From the matchmaker's desk

How we built the matching system, what each signal contributes, and where its limits matter.

Written by Find Your Person. Product-mechanics articles are checked against the current implementation and public policies; planned behavior is labeled as planned.

Why we do not start with a profile stack.

Find Your Person asks for more context first, then uses it to recommend a smaller field of potential introductions.

What photo comparisons can teach a matching system.

Pairwise choices can personalize visual-preference signals. They cannot guarantee attraction, and they should not be treated as a popularity score.

What a modeled conversation can—and cannot—predict.

The simulation is a useful compatibility signal, not a rehearsal of the future and not a guarantee of chemistry.

How our AI matchmaking system uses six signals.

A transparent look at the baseline weights, reciprocal gate, and why no single model output decides an introduction.

What does reciprocal matching mean?

Why “you fit their preferences too” is a separate check—and why even two passing scores cannot promise mutual attraction.

Seven privacy questions to ask an AI dating app.

A practical checklist for interviews, voice data, photos, biometrics, messages, model providers, and account deletion.

Why a matchmaking app launches city by city.

Reciprocal preferences and local density make geography part of the product—not just a marketing rollout decision.

Questions to ask before signing a matchmaking contract.

A practical checklist for the price, the pool, the person, and the paperwork — before you sign anything.

Why we publish our pricing.

Transparent pricing is not a marketing feature. It is the whole argument for building a matchmaker as software.