v0.1 draft · open standard · a personal project by antferr
Every size-recommendation service works — and keeps your profile captive. Agio Fit is the opposite: an open data model where your measurements live in a vault you choose, and shops only ever get a size.
The problem
A large share of online fashion returns are size-related. The market's answer is bracketing: order three sizes, keep one, send two back.
Size recommendation works — but in every existing implementation the profile belongs to the vendor or the retailer. You rebuild it, implicitly, at every shop you visit.
Agentic commerce protocols are standardising catalogue, checkout and interfaces — but not fit. The assistant that will buy clothes on your behalf would, today, guess your size from nothing.
What it is
You own the first. The seller publishes the second. The third is the only thing that travels between the two — confidence exposed, per-zone reasoning, never a size handed down from a black box.
— yours
Body measurements with declared source and uncertainty, fit preferences, and the history of how past garments actually fitted you.
schemas/v0.1/fit-profile.schema.json— the seller's
How the garment is cut, not what the brand calls the size: finished measurements per size, intended ease, stretch, data provenance.
schemas/v0.1/cut-profile.schema.json— what travels
A size, the alternatives, zone-by-zone reasoning and an honest confidence — decomposed, capped when data is thin, never overstated.
schemas/v0.1/match-report.schema.jsonIn practice
The fitting room in your pocket: a QR code on the label points to the Cut Profile. Scan it and the match runs on your phone — you know whether it fits before trying it on, and nothing leaves for the shop. Not even the size.
The same gesture where no fitting room exists: online the garment can't be tried on, but its cut is published and your profile answers — with confidence exposed and per-zone reasoning, not a promise.
Two garments that publish their cut can be compared with each other: a retailer can tell you this jacket is cut like the one you kept. A garment-to-garment comparison that never touches your measurements.
Privacy by design
In the in-store scenario nothing leaves: the computation happens entirely on your side. When something does need to leave — an online shop, a return, your tailor — the answer is graduated: from the full profile down to a size and nothing else.
, i valori di agio vengono rimossi alla serializzazione. The arithmetic leak is why the lower levels exist: publishing ease in centimetres next to the garment's measurements would reveal body measurements by subtraction. Below scoped, ease values are stripped at serialisation. , les valeurs d'aisance sont retirées à la sérialisation.
Scope
Not a size-recommendation vendor. The argument is ownership and portability; becoming another silo would destroy it.
Not an identity system. The profile lives in a vault the person chooses; authentication and custody are deferred to existing wallets.
Not a scanning technology. The model accepts measurements from any source — and declares their quality instead of pretending it uniform.
Not a size-chart aggregator. Charts say what a brand calls a size, not how the garment is cut.
Not a protocol. It is a data model: it works over a plain HTTP API, and the algorithm is not normative — the shape of the answer is.
Status
v0.1 draft Nothing is stable before 1.0. One commitment already holds: schema identifiers stay resolvable, and breaking changes bump the version instead of mutating a published schema.
Specification under CC BY 4.0, reference implementation under Apache 2.0 — zero runtime dependencies, so the privacy invariant can be verified by reading the code in an afternoon. The most valuable contribution? A body and a garment where the answer is obviously wrong, as a failing test.