Fashion product intelligence, as an API
Skip building a computer-vision pipeline. Get enriched product traits and style-aware recommendations from an API, and ship discovery features in days.
What you’re up against
- 01
CV enrichment is a product in itself
Training and maintaining models that reliably extract fit, silhouette, and style traits across categories is a multi-year effort. The API gives you the output without the pipeline.
- 02
Recommendations need product understanding
Behavioral recommenders cold-start badly in fashion. Trait-based matching works from the first SKU and the first user.
- 03
Headless stacks need composable pieces
Feed connectors, webhooks, and REST endpoints slot into Salesforce, BigCommerce, and custom storefronts without dictating your architecture.
Where Veristyle fits
- Start with the Developer Toolkit.
- See the data model behind it in the semantic product data definition.
- Scope enterprise integrations via a technical demo.
Recommended reading
Frequently asked questions
What does the API expose?
Enrichment endpoints (send products, receive 200+ structured traits), recommendation endpoints (shopper context in, ranked products out), plus feed connectors and webhooks for sync.
Which platforms are supported?
Shopify natively via the app; Salesforce Commerce Cloud, BigCommerce, and headless/custom storefronts via the API, SDKs, and feed connectors.
Can I use enrichment without the discovery widget?
Yes. The layers are composable: many teams consume traits via API for their own UX while using Veristyle for GEO data publishing, or vice versa.
See Veristyle on your catalog
See how your products show up in AI search today, and what personalized discovery could do for your numbers.