Self-Hosted LLM Chatbot for Insurance Integration Process

Project Overview

We developed a self-hosted large language model (LLM) chatbot designed to guide users through the insurance integration process. The goal was to provide a secure, scalable solution that not only addressed privacy concerns but also offered custom functionalities, such as an admin panel and a lead-generation dashboard.

Technologies Used

The following technologies were employed in building the chatbot:

• PyTorch: For training and fine-tuning the large language model.
• FastAPI: Used to develop the back-end and API services, ensuring high performance and fast response times.
• PostgreSQL: Serving as the database for storing user data, interactions, and lead information.
• LangChain & Langraph: Leveraged to orchestrate the logic behind the LLM and manage conversation flows.
• Neo4j: Utilized for storing graph-based data structures, especially in handling intricate insurance processes and integrations.

Challenges

Some of the primary challenges included:

• Self-hosting the LLM: Ensuring the system was self-hosted to guarantee full control over the data and compliance with strict data privacy regulations.
• Custom features: The project required building an admin panel and lead-generation dashboard tailored to the client’s business needs.
• Scalability: Designing a system that would grow with the company as it secured more users and data processing needs increased.

Results

Our team successfully delivered a self-hosted LLM chatbot that:

• Met all client requirements: The chatbot was fully customized to handle insurance integrations, supporting both users and administrators.
• Secured investment: The success of the chatbot played a key role in helping the client secure another round of investments, thanks to its effectiveness and scalability.
• Ensured data privacy: The self-hosting approach allowed the client to maintain full control over user data, a critical aspect in the highly regulated insurance industry.

Industry

Insurance – focusing on integration processes and user support through AI-driven chat interfaces.

Competitor Differentiation

Our approach emphasizes not only building a functional product but also ensuring scalability and data privacy, which are critical for insurance startups. By self-hosting the LLM and providing custom-built tools, we helped the client differentiate themselves in the market and prepare for further growth and investment.

Long-term Impact

The solution delivered by our team will continue to support the client's growth by offering a scalable platform for managing insurance integrations and improving user experiences. The chatbot is positioned to adapt as the company expands, helping it remain competitive and secure additional investments in the future.