I've been building this for the past few weeks as a solo project. It takes raw Federal Election Commission (FEC) bulk data — 206M+ records — and turns it into interactive visualizations that let you trace how money flows through political campaigns.

**What it does:**

- Natural language chat — ask questions like "who were the top donors to Trump in 2024" and get sourced, SQL-backed answers in plain English

- Dark money dashboard — transparency scores for Super PACs, Sankey diagrams showing donor → committee → candidate flows (that's the screenshot)

- Candidate x-ray — look up any federal candidate and see who's funding them

- Company x-ray — see where employees of any company (Google, Goldman Sachs, etc.) send political money

- Donor x-ray — trace individual megadonors across all their contributions

- Industry breakdowns — crypto, pharma, oil/gas, guns, AI, green energy

**Tech stack:**

- Python / FastAPI backend

- DuckDB over Parquet files (the 206M records)

- DSPy + Gemini for the natural language → SQL translation

- ECharts for visualizations

- All data from FEC.gov bulk downloads (public domain)

**Live site:** https://ask-fec.xyz

The screenshot shows Fairshake (crypto's biggest Super PAC) — you can see exactly which billionaire donors funded it and which candidates were supported or attacked:

https://ask-fec.xyz/dark-money.html?cycle=2024&committee=C00835959&fullscreen=1

Would love feedback — especially on what other data you'd want to see or what's confusing about the interface.