Better fit. Higher conversion. Fewer returns.

The fashion industry has spent years investing in sizing tools, virtual try-ons, and recommendation engines — yet return rates remain stubbornly high.

That’s because most solutions focus narrowly on measurements alone.

Research behind Idntfy showed that returns are driven by a combination of factors:

- inconsistent sizing between brands

- uncertainty around fit and silhouette

- lack of garment-specific production data

- shopping behaviour shaped by low confidence and guesswork

Most tools help customers choose a size.

Idntfy helps them understand whether a garment is likely to work for them in the first place.

Idntfy combines millimetre-accurate body scan data with real garment production data to deliver more reliable sizing recommendations.

Our system takes into account:

- fabric stretch

- drape and stiffness

- intended bagginess

- garment ease

- silhouette and cut

- brand-specific fit philosophy

This creates recommendations that go far beyond basic measurements or generic size charts.

Idntfy gives shoppers personalised virtual try-ons and sizing guidance directly on the product page. Using body scan data together with real garment production data, our system takes into account fabric stretch, drape, silhouette, intended bagginess, and brand-specific fit philosophy — going far beyond standard size charts.

Brands can work with their own fit models or use Nordic-market fit models developed with Nina Skarra’s fashion expertise.

Idntfy users keep one persistent body profile that works across participating brands and platforms. This creates a smoother shopping experience while helping brands benefit from stronger fit understanding and higher customer confidence.

We already have a growing base of users who have completed body scans and are actively ready to explore sizing and virtual try-ons across new brands joining the platform.