The team developed an AI-powered virtual try-on pipeline optimized for both realism and production readiness.

Key solution components include:

• Enhanced model fine-tuning to improve output quality across a wide range of body types
• Pose estimation and segmentation to better align garments with the user’s body structure
• Diffusion-based generation for more natural visual synthesis
• Upscaling and refinement to improve image sharpness and presentation quality
• Sizing and realism optimization to simulate body proportions and fabric behavior more accurately
• Serverless deployment architecture to support scalable retail usage

AI Stack

• Diffusion
• Pose Estimation
• Upscale
• IP Adapter
• Segmentation

Ops Stack

• Runpod Serverless

Key Impact:

• Improved online purchase confidence through realistic virtual try-on
• Reduced visual generation errors for more reliable customer experiences
• Increased conversion performance for fashion retail partners
• Enabled scalable deployment for high-volume usage scenarios
• Built a strong foundation for future AI-powered personalization in eCommerce

Output & Results:

• 4 weeks to deliver Proof of Concept (PoC)
• 9 weeks to develop and deploy the AI solution from scratch
• 500 fashion partnerships supported
• 1% hallucination error rate
• 35% boost in conversion rates

These results demonstrate the solution’s ability to move beyond experimental virtual try-on and operate as a commercially viable retail enhancement tool.