AI Coffee Pairing
Using LLMs to suggest coffee origins and brewing methods based on your flavor profile and preferences.
The problem
Walk into any specialty shop and you'll find a wall of origins, processes, and roast dates — but almost no way to translate "I like bright, juicy, low-acid coffee" into an actual bag to buy or a brew method to use. Tasting notes on packaging are inconsistent, and most recommendation tools just upsell whatever's in stock.
The solution
AI Coffee Pairing takes a plain-language flavor description — or a set of cupping scores straight from the Cupping Tool — and matches it against a structured dataset of origin, process, and roast characteristics using an LLM prompted with SCA flavor wheel vocabulary. It returns a ranked shortlist of origins and a recommended brew method, with the matching water profile from Water Crafter already attached.
Tech stack
Timeline
Dec 2025
First prototype
Built a prompt-based recommender against a hand-labeled set of 40 origins using GPT-4.
Feb 2026
Cupping data pipeline
Connected Cupping Tool exports as a live reference dataset instead of static labels.
Apr 2026
Vector search
Moved matching to Qdrant embeddings so recommendations scale past a few dozen origins.
Jun 2026
Private beta (current)
Testing pairing accuracy with a small group of newsletter subscribers before a public launch.
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