AI for eCommerce product cataloguing
AI in an eCommerce catalogue works when it starts from real data and verifiable rules.
You don't need thousands of generic descriptions. You need normalised information, missing facets found, tags suggested, duplicates flagged and product pages improved where they genuinely affect SEO and conversion.
You don't need thousands of generic descriptions. You need normalised information, missing facets found, tags suggested, duplicates flagged and product pages improved where they genuinely affect SEO and conversion.
What we automate
- Product tags and attributes.
- Colour, material, use and compatibility facets.
- Title and description normalisation.
- Flagging duplicates and missing data.
- FAQs and selling points drawn from reviews and spec sheets.
Quality control
- Rules applied before publishing.
- Human review on sensitive fields.
- Logs of every suggested change.
- Checks for duplicate or overly generic copy.
- Alignment with SEO and feeds.
Useful outcomes
- A catalogue that's easier to navigate.
- More coherent facets.
- Better site search.
- Clearer product pages.
- Feeds and Ads with cleaner data.
FAQ
Can AI write all the product descriptions?
It can help, but without real data and review it produces text that all reads the same. Better to use it to enrich the pages that matter and fix weak data.
Can it create facets automatically?
Yes, but the facets have to be confirmed by rules and data. A wrong facet makes navigation, SEO and trust worse.
Do I need a custom platform?
Not always, but a custom platform makes it far easier to connect AI, catalogue, logging, backend and approval workflows.