Most booking systems match on availability alone, first come first served. This one matches on fit. A conversational agent gets to know the person booking, reasons over background and shared interests against a live pool of providers, and hands back a ranked shortlist with a real reason for each pick. Built here around teacher matching, but the matching engine has nothing to do with tutoring specifically.
The problem
Any business pairing customers with a specific person, not just an open seat, runs into this. A salon matching clients to stylists. A staffing agency matching candidates to shifts. A coaching business matching clients to consultants.
Usually it's a form and someone manually scanning a list, or no matching at all — whoever answers first. Either way, fit gets left to chance, and mismatches quietly cost repeat business.
"Fit gets decided by whoever answers the phone first, not by who's actually the right person for the job."
The solution
A short conversational onboarding collects who's booking, their background, and what they're into. That gets passed to an AI agent that pulls the live pool of providers, weighs subject fit, context fit, and genuine shared interests, and explains its reasoning in plain language for each match — not just a score.
Every field in the onboarding chat is validated before it's sent, no empty submissions, no guessing what the agent meant. Every match comes back with a real photo, a plain-language reason, and a live booking link, ready to render straight into the UI with no parsing required.
Try it
This is the actual onboarding flow, running against a live sample data pool and a real agent. Try it as yourself, or on behalf of a child — the questions adapt either way.
Built to be swapped
The engine doesn't know it's matching teachers. It knows how to weigh a person's needs against a pool of providers and explain the fit. The data source is interchangeable — a Postgres table, a ClickUp board, a live spreadsheet, whatever the business already runs on. Swap the source and the prompt's vocabulary, and the same build becomes:
Same reasoning pattern, different data source, different niche.