The data layer underneath your store.
A clean, structured, machine-readable view of your catalog. Built and maintained automatically — attributes, policies, embeddings, schema — so every layer above reads the same ground truth.
The base every layer above depends on.
Without this, agents see the title and the price. With it, they see the SKU the way a knowledgeable salesperson would — material, fit, compatibility, policy, ground truth — and they cite you accordingly.
Structured per category, not generic
Auto-detected attribute templates per category — material and fit for apparel, roast and brew method for coffee, capacity and compatibility for hardware. Filled from your product copy, images and reviews, validated, and held to the same shape across SKUs.
Shipping, returns, warranty — as data
Pulled from your terms, shipping zones, return windows and warranty pages, then exposed as structured records every layer above can query. Agents stop hallucinating your policy because there is a canonical answer.
Semantic search, ready to use
Every product and policy embedded at ingestion. Vector search powers the conversational UI, the recommendation engine and the agent surface without you running a vector store.
Validated and published
schema.org Product, Offer, FAQPage, BreadcrumbList — fully populated, validated against the spec, and published to your storefront so traditional search and agentic search both index a canonical version.
An AI-Ready Score per product
Every SKU gets a score from the same data the agents read. The console shows you exactly what's missing per category — and the enrichment pipeline closes the gap automatically where it can.
Common questions.
01What is product data enrichment?
It's turning your existing product copy, images and policies into structured, machine-readable data — per-category attributes, policy records, embeddings and schema.org markup — built and validated automatically, so both shoppers' assistants and AI agents read the same ground truth.
02Do I need to clean up my catalog first?
No. The pipeline extracts attributes from what you already have and shows you exactly what's missing per category via an AI-Ready Score per product. You fix gaps at the source where it matters; the pipeline closes the rest automatically where it can.
03Why does this matter for AI search?
AI assistants cite stores they can read. Structured attributes, validated schema.org markup and canonical policy data are what make your products quotable in ChatGPT, Perplexity and Google's AI results — enrichment is the layer that makes every other AI surface work.
04Does it stay in sync with my store?
Yes. Enrichment runs continuously against your live catalog — add products, change a shipping zone or update a return window and the structured layer updates with it.
Get a free product data quality audit.
Find out which product attributes LLMs are missing on your catalog — and what it's costing you in agentic search visibility. No call required, the report lands in your inbox.