Board Coffee Match

The challenge

Closed business communities have a recurring problem: members don't know who they should meet. Random networking wastes everyone's time, while the most valuable introductions — a founder and the exact investor they need, a buyer and the right supplier — never happen because no one can see the whole graph.

What we built

Board Coffee Match runs monthly matching rounds. Members fill a short survey about their goals, expertise, and what they're looking for, and the platform generates 1-on-1 coffee-meeting recommendations, delivers them through a Telegram bot, and tracks whether the meeting actually created value.

• Survey intake and member profiles
• A two-layer matching engine (see below)
• Telegram delivery with accept / decline / schedule
• Admin dashboard to run rounds, review and edit pairings, publish results
• Post-meeting feedback loop to measure real outcomes

The role of AI

Matching is not a simple similarity search — similarity actually produces bad networking ("you're both in IT in Kyiv"). The engine works in two layers:

1. Embeddings (OpenAI) turn each member's profile and answers into vectors, pre-filtering thousands of possible pairs down to the strongest candidates.
2. Claude ranking then judges each candidate pair against a strict rubric — rewarding genuine complementarity (buyer↔seller, investor↔founder, mentor↔mentee), rejecting direct competitors, and scoring the concrete value to each person. Every match comes with a short human explanation and three conversation starters.

The whole pipeline is chunked to run reliably for 100+ member rounds within serverless time limits, persisting intermediate state so nothing is lost.

The result

Members get introductions that feel hand-picked, with a reason and an icebreaker attached — and the community gets data on which match types actually drive business value.

Stack: Next.js, Supabase, OpenAI embeddings, Claude, Telegram Bot API.