40% of Returns are a Fit Problem.
Manikan gives your shoppers AI-powered size recommendations and virtual try-on embedded in your store with a single script tag. No engineering changes. No new app.
Wrong sizes cost everyone.
Of all online fashion returns trace back to sizing confusion — not defective products.
Wasted globally on returns processing, inspection, and restocking every single year.
One HTML script tag. That's all it takes to add AI size recommendations and virtual try-on to any storefront.
One Line of Code.
Zero Engineering.
Manikan is a standalone widget bundle that works on Shopify, WooCommerce, or any custom storefront. Drop in the script tag and your shoppers instantly get AI-powered try-on.
Upload your catalog
Share your CSV product catalog — we ingest it, generate embeddings, and index your size charts.
Embed the widget
Add one script tag to your product pages. Widget inherits your store's colors automatically.
Watch returns drop
Shoppers get AI size recommendations and virtual try-on. You get fewer returns and more confident buyers.
<!-- Manikan AI Widget -->
<script src="https://cdn.manikan.io/widget.js"
data-retailer-id="ret_8f92jK"
data-product-id="prod_44mA2">
</script>Everything you need, nothing you don't.
AI Size Recommendation
LangGraph agent reasons over your size charts and the shopper's body measurements to recommend the right size with a natural language explanation.
Virtual Try-On
Shoppers upload a photo and see themselves wearing the actual garment fabric, color, print and all before they buy.
Retailer Dashboard
Track widget usage, recommendation accuracy stats, and catalog performance from a dedicated analytics dashboard.
Catalog Ingestion
Upload your product CSV once. We parse, validate, and index your size charts automatically. Update anytime from the dashboard.
Ready to cut returns and boost confidence?
Tell us about your store and we'll reach out within 1–2 business days to walk you through a personalized demo.
Request a Demo
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Privacy-first by design
Shopper photos are processed entirely in-memory and deleted from our systems immediately after the try-on inference completes. We never log, store, or train on raw user photos.