Social platform launches AI try-on from 1M+ assets

Overview

AI virtual try-on doesn't ship without the right training data — and the right data isn't on a stock-photo site.

To launch an AI-powered virtual try-on at consumer scale, a Global Social Platform needed authentic human-garment interaction data at volume: real bodies, real garments, real fit dynamics across diverse populations.

Firstsource built and ran the data program ” 1,000,000+ multimodal assets captured from 10,000+ participants across 30+ cities and four countries, feeding computer-vision training, SFT, and production-grade validation.

This was Intelligence that Operates: a multimodal capture pipeline running on launch timelines, not research timelines.

Challenges

• High-fidelity visual training signal at scale doesn't exist off the shelf. Virtual try-on needs human-garment interaction data ” fit, drape, motion ” that scraped images can't provide and synthetic data can't match for body diversity.
• Body diversity is a participant-recruitment problem, not a sampling problem. Training a try-on model that works across sizes, body types, and demographics requires participants intentionally recruited for diversity, not collected opportunistically.
• Launch acceleration demands one program across capture, annotation, and validation. A capture → CV training → SFT → validation pipeline broken across multiple vendors slows the model and misaligns its outputs. The platform needed one operator across the full data lifecycle.

How We Made It Happen

We ran capture, annotation, and validation as one program on a launch cadence.

• Multimodal capture across 10,000+ participants in 30+ cities. Images and motion video collected for human-garment interaction, fit, and body diversity across four countries.
• Multimodal Evaluation feeding CV, SFT, and production validation. Captured signals routed into computer-vision model training, instruction-tuning, and production-grade validation as one connected workflow.
• One program across the data lifecycle. Recruitment, capture, annotation, and validation orchestrated end-to-end rather than handed off across vendors.

Conclusion

AI virtual try-on only ships when the data behind it sees real bodies in real motion. Firstsource ran that program at consumer-launch scale, turning multimodal capture into Intelligence that Operates.