Turn the data you already have into a decision you can defend.

Built from 44,648 peer-reviewed papers. Maps 5,301 distinct molecules across 852 ingredients into 296,732 computed relationships, indexed against a ~610,000-recipe corpus, with UMAP cross-category bridges surfacing 450 standout ingredients.

Pulls Reddit, Google Trends, RSS, and Nitter into a FastAPI backend with SQLite scheduling. Claude-powered post drafting and an interactive review dashboard close the loop.

Public JSON endpoints, Flask dashboard, email alerts, background refresh, and intent-weighted keyword scoring with competitor-switching signals.

Props engine with decay-weighted rolling averages, opponent and pace adjustments, Kelly stake sizing, and Odds API integration. Proprietary edge detection layered on top.

Natural-language brief to a working formula: character-impact compounds, OAV-ranked, starting ratios in ppm, mapped to real vendor SKUs, with an iteration rubric. Lets small food brands reason at the molecular level without a flavor house.

Turns one recording into the same voice speaking every language. Transcribes to timed segments, translates each with Claude, re-speaks with real cross-provider failover, times it back to the source, and stores every result with a hash-verified provenance manifest.

Reads skin, hair, and eye color from one selfie, computes undertone, depth, and contrast in CIELab, and returns a season, a palette, and the exact reasoning behind it. Digital draping puts a best color and a draining one side by side on your own photo.