Launch Alert – PointAI launched the Industry's First In-Store Virtual Try-On Standee at Bharat Tex 2026 in Bharat Mandapam on 14th July
PointAI is building the AI infrastructure that powers the future of fashion commerce—from product digitization and catalogue creation to personalized shopping experiences across every channel.
At Bharat Tex 2026, we showcased PointAI's AI-powered Virtual Trial Room, where visitors created their own Virtual Twin, scanned garments, tried multiple outfits in seconds, and experienced the future of fashion retail—all without stepping into a physical trial room.
The response reinforced what we've always believed: the Future of retail isn't online or offline—it's connected, intelligent, and personalized.
Why Does Every Offline Retail Store Need PointAI’s In-Store Virtual Try-on Solution?
The retail industry has rapidly adopted AI across e-commerce, from product recommendations to virtual try-on experiences. However, the in-store shopping journey has remained largely unchanged. Customers still wait in long trial room queues—especially in stores like H&M and Uniqlo—carry multiple sizes and repeatedly change outfits before making a purchase.
As retailers strive to deliver faster, more personalized shopping experiences, the traditional trial room has become a bottleneck rather than an advantage.
PointAI's In-Store Virtual Try-On, powered by Simulation AI, bridges the gap between physical retail and digital commerce by creating one connected shopping experience across both online and offline channels.
Unlike conventional smart mirrors or image-generation tools, PointAI creates an exact Virtual Twin using a customer's body measurements and proportions in seconds, enabling faster, more personalized shopping at enterprise scale.
Inside the In-Store Virtual Try-On: How It Works
The fitting room experience is moved directly to the retail floor via interactive kiosks or dedicated tablet applications. The entire process requires zero physical trials and happens in four frictionless steps:
- Instant Garment Capture: A customer scans the barcode of any item on the sales floor using the kiosk, tablet, or app to instantly load its high-fidelity digital version.
- Virtual Twin Profile Creation: Click a quick selfie and input basic body measurements. Our Simulation AI generates a personalized Virtual Twin matching the customer's height, proportions, and body type.
- Size & Fit Visualization: Our Simulation AI-powered Virtual Try-On recreates how a garment fits on the customer's Virtual Twin, accurately visualizing fabric tension and fit across the body to show where it will feel tight, loose, or slim-fit.
- Full-Outfit Mix & Match: Customers can select and curate endless outfits—combining tops, bottoms, and accessories in real time and see the full look.
Once created, the Virtual Twin is securely saved to the customer's profile, enabling instant virtual try-ons across future online purchases or visits to any store location—without repeating the setup.
Built for Enterprise Fashion Retail
Crucially, PointAI's In-Store Virtual Try-On is designed for enterprise deployment—not as a standalone application but as part of a connected AI commerce platform for both online & offline. The solution offers:
- Lightweight SDK: Engine architecture built for rapid integration.
- Quick Deployment: Fast implementation across existing, legacy retail systems.
- Pixel-Based Tracking: Deep customer journey analytics to measure intent and conversion.
- Catalogue-Scale Deployment: Seamless scaling across thousands of SKUs simultaneously.
- Unified Digital Assets: A single digital asset powering both online and in-store experiences.
For retailers, this means higher sales floor throughput during peak hours, reduced garment wear from repeated physical trials, and valuable in-store intent data to optimize merchandising and inventory.
The Future of Fashion retail isn't online versus offline. It's about connecting both through a single, intelligent shopping experience. At PointAI, we're building the AI Fashion infrastructure that makes this possible.
