AI enrichment for product data

The enrichment problem

Most product data arrives incomplete. Supplier files bring a name, a price and a handful of specs — but your channels need structured attributes, a good description, the right category and consistent values. Filling those gaps by hand is the slowest part of getting products live, and it's where quality quietly erodes.

What Accista's AI enrichment does

• Draft product descriptions — in your tone of voice, based on the structured data and supplier text you already have.
• Extract attributes — pull specifications out of unstructured supplier text and documents into structured fields.
• Normalise values — align units, formats and terminology so "1.5 m", "150cm" and "1,5 meter" become one consistent value.
• Suggest categories — place products in your category tree based on their data.
• Flag gaps and inconsistencies — surface incomplete or contradictory product data before it reaches a channel.

Human review, by design

AI-generated content is a draft, not a decision. In Accista, every AI suggestion goes through the same review and approval workflow as a human edit. Your team sees what changed, approves what's right and corrects what isn't — and the AI never publishes anything on its own.

This matters for commerce teams: your product content carries your brand and your legal responsibility. Accista is designed to make enrichment faster, not to take it out of your hands.

Enrichment at assortment scale

The point of AI enrichment isn't polishing one product — it's working through two hundred new supplier products in an afternoon instead of two weeks. Accista is built around batch workflows: enrich a whole import, review the results in a focused queue and publish when the batch is approved.

AI enrichment is part of the Accista early access program. Request access to try it on your own assortment.