Every wig without a try-on is a sale you didn't make. Every shade without a photo is the rest.

Wig Virtual Try-On is the preview a product photo can't deliver: a shopper uploads a selfie and instantly sees their future self in the wig - how it fits, how the color sits against their skin tone, and how the style suits their face. Wigs are a high-investment purchase, and the question "how will it look on me?" is what stops people from buying. Virtual Try-On answers it before the cart, so shoppers commit with confidence.

The 48-hour promise is a working demo on your own products. You send us three product links from your store, we integrate them into Virtual Try-On, and within two business days you can see your own wigs rendered on a real selfie - concrete evidence on your own catalog before committing to a full rollout.

Yes - the install is a single script tag with your API key in your site's footer (paste it directly or push it through Google Tag Manager). We use your existing product feed - the one already powering Google Shopping or similar - to load your catalog into the TWIWT dashboard, and our team places the try-on buttons on your product pages, so no developer work is required from your side. The widget runs on product pages by default; we help integrate it on collection or search pages if you want it there too.

There's no practical limit on catalog size - we work with catalogs of 10,000+ products without issue. New products you add to your existing feed appear automatically in your TWIWT dashboard, where you choose which ones to activate for virtual try-on. Each variant - every colour and length combination - is treated as its own try-on entry, so you have full control over which exact SKUs your shoppers can try on.

Yes. Every generated try-on has a regenerate button in your dashboard. When you flag one as off, you pick a reason from a short list - wrong colour, bad fit, unrealistic rendering - and a corrected version is produced. The categorised reason stays attached to the original generation, so issues are tracked, not lost.

Hair is complex - colours shift along the length, with balayage, highlights, and roots - so for the closest match we combine the wig's style with a reference photo of the actual product, extracting the real highlights, shadows, and root distribution rather than relying on a single colour code. Cap fit is handled separately by our automatic head measurement tool, which sizes each shopper to the right cap.

AI is probabilistic - we don't claim 100% perfection. Roughly 1 in 10 renders may not perfectly capture length or colour distribution, which is exactly why every generation has a regenerate button to flag and re-render.

Yes, the result is the same as for any other shopper. Our pipeline always removes the shopper's existing hair as a first step (so someone with long hair can try a pixie cut, for example), so a bald or partially bald shopper isn't an edge case at all. The flow stays identical for everyone: pick a colour, then upload a photo, take one with the camera, or choose one of six default models.

The minimum requirement is a detectable face - the AI uses facial landmarks to place the wig accurately, so a clear, front-facing photo works best. Before the shopper takes or uploads a photo, the widget guides them on what works (front-facing, hair away from the face, good light), so most photos come in usable. We also have guardrails that prevent generations on offensive content or photos of recognisable public figures. Beyond those guardrails, the experience is intentionally permissive to keep the shopper flow smooth.

Quality parity is something we test for actively. Our dataset covers men's and women's hairstyles, varied ethnicities, and skin tones across the spectrum, evaluated at the front-facing angles the shopper widget asks for. The result is consistent quality regardless of a shopper's skin tone, hair texture, or gender.

We treat selfies as sensitive personal data and minimise everything we hold.

• Storage - The photo is sent to our Cloudflare EU cluster and processed by AI partners under contracts that forbid them from training on it. It never leaves the EU. No internal tool lets us browse or view shopper photos - no one on our team looks at them as part of normal operations.
• Retention - Sessions are automatically wiped 24 hours after creation. The photo, derived face landmarks, and every generated asset are deleted, leaving only an anonymised record of which product was tried at what time. Right-to-be-forgotten is built into the system - there's nothing left to delete after a day.
• Training - Shopper selfies are never used to train our AI models. The shopper's consent screen states this explicitly: "AI partners are contractually forbidden from using it for training."
• Compliance - Infrastructure is fully EU. A DPA template is available on request. The widget shows a clear consent screen before any upload, with the no-training clause and retention policy in plain language.
• Sensitive content - Our guardrails detect and reject inappropriate or unsafe uploads before generation.

Yes - you own every asset generated from your catalog. You're responsible for the upstream rights (your source photos and the consent of any people depicted), and the upload screen makes that explicit before any generation runs. We provide the editing and generation; the rights and marketing reuse are yours.

Catalog assets generated in your B2B dashboard carry no visible watermark, so they're ready to use in paid ads, email, print, lookbooks, or in-store displays. They include C2PA provenance metadata - the open standard for cryptographic AI-content provenance now expected under the EU AI Act. Shopper-facing virtual try-on results are different: they carry both a visible watermark and C2PA metadata, because those are previews for the shopper, not catalog assets, and shouldn't be repurposed as marketing photography.

We wire into your existing analytics stack so you can attribute lift on your own traffic. Conversion events to GA4, Meta CAPI, Klaviyo or Segment are set up during integration on request, and most partners run their own holdout test in Optimizely / VWO / similar to compare a try-on-on vs. try-on-off variant.

The +34% / -18% figures on our page reflect early-partner reports rather than a formal A/B study - the most reliable number for your catalog will be the one you measure on your own traffic. We'll set up the tracking to make that easy.

Speed. A first try-on takes about a minute; each additional generation in the same session takes around 45 seconds. The platform auto-scales, so peak traffic doesn't queue or degrade.

Languages. The shopper-facing widget supports 20 languages today. The B2B dashboard is English-only at the moment, with translations on the roadmap.

Support. Every retailer has email support; larger / enterprise partners are assigned a dedicated support specialist.

We tailor pricing to each retailer based on the scope of work. Reach out via the form above and we'll prepare a quote for your store.

Most virtual try-on tools were built for makeup or eyewear and retrofit later for hair. TWIWT was built for wigs from day one - our pipeline understands wig-specific concerns like cap construction, hairline placement, density, and how a style behaves on different face shapes. That's why our renders feel like the actual product, not a generic hair filter.