Across European fashion e-commerce, the same loop repeats every season: shoppers hesitate, ship two sizes, keep one, send one back. The reverse logistics, restocking, and write-downs sit on the merchant's side of the ledger.
Most sizing tools answer half the question. We answer both — what it looks like on you, and what size to pick — inside the same component on the same product page.
The shopper uploads one well-lit photograph. The garment is rendered onto their silhouette — not a generic avatar, not a model proxy. Drape, length, and proportion can be assessed directly on the product page.
Shopper measurements are matched against your brand's size chart and the garment's specific cut. Recommendations include a fit score and a fallback size when the shopper sits between two grades.
Try-on and size recommendation share one input flow, one UI, and one event stream. One snippet to scope, one product-page component to test, and one measurement plan to review.
Adjust monthly orders, current return rate, and the share of returns you expect to avoid. The annual estimate uses a configurable cost-per-return assumption; validate every input against your own logistics during the pilot.
These ranges are planning benchmarks, not forecasted FittingMe.ai outcomes. Your pilot should validate conversion, returns, and payback against your catalogue, traffic, and baseline return rate.
We validate outcomes through the agreed pilot measurement plan.
Most teams already evaluated a sizing solution two or three years ago. The category has shifted. Here's where the math has changed.
| Capability | FittingMe.ai | Classic sizing tools |
|---|---|---|
| Try-on rendered on shopper's own photograph | Native | Avatar or model only |
| Size recommendation cross-referenced with brand chart | Per-SKU | Generic grade |
| Single, unified widget on product page | One snippet | Two vendors, two integrations |
| Image retention terms | Scoped by deployment | Varies |
| A/B test instrumentation | Pilot dashboard | Self-serve at extra cost |
| Integration scope | Pilot-scoped | Varies by architecture |
Drop a single <script> tag into your product-page template after technical scoping. The widget is configured against your product data, size charts, consent flow, and analytics plan.
Photograph plus self-reported size. The widget pre-processes on-device, then renders the garment on the shopper's silhouette and returns a recommended size with a fit score.
Measure conversion lift, return-rate movement, and revenue per visitor against the agreed pilot plan. Export requirements are scoped with your analytics setup.
Designed for asynchronous loading and scoped product-page pilots. Integration details are validated with your platform, catalogue data, and consent requirements before launch.
<!-- 1 · An anchor where the widget mounts -->
<div id="fittingme-widget-container"></div>
<!-- 2 · One script tag — it builds the widget iframe -->
<script
src="https://cdn.fittingme.ai/fittingme-widget/loader/fittingme-loader.min.js"
data-container="#fittingme-widget-container"
data-api-url="https://api.fittingme.ai"
data-api-key="YOUR_PUBLISHABLE_KEY"
data-locale="fr"
data-product-id="YOUR_PRODUCT_SKU"
data-brand-slug="your-brand"
data-garment-type="dresses"
data-gender="female"
></script>A 30-minute live call to review a representative product page, integration constraints, and pilot measurement assumptions. You leave with a scoped next-step summary.