A person decides the match. Always.
What it replaces
Not judgement. The hours before the judgement.
The hours figure is an estimate, not a measurement. The audit measures yours, and if it turns out to be twenty minutes we will tell you the engine is not worth building.
The matching engine
It recommends. It never decides.
We build the algorithm and the agentic system that reads what you already hold: the carrier’s application and profile, the intended parents’ profile, and the history around both. It analyses the pair, ranks the candidates, and hands your coordinator a recommendation that arrives with its reasoning attached.
Not a score with no explanation. A named set of signals, what agreed, what did not, and what somebody should ask about before the introduction call.
Your criteria, not ours. The signals and their weighting are yours to set, and you sign them off before anything runs.
Illustrative record. Names and identifiers are blacked out because in a real one they never leave your systems.
The screen cannot approve a match.
This is a product constraint, not a policy we could quietly relax later. There is no auto-advance, no silent approval path, and no configuration that turns one on.
A match is two families and a pregnancy. The cost of getting it wrong is not a bad quarter, and no model should carry that.
- You approve the signals and their weighting in writing before anything runs.
- Every recommendation is logged with the inputs that produced it, so a decision can be reconstructed months later.
- Nothing is auto-rejected. A low-ranked pair still reaches a person.
- We review the distribution with you for patterns nobody intended.
Before you ask
Three questions we always get.
Will it match better than my coordinator?
Wrong question, and we would not claim it. It assembles faster and forgets nothing. Your coordinator brings the judgement, and now brings it to a complete picture instead of a half-read file.
What if we disagree with a recommendation?
Then you are using it correctly. Disagreement is logged too, and it is the most useful signal we get for tuning the weighting.
Whose data trains it?
Yours, for you. Nothing built for you is reused for anyone else, and nothing is trained on your data for use elsewhere.
Matching is stage five of six. The audit tells you whether it is your bottleneck.