Self-Hosted LLM Chatbot for Insurance Integration Process

Project Overview

We partnered with an innovative insurance company focused on accelerating claim processing and reducing operational costs. Their existing workflow involved extensive manual effort, leading to inefficiencies, slower processing times, and high labor costs. To address these challenges, we developed a self-hosted large language model (LLM), designed to automate insurance integration tasks while ensuring unparalleled data privacy and compliance.

Key Challenges

• High Manual Workload: Insurance claim processing required employees to spend 80% of their time on repetitive tasks, slowing down service delivery.
• Data Privacy & Compliance: The company needed an AI-driven solution that adhered to strict data security regulations, ruling out third-party cloud-hosted LLMs.
• Scalability & Integration: The chatbot needed to seamlessly integrate into the company’s internal CRM system to handle customer requests and lead management.
• Edge Case Handling: While automation could reduce workload, human oversight was still essential for complex cases and regulatory checks.

Solution & Differentiation

Unlike traditional API-based automation or cloud-hosted AI chatbots, we built a self-hosted LLM chatbot fine-tuned for insurance-specific workflows. This approach provided:

• Data Privacy at the Highest Level - No third-party access to sensitive claim data.
• 50% Reduction in Manual Work - Employees now focus on critical cases instead of repetitive tasks.
• Seamless CRM Integration - The chatbot extracts, processes, and organizes customer data, streamlining lead management and request handling.
• AI-Assisted Human-in-the-Loop System - Automation covers routine processes, while employees intervene only for validation or handling unique edge cases.

Technologies Used

• PyTorch, llama.cpp - Fine-tuned & serve an open-source LLM to match insurance-specific data.
• FastAPI - Ensured high-speed backend performance for real-time chatbot interactions.
• PostgreSQL - Stored structured insurance claim data, user interactions, and lead information.
• LangChain & Langraph - Managed complex conversation flows and chatbot logic.
• Neo4j - Provided a graph-based data structure to handle intricate insurance processes.

Results

• 50% Reduction in Manual Work - Employees now validate claims instead of processing everything manually.
• Increased Team Efficiency - Staff focus on real issues rather than repetitive admin tasks.
• Full Compliance & Data Security - A self-hosted solution that meets strict industry regulations.
• Investment Secured - The AI-driven automation played a key role in attracting new funding for the company.

Scalability & Future Potential

The chatbot is already integrated into the company’s CRM system, automatically handling customer inquiries and lead management. Future enhancements could include:

• Multi-language capabilities (currently limited due to reliance on open-source LLM).
• Automated claims validation using AI-powered risk assessments.
• Fraud detection features to flag suspicious claims in real-time.

Why This Matters

By leveraging AI and automation, we helped our client reduce costs, improve efficiency, and scale their business, all while maintaining strict control over their data. Our expertise in LLMs and privacy-first AI solutions makes us the ideal partner for businesses in regulated industries looking to modernize their operations.