Matching is the engine that makes a marketplace more than a listings board. Without it, a buyer faces the whole market and a seller faces the whole buyer pool, and both waste effort on poor fits. The matching and appetite-scoring engine does that filtering once, computationally, on every buyer-listing pair — so each side sees only what fits. This is how the scoring works.
What fit means.
Fit is not a single number; it is a set of signals, each answering a concrete question about whether the book would transfer and perform in the buyer's hands.
| Signal | Question it answers | Weight |
|---|---|---|
| Appointment overlap | Does the buyer carry the same carriers, so the book transfers cleanly? | Binary · high |
| Line-of-business mix | Does the book's commercial/personal balance fit the buyer's appetite? | Binary · high |
| Geographic fit | Is the book in or adjacent to the buyer's footprint? | Binary · high |
| Premium-volume band | Is the book the right size for the buyer's capacity? | Refining |
| Retention threshold | Is the book's retention above the buyer's floor? | Refining |
Most of the signal.
Three of those signals are binary gates that carry most of the weight: appointment overlap, line-of-business mix, and geographic fit. They are binary because they are mostly pass-or-fail in practice — a buyer either carries the carriers or doesn't, operates in the geography or doesn't, wants the line mix or doesn't. A listing that clears all three is a genuine candidate; one that fails any is usually not, regardless of how attractive it looks on price. The premium-volume band and retention threshold then refine the surviving candidates into a ranked shortlist.
Tension without shopping.
The same scoring runs in both directions, and that symmetry is what produces fair outcomes. For the buyer, it turns the whole market into a fitted shortlist — a tuck-in acquirer who would otherwise scroll past thousands of listings sees the handful, sometimes Slices, that match their thesis. For the seller, it surfaces the listing to exactly the buyers it fits, which produces genuine competitive tension without the seller ever having to shop the book around or expose it broadly.
That is the quiet power of computed matching: the seller gets tension and the buyer gets fit, from one scoring pass, without either side doing the other's work. The data the engine scores on comes from the ingestion pipeline; the outputs feed the marketplace app; and the buyer-side strategy that exploits good matching is covered in the buyer theme's deal sourcing cluster.