Partner infrastructure for the next interface of fashion commerce.

Most PDPs still show someone else's body. Customers interpolate fit from flat fabric shots and distant models; when the garment arrives, reality diverges—and return rates stay structurally high.

Virtual try-on as a service is how you anchor the product to the shopper. Velvet delivers body-aware visualization you can embed in product pages, paid and owned campaigns, or native apps: realistic drape and proportion without a reshoot for every SKU, scaled with the same render discipline we use in-market.

Outcomes we design for: fewer unforced returns, higher qualified add-to-cart, and creative that finally shows the garment as worn—not only as hung.

Discovery is shifting to LLM-driven assistants and embedded shopping copilots. Those systems do not browse like humans; they ingest structure. Sparse titles, opaque variants, and inconsistent attributes leave models to hallucinate gaps—or skip your assortment entirely when a competitor's catalog is clearer.

Velvet enriches catalogs into an agent-ready fashion layer: normalized attributes, styling and occasion semantics, pairing and substitutability signals, and explicit links between marketing language and machine-checkable facts. The goal is simple: when a shopper asks an assistant for "something similar but warmer," your SKUs can be compared, explained, and surfaced with integrity.

If virtual try-on answers "how it looks on me," catalog enrichment answers "what it is, how it relates, and why it belongs in the set." Together they are the spine of conversational commerce.

The Model Context Protocol is quickly becoming the wiring layer between LLMs and real-world tools. Velvet MCP will expose curated endpoints for style, fit, and catalog semantics—so the assistants your organization and your customers deploy can call Velvet the same way they call other trusted data sources.

We are building toward a world where one graph—avatar, taste, product understanding—travels across surfaces. MCP is how that graph meets the proliferation of agents outside our own apps.

Public availability is slated after private pilots. If you want to explore early access or integration patterns, start a conversation—we'll share timelines and guardrails as they firm up.