AI Embroidery Preview: How We Built TextileStudio.ai

An AI embroidery preview shows how an embroidery design will look on fabric before anyone stitches a physical sample. We built TextileStudio.ai, an in-house product of Rain Infotech, to give textile manufacturers that preview from a hand-drawn sketch or a short description, in minutes.

This post explains the problem the product solves, what it does today, the three technical problems that took the most work, and how we shipped it on the web, Android, and iOS. If you are planning an AI product for your own industry, the last section covers what this approach suggests for other markets.

The Problem: Paying for a Sample Before Approval

TextileStudio.ai is used mainly by embroidery and textile manufacturers in Surat, Gujarat, India: saree, blouse, lehenga, kurti, and dupatta makers, plus boutiques and designers. Before a design moves forward, these manufacturers usually need a dealer to approve it.

Without a realistic way to show the design first, the manufacturer has to spend money on a physical sample before knowing whether the dealer will accept it. A change request can mean another sample. TextileStudio.ai moves the dealer conversation earlier: the manufacturer shows a realistic preview first, and pays for a physical sample only after the design is approved.

Sample First vs. Preview First

The table compares the two workflows at each step.

What TextileStudio.ai Does

A manufacturer uploads a hand-drawn sketch or describes an idea, and the product uses generative AI to produce a realistic embroidery preview on fabric. The live features are:

• Sketch-to-design: turns a hand-drawn sketch into a realistic embroidery preview on fabric.
• Style transfer: applies a new visual style to an existing design.
• Multiple variations: generates several versions of a design from one input, giving the manufacturer options to show.
• Model view: shows a generated design on a model.

Building a product like this is generative AI development applied to one industry’s workflow. The model is only one part of it. The rest is making the output fit how the industry actually works, and that is where the three problems below came from.

Three Problems Behind a Usable AI Embroidery Preview

1. Rendering Real Embroidery Materials

Embroidery in this market uses materials such as zari, sequins, and cut dana, and each one has its own look. A preview that renders them loosely is not good enough to put in front of a dealer. We built TextileStudio.ai to render these materials at specific sizes. In Bead Pro, users can also control materials by percentage, so the preview reflects the material mix they intend to use.

2. Changing the Fabric Without Changing the Design

A natural question for a manufacturer is how the same embroidery would look on a different base fabric. In image generation, changing one part of an image can easily change other parts as well. We solved this so users can change the fabric under a design without altering the embroidery itself.

3. Accepting Gujarati and Hindi Input

Many people in this industry work in Gujarati or Hindi. TextileStudio.ai accepts design descriptions in Gujarati, Hindi, and English, so a user can describe an idea in the language they already work in instead of translating it into English first.

Shipping on Web, Android, and iOS

TextileStudio.ai is live as a web app, an Android app, and an iOS app. The main parts of the stack are:

• Web app: built with Next.js.
• Mobile apps: built with Flutter for Android and iOS.
• Login and messaging: OTP login and lifecycle messages through the WhatsApp Cloud API, across two numbers, in Gujarati, Hindi, and English.
• App services: Firebase for push notifications, analytics, and crash reporting.
• Measurement: GA4 and server-side Meta conversion tracking.
• Billing: a credit-based billing system.

The design output itself comes from AI image generation, wrapped in the product logic described above. As of October 2026, users have signed up from 27+ countries, mostly through Play Store discovery, including Pakistan, Bangladesh, and the UAE.

What Other Industries Can Take From This

TextileStudio.ai is one example of a vertical AI product: a general capability, image generation, shaped around one industry’s key decision. The same approach can carry over to other markets:

1. Start at the costly decision. In textiles, it is the money spent on a sample before a dealer says yes. Look for the equivalent step in your own industry.
2. Build the controls the industry needs. Material sizes, Bead Pro percentages, and fabric changes that leave the embroidery untouched are what make an AI embroidery preview usable for a business decision.
3. Fit the way users already work. Gujarati and Hindi input and WhatsApp login match how this market already communicates.

If you are planning a product like this for your own industry, our AI development team can help you scope where AI fits and what it will take to build.

Conclusion

TextileStudio.ai gives textile manufacturers an AI embroidery preview on fabric in minutes, from a sketch or a written idea, so they can seek a dealer’s approval before paying for a physical sample. Building it meant solving material rendering, fabric changes, and multilingual input, and then shipping it on the web, Android, and iOS for users in 27+ countries.