Virtual Fitting Room Assistant
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Conversion & Catalog Experience
Virtual Fitting Room Assistant
Let shoppers see it on themselves before they buy.
An on-site AI try-on widget that lets customers visualize how apparel, eyewear, or accessories look on them, from an uploaded photo or a chosen body/skin profile, directly on the product page.
+15–25%
PDP conversion lift
−20–30%
fit-related returns
<10s
to render a try-on
The impact
Higher add-to-cart confidence means more conversions and far fewer size-and-fit returns.
+15–25%
PDP conversion lift
−20–30%
fit-related returns
<10s
to render a try-on
Who it's for
- Apparel, eyewear, and jewelry brands on Shopify doing ₹50L–₹10Cr/yr
- Catalogs where size or fit anxiety drives high return rates
- Brands selling on-model items they cannot photograph on every body type
- Teams that want conversion lift without a custom PDP rebuild
What you get
- Installed try-on widget via Shopify theme app extension
- Pose-and-segmentation render pipeline tuned to your catalog
- Size-recommendation logic mapped to your product size charts
- Try-on and add-to-cart event tracking wired into GA4
- Pilot lift report on your top 50 SKUs
The pipeline
How it works, end to end.
Every step is built, benchmarked, and wired into your stack. Here is exactly what happens.
Shopper opens PDP
A "Try it on" button is injected into the product template via the Shopify theme app extension, no theme rewrite needed.
Capture or pick a profile
The customer uploads a photo or selects body type, height, and skin tone; images are processed in-memory and never stored without consent.
Garment mapping
The product variant image and size chart are pulled from the Shopify catalog and aligned to the shopper using a pose-and-segmentation model.
AI render
A diffusion model composites the garment onto the shopper at the selected size, returning a photoreal try-on preview in seconds.
Size suggestion
Based on the chosen profile and the product size chart, the widget recommends a size and confidence level inline.
Convert & learn
Try-on events, chosen sizes, and add-to-cart outcomes flow back to analytics so the size model improves and returns drop over time.
Shopper opens PDP
A "Try it on" button is injected into the product template via the Shopify theme app extension, no theme rewrite needed.
Capture or pick a profile
The customer uploads a photo or selects body type, height, and skin tone; images are processed in-memory and never stored without consent.
Garment mapping
The product variant image and size chart are pulled from the Shopify catalog and aligned to the shopper using a pose-and-segmentation model.
AI render
A diffusion model composites the garment onto the shopper at the selected size, returning a photoreal try-on preview in seconds.
Size suggestion
Based on the chosen profile and the product size chart, the widget recommends a size and confidence level inline.
Convert & learn
Try-on events, chosen sizes, and add-to-cart outcomes flow back to analytics so the size model improves and returns drop over time.
Under the hood
The data flow, wired into your tools.
Reads
- Shopify product variants & images
- Product size charts
- Shopper-uploaded photo or body profile
- Historical return-reason data
Produces
- Photoreal try-on render
- Inline size recommendation with confidence score
- Try-on engagement events
- Fit-feedback signals for the size model
Before & after
What changes once it ships.
Shoppers guess their size from a static chart and bounce when unsure
Shoppers see the item on their own profile and add to cart with confidence
A quarter of apparel orders come back as fit returns
Fit returns fall 20–30% as size guidance improves with every order
On-model imagery only exists for hero SKUs
Every SKU gets an interactive on-self preview with no extra shoot
Why it matters
The business case
Fit uncertainty is the single biggest reason apparel shoppers bounce or return, and returns can eat 20–30% of fashion revenue once you count reverse logistics, restocking, and write-offs. A try-on that gives shoppers confidence at the moment of decision converts browsers who would otherwise leave, and the returns it prevents drop straight to the bottom line. On a store doing ₹2 Cr a year, a 20% cut in fit returns alone is often worth ₹15–25L.
FAQ
Virtual Fitting Room Assistant: your questions, answered.
What is a virtual fitting room and how does it work?+
It is an on-page widget that uses AI to show a customer how a product looks on their own body or face before buying. The shopper supplies a photo or a body profile, and a generative model renders the item on them in seconds. It works for apparel, eyewear, jewelry, and most wearable categories.
Do I need Shopify Plus to use it?+
No. It installs on any Shopify plan through a theme app extension and a lightweight script, so standard Shopify, Plus, and most headless setups are all supported. We also offer a custom embed for non-Shopify stacks.
How accurate is the fit recommendation?+
The size suggestion combines the customer profile with your actual product size chart and historical return data, so accuracy improves as more orders flow through. It is conservative by design: it flags low-confidence cases instead of guessing, which is what actually reduces returns.
What happens to customer photos and PII?+
Uploaded photos are processed in-memory to generate the render and are discarded immediately unless the shopper explicitly opts to save them. We are GDPR/DPDP-aligned, store no biometric identifiers, and can run rendering in your own region if required.
Does it slow down my product pages?+
No. The widget loads asynchronously and only initializes when a shopper clicks "Try it on," so core PDP load time and Core Web Vitals are unaffected.
Which product categories work best?+
Apparel, eyewear, watches, and jewelry see the strongest lift because fit and look uncertainty are the biggest conversion blockers there. We can scope a pilot on your top 50 SKUs to validate lift before a full rollout.
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