AI-driven Conversational analytics from MongoDB Health Insurance records

This is a submission for the Built with Google Gemini: Writing Challenge What I Built with Google Gemini Our client is a digital health insurance company serving 26,000+ members across East Africa. Their Relationship Managers and HR partners had no way to query their own data without routing requests to engineers — meaning answers to routine questions like "How many wellness visits did Company X complete this quarter?" took hours or days. I lead my practicum team to build an AI-driven conversational analytics platform that lets non-technical staff ask questions in plain English and get instant responses — as tables, charts, and plain-English summaries — tested on development MongoDB data. Architecture: Classify → Generate → Execute The core insight was that sending a raw natural language query straight to an LLM and hoping for accurate MongoDB Query Language (MQL) output is unreliable in a healthcare context. So we split the work into two stages: Intent Classification — Gemini classifi