer data. 

• EC2 /ECS – Runs backend services, AI models, and processing pipelines in scalable compute environments.