FinPulse — Text-to-SQL AI Analytics Copilot for Fintech (Gemini + PostgreSQL)

Ask a neobank dataset a question in plain English. Get a safe read-only SQL query, a table, a chart and a written summary back.

The problem

Every fintech has the same bottleneck: business users have questions (“what is the fraud rate by MCC this quarter?”) and analysts have a queue. Handing an LLM write access to a production database is not an option.

What I built

• Text-to-SQL with a hard safety boundary. Generated SQL is constrained to read-only, validated against the schema before execution, and runs under a least-privilege role. No DDL, no writes, no unbounded scans.
• Schema-aware generation using vector search over table and column descriptions, so the model retrieves the right part of a wide schema instead of being handed everything at once.
• Results as tables, charts and prose — Gemini writes the narrative summary on top of the actual query result, not on top of its own guess, which keeps the numbers honest.
• Built against a synthetic neobank dataset (accounts, transactions, merchants, MCC codes, fraud labels) so the demo is public and no real customer data is involved.

Stack

Gemini, FastAPI, PostgreSQL, pgvector, Next.js, Docker.

Live demo, GitHub source and architecture notes: https://www.twine.net/signin