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PointAI to Showcase Patented Simulation AI Technology for Fashion Retail at London Morning Social

PointAI to Showcase Patented Simulation AI Technology for Fashion Retail at London Morning Social

VMPL
London [UK], October 21: PointAI, in association with HMJ Partners, will host an exclusive morning social on Friday, 23 October 2026, at Pavilion Club, Knightsbridge, London. Titled “Try It On. Style It Up. Watch It Sell.”, the event will bring together industry professionals to explore the role of artificial intelligence in transforming fashion retail, with a focus on virtual try-ons, personalised shopping experiences and technology-led sales conversion.
As fashion retailers increasingly adopt AI-powered solutions, virtual try-on technology presents both opportunities and challenges. Generative AI systems can struggle to accurately account for individual body characteristics, including bust, waist and hip measurements, torso proportions and leg length. Fabric texture, colour and garment design details may also vary across generated outputs, affecting consistency and the reliability of the virtual shopping experience. Rendering times and processing costs are additional considerations for retailers looking to deploy these solutions at scale. According to PointAI, commercial try-on APIs can cost approximately $0.15 per image and take between 30 and 60 seconds to render, depending on the provider and implementation.
PointAI is addressing these challenges through its patented Simulation AI technology, which models garments as physical objects governed by the underlying physics of fabrics. The technology combines physics-based garment simulation with deep-learning models trained across more than 200,000 body-type variations. According to the company, this approach enables the system to simulate how garments fit and drape across different body types and render realistic fabric on a human body in under one second, at approximately one-hundredth the cost of generative AI alternatives. By focusing on the physical behaviour of garments rather than generating visual outputs alone, PointAI aims to offer fashion retailers a faster, more cost-effective and consistent approach to virtual try-ons.
Commenting on the technology and the upcoming event, Nitin Vats, Founder & CEO, Point AI said, “The future of AI-led fashion retail will depend on more than visually appealing experiences. Retailers need solutions that can deliver speed, accuracy and cost efficiency while maintaining consistency in how products are represented. Our Simulation AI technology brings the principles of physics into virtual try-ons, enabling a more realistic representation of garment fit and fabric behaviour across different body types. We believe this approach can help retailers improve the online shopping experience, build consumer confidence and make virtual try-ons more commercially viable at scale.”
The morning social will provide a platform to discuss how advances in AI and simulation technology can reshape product discovery, styling and online purchasing. As consumer expectations around personalisation and convenience continue to evolve, solutions that bridge the gap between digital product representation and the real-world experience of wearing a garment are becoming increasingly relevant to fashion brands and retailers.
Scheduled from 8:30 AM to 12:00 PM, the event will bring together industry professionals for discussions, demonstrations and networking over breakfast. The gathering will take place at Pavilion Club, Knightsbridge, 64 Knightsbridge, London SW1X 7JF, and aims to encourage conversations around the next phase of technology-led innovation in fashion retail.
Through this initiative, PointAI, in association with HMJ Partners, seeks to highlight the potential of physics-based Simulation AI to deliver more realistic, efficient and scalable virtual shopping experiences, helping retailers explore new ways to connect product discovery with purchase decisions.
(ADVERTORIAL DISCLAIMER: The above press release has been provided by VMPL. ANI will not be responsible in any way for the content of the same.)

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