The moat is the garment, not the model
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Models
A planned family of domain-specialized image models, trained and evaluated on the garment-identity graph. We publish benchmarks before we train: each model is gated on measured gaps in frontier systems, not on ambition.
A compact model for garment-identity conditioning that holds one specific physical garment faithfully within a single generated image.
The workhorse, built for multiview generation with a shared garment representation that keeps the same SKU coherent across poses, scenes, and try-on.
The same domain structure at the largest scale our data supports, intended for a higher quality tier and for offline batch generation.
Systems
The annotation layer over every image we process, covering role classification, label detection, garment segmentation, and SKU-level grouping, along with the garment-identity graph it produces.
Self-hosted model infrastructure. Each model is a self-contained, scale-to-zero deployment that can be stood up, evaluated, and retired on its own.
A benchmark for garment identity and multiview consistency, built from the identity graph and calibrated against a human-verified core.