Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Deploy realistic virtual try-on experiences to reduce returns and increase online conversion.
Reduce apparel return rates by up to 40% by allowing customers to visualize products on themselves before buying. Our solutions integrate advanced computer vision and generative AI models like Stable Diffusion to create photorealistic simulations for apparel, eyewear, and cosmetics.
REST or GraphQL APIs.We engineer systems that process customer-uploaded images or use live camera feeds to deliver sub-2-second latency try-ons. This directly addresses the "high cost of online uncertainty," transforming browsing into confident purchasing.
Explore our broader capabilities in Retail and E-Commerce Hyper-Personalization or see how we build foundational AI with Domain-Specific Language Model (DSLM) Training.
Our AI-Powered Virtual Try-On solutions are engineered to directly impact your core retail metrics. We focus on delivering concrete, measurable improvements in conversion, returns, and customer engagement.
Deploy computer vision models that accurately predict fit and drape, giving customers confidence in their size selection. This directly addresses the leading cause of online returns.
Integrate realistic virtual try-on directly into the product page to reduce purchase hesitation. Our solutions drive higher engagement, leading to more add-to-carts and completed purchases.
Provide an interactive, novel experience that keeps users on your site longer. Increased session duration correlates strongly with higher purchase intent and brand loyalty.
Leverage our pre-built pipelines for model fine-tuning, 3D asset processing, and real-time inference. We deploy production-ready virtual try-on experiences in weeks, not months.
Incorporate Stable Diffusion and ControlNet for photorealistic garment visualization on diverse body types. This eliminates the need for expensive model photoshoots for every SKU.
All processing is designed with privacy-by-design. User images are processed ephemerally with no persistent storage, ensuring compliance with global data protection regulations like GDPR.
Our structured, milestone-driven approach ensures predictable delivery, continuous value delivery, and seamless integration with your existing e-commerce stack.
| Phase | Key Deliverables | Timeline | Outcome |
|---|---|---|---|
Discovery & Architecture | Technical spec, data pipeline design, model selection framework | 2-3 weeks | Clear roadmap, defined KPIs, and infrastructure plan |
Core CV Model Development | Garment segmentation, pose estimation, and initial warping pipeline | 4-6 weeks | Functional try-on prototype for key apparel categories |
Generative Refinement & Realism | Integration of diffusion models (e.g., Stable Diffusion) for photorealistic texture & lighting | 3-4 weeks | High-fidelity, believable try-on visuals ready for user testing |
Platform Integration & API Development | RESTful APIs, SDKs for web/mobile, and CMS/ERP connectors | 3-5 weeks | Seamless integration into your live e-commerce environment |
Pilot Deployment & Optimization | A/B testing framework, performance monitoring, latency optimization | 2-3 weeks | Validated performance metrics (e.g., <2 sec latency, +15% conversion lift) |
Enterprise Scaling & Analytics | Multi-region deployment, analytics dashboard, and SLM integration for personalized recommendations | 4-6 weeks | Scalable, secure platform with full analytics and ROI tracking |
We deliver production-ready virtual try-on solutions in weeks, not months, using a battle-tested methodology that prioritizes accuracy, scalability, and seamless integration.
We build on state-of-the-art models like Stable Diffusion and ControlNet for high-fidelity garment warping and realistic skin-texture rendering. Our pipelines ensure sub-second latency for real-time user interaction.
Beyond simple overlays, we integrate physics-based simulation for fabric drape, stretch, and shadow casting. This creates a believable try-on experience that dramatically reduces return rates.
Deploy on auto-scaling GPU clusters (AWS G5, Azure NCas) with intelligent load balancing. Our architecture supports millions of concurrent sessions with a 99.9% uptime SLA.
Seamless API-first integration with major e-commerce platforms (Shopify Plus, Adobe Commerce, custom ERPs) and Product Information Management (PIM) systems. We handle the complex data synchronization.
User images are processed ephemerally with no persistent storage. We implement on-device preprocessing and comply with GDPR/CCPA by design, building crucial consumer trust.
Post-launch, we instrument detailed analytics on engagement, conversion lift, and fit accuracy. We run continuous model fine-tuning and A/B tests to optimize for your specific KPIs. Learn more about our approach to Retail and E-Commerce Hyper-Personalization.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Common questions from CTOs and product leaders evaluating partners for AI-powered virtual try-on development.
We deliver production-ready virtual try-on solutions in 4-6 weeks for standard apparel integrations. This includes model integration, API development, and front-end SDK delivery. Complex multi-category deployments (e.g., eyewear, cosmetics, jewelry) typically require 8-10 weeks. Our rapid deployment is enabled by a modular architecture and pre-built integrations with models like Stable Diffusion and specialized computer vision libraries.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
