Adding Virtual Try-On to Your Own Fashion App With a REST API

To add virtual try-on to your own fashion app, your app collects a shopper's photo and height, sends them to a REST API from your server, and shows the returned size recommendation and try-on image on the product screen. The API does the AI work, so you build the interface, the data handoff and the checks around it.

This guide walks through what to prepare, how the flow fits together, and what to test before launch. It suits developers on a custom storefront, a WooCommerce or Magento build, or a native mobile app.

What does a virtual try-on REST API actually do?

It takes two jobs off your plate: sizing and visualising. Sizing means estimating the shopper's measurements and matching them against your size chart. Visualising means rendering your actual product on their body.

With ZeDaq.ai, the shopper uploads one photo and enters their height. Their measurements are matched against your store's size chart to recommend a size. Virtual Try-On then renders your real product on their body, shown in the product carousel.

Your app never needs its own computer vision model. It needs a clean way to send inputs and display outputs.

What do you need before you write any code?

Most integration delays come from missing data, not missing code. Gather these first:

• A complete size chart for each product or product group, in consistent units.
• Good product images that show the garment clearly, since the render uses your actual item.
• A photo and height capture screen with clear instructions for the shopper.
• A consent message explaining what the photo is used for.
• A server environment where API credentials can stay private.

Size charts deserve the most attention. A recommendation is only as good as the chart it is matched against. If your "Medium" differs between product lines, fix that before you integrate.

How should the integration flow work in your app?

Keep the flow simple and put your server in the middle. Here is a sensible shape:

1. The shopper opens a product and taps a try-on button.
2. Your app asks for one photo and the shopper's height.
3. The app sends both to your own backend, never straight to a third party with embedded credentials.
4. Your backend calls the ZeDaq.ai REST API with the photo, height, and the product and size chart details.
5. Your backend returns the recommended size and the try-on image to the app.
6. The app shows the result in the product gallery and preselects the recommended size.

The shopper does not need a ZeDaq.ai account or any sign-in. They stay inside your product page or screen the whole time.

For exact endpoints, request fields and authentication, use the API documentation you receive when you sign up. Build against that rather than assumptions.

Should you use the widget or the REST API?

It depends on how much control you want over the interface. Both give shoppers the same core experience.

Your setup	Best route	Why
Shopify store	Shopify App Store app	Installs without custom code
Any website with editable templates	Embeddable widget	One script tag pasted into the product page template, no redesign
WooCommerce or Magento with custom needs	REST API	Full control of design and data flow
Custom storefront	REST API	Fits your own architecture
Native or hybrid mobile app	REST API	The widget is for web pages, so your app builds its own screens

If a script tag would do the job, start there. Choose the REST API when you want the try-on to feel native to your own app. The how it works page shows the shopper journey you would be recreating.

How do you design the shopper experience around the API?

The API is the easy part. The experience decides whether shoppers finish the flow.

Ask for as little as possible. One photo and one height value is the whole input. Resist adding extra fields.

Coach the photo. Show a simple example: full body, good light, plain background if possible, fitted clothing. A short tip before the camera opens saves failed attempts.

Plan for waiting. Rendering an image takes a moment. Show a clear loading state and let the shopper keep browsing rather than freezing the screen.

Handle failure kindly. If a photo is unusable, say why and invite another try. Never show a raw error message.

Show the size, then the image. Shoppers want to know what to order. Display the recommended size prominently, then the try-on render in the product carousel beside your normal photos.

Respect language and currency. The ZeDaq.ai storefront widget speaks 10 languages, and billing supports USD, NGN, GBP, EUR and more. In a custom app, make sure your own labels and prices match the shopper's locale.

What should you check before launch?

Run through this checklist with real devices and real products.

• API credentials live only on your server.
• Photo upload works on slow mobile connections, which matters for many shoppers in Nigeria and across the UK on the move.
• Large phone photos are handled without timeouts.
• Every product that shows the try-on button has a size chart attached.
• The consent message is visible before upload.
• You decided how long photos are kept on your side, and you state it.
• Loading, error and retry states are all designed.
• The recommended size preselects correctly in your add-to-basket flow.
• Out-of-stock sizes are handled sensibly when a recommendation lands on one.
• You tested with a range of heights, body shapes and skin tones.

Start with a handful of products, watch how shoppers behave, then widen the rollout.

Can tailors and made-to-measure businesses use this too?

Yes, though the focus is different. If you make clothes to order, you may care less about shopper try-on and more about running your business in one place.

Every ZeDaq.ai subscription includes tools for tailors: a client CRM, orders with deposits, a delivery calendar with automatic reminders, and invoicing with your logo and bank details sent by email or WhatsApp. Measurement capture uses a front and a side photo, finds 33 skeletal points, and is calibrated with a sheet of A4 paper. You can also send a measurement link the client opens on their own phone, or type measurements in by hand.

If your records still live in paper books, you can photograph a page and have the client's details and measurements read into a digital profile. See the tailor tools page for more.

ZeDaq.ai is the technology platform. It does not make, sell or ship clothes.

How do you try it before committing?

There is a free plan and a 3-day free trial with no card required. Paid plans differ by monthly try-ons, measurements and invoices, so the right one depends on your traffic. Create an account at zedaq.ai signup, test the flow on a staging copy of your app, then decide.

Frequently asked questions

Do shoppers need to create an account to use try-on?

No. The shopper uploads a photo and enters their height inside your product page or screen, with no ZeDaq.ai account or sign-in.

Can I use the REST API on WooCommerce or Magento?

Yes. Stores on WooCommerce, Magento, a custom storefront or their own app can integrate directly with the REST API. If you prefer less code, the widget works on any website with one pasted script tag.

Does the try-on use my real products?

Yes. Virtual Try-On renders your store's actual product on the shopper's body, so the shopper sees your item rather than a generic stand-in.

How is the recommended size chosen?

The shopper's measurements are matched against your own size chart. That is why an accurate, consistent chart matters before you integrate.

Is there a cost to start building?

There is a free plan and a 3-day free trial with no card required. For plan details, check the pricing page on the ZeDaq.ai site rather than relying on a quoted figure.