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SA-01 · AI · Fashion commerce · beta since 2026

Kultry

See it on you. Know your size. Before you buy.

An AI shopping companion that renders any outfit from any store onto your own body in ~20 seconds, then tells you your true size per brand, with an honesty badge on every render.

For: Indian online shoppers · brides, grooms, guests

kultry · try-onLIVE
honest
20.4s
Fit report · Manyavar
Size 40
Chestfits
Shoulderfits
Sleeve−2 cm
Lengthfits
“Sleeves may run 2 cm short. Chest is true to size.”
0s
per render
0
verified brand charts
0 min
to set up your twin
0.0/5
try-on quality gate

Why it exists

The truth about fit.

Kultry builds a digital twin from a few photos and your height, then photorealistically renders outfits from Myntra, Ajio, Mirraw, Manyavar, Kalki and any other store onto you, not a model.

A deterministic size engine checks verified brand size charts and gives candid callouts like “sleeves may run 2 cm short”. Every AI preview is judged against the real product photo and flagged if anything was invented.

The launch wedge is Indian wedding and occasion wear: sarees, lehengas, sherwanis and kurtas, where try-on is technically hardest and the cost of a wrong size is highest.

Capabilities

What's inside

Photoreal try-on in ~20s

Your body, your photo. Any product link or screenshot from any store.

Per-brand true size

Deterministic engine over 12 verified brand size charts, not LLM guesswork.

Honesty badge

A fidelity judge flags any render that added accessories or changed details.

Digital twin

AI-estimated measurements you can correct once and reuse everywhere.

Shopping agent

Chat to search, compare, size and try on. Tool-calling under the hood.

Curated looks

~90 wedding & occasion outfits ready for one-tap try-on.

Why it's different

  • Honesty-first: a fidelity judge on every render
  • Built for Indian occasion wear, the hardest try-on domain
  • Works across any store, not a single catalog

Built with

Next.jsFastAPIPostgres + pgvectorGeminiVertex AI Try-OnCloud Run

Every Slantaxiom product runs on the same opinionated stack: Next.js on Vercel in front, Python services on AWS ap-south-1 behind, Postgres underneath, tested backups always.

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