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AI-Driven Claims Assistant for Insurance
AI & Automation8 MonthsNationwide Insurance Group

AI-Driven Claims Assistant for Insurance

A custom LLM-powered solution enabling claims adjusters to query complex policy documents and legal files using natural language.

Overview

The story
behind it

The client’s adjusters were spending hours manually searching through thousands of policy variations and historical claim records. Keyhole Software developed a Retrieval-Augmented Generation (RAG) assistant that integrates directly with their document management system, providing instant, source-cited answers to adjuster queries.

75%
Search Time Reduction
96%
Answer Accuracy
15,000 Users
Adjuster Adoption
$4.5M
Estimated Yearly Savings
The Challenge

What stood in the way

Insurance policies are hyper-dense and context-dependent. Standard keyword search was failing to provide accurate answers. Furthermore, all data had to remain strictly on-premise or within a private cloud VPC to ensure policyholder privacy and compliance with insurance regulations.

Our Solution

How we made it happen

We built a private RAG pipeline using a vector database for semantic search and a private instance of a Large Language Model. The system included a 'Source Verification' feature that highlights exactly where in a 200-page document the AI found its answer, ensuring human-in-the-loop accuracy.

Key Results

Impact that speaks

12min

Faster Processing

The time required to review a complex claim decreased from an average of 45 minutes to 33 minutes.

100%

Data Privacy

Data never left the client's secure network, satisfying all regulatory and legal requirements.

25%

Reduced Appeals

Higher initial accuracy in policy interpretation led to fewer disputed claims and legal appeals.

Gallery

A closer look

AI-Driven Claims Assistant for Insurance gallery 1
AI-Driven Claims Assistant for Insurance gallery 2
Features

What we
delivered

Every feature was built with purpose, performance, and user experience at its core.

Private LLM Integration (VPC)
Semantic Document Indexing
Multi-format PDF/Email Ingestion
Source Attributions & Deep Linking
Role-based Access Control for AI
Continuous Feedback Learning Loop
Automated Claims Summarization
Real-time Policy Fact-Checking
Tech Stack

Tools & technologies

PythonLangChainPinecone (Private)Azure OpenAI (VPC)FastAPIReact.jsDocker
Timeline

From concept to launch

Phase 1

AI Ethics & Data Strategy

Defining security boundaries and selecting the optimal embedding models for legal text.

Phase 2

MVP Vector Indexing

Ingesting 50,000 pilot documents and tuning semantic search relevance.

Phase 3

Assistant Development

Building the React frontend and implementing the LLM orchestration layer.

Phase 4

Enterprise Rollout

Full-scale deployment with training sessions for adjuster teams across multiple regions.

FAQ

Common questions

Ready to start?

Let's build something remarkable together

We'd love to discuss how we can help bring your vision to life. Our team has extensive experience across AI, web, mobile, and cloud technologies.

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