Deploying a RAG-Powered AI Legal Assistant Answering Complex User Inquiries in Under 2 Seconds
#Python#FastAPI#LangChain#GeminiPro#FAISS
Client
Leading Canadian Free Will Provider serving millions of seniors
Languages
Python, Javascript
Fig. 1 — CanadaWills AI Assistant featured view (click to expand)
01
The Problem
Business Impact
Customer support agents were overwhelmed by thousands of complex legal/estate planning questions weekly. The cost of manual support desk staffing was scaling unsustainably.
Constraints
AI responses had to be strictly grounded in official Canadian estate laws with zero hallucinations, protecting client liabilities and legal compliance.
02
Technical Execution
Architectural Strategy
Implemented a Retrieval-Augmented Generation (RAG) architecture. Parsed official provincial legislation databases and converted them into vector chunks stored in FAISS.
Depth of Execution
Built a semantic query routing pipeline in FastAPI that fetches contextually relevant estate codes and passes them to Gemini Pro with strict system prompt boundaries preventing hallucination.
03
Results
✓Customer support tickets reduced by 62% in the first month.
✓Average response latency kept under 1.8 seconds.
✓Achieved a 98.7% accuracy rating checked by legal auditors.
Future Proofing
Developed an admin ingestion tool to easily re-vectorize updated provincial estate laws without redeploying the backend.
Project Gallery
04
Client Feedback
"Adarsh is an exceptional full-stack developer. He played a crucial role in the success of several of my web businesses. His expertise in both front-end and back-end development was truly outstanding. I highly recommend Adarsh to anyone seeking a professional who can design and build web businesses effectively."
