At 4 minutes per SKU — a generous estimate — enriching a 50,000-product catalogue manually takes two people an entire year. AI does it overnight. The maths makes the decision for you.
.png)
Managing a backlog of enrichment work that grows faster than the team can process it
Repeatedly asked to hire more data staff for work that feels like it should be automatable
Aware that thousands of products are live but under-described, under-attributed, and invisible to search
Approving headcount requests for data enrichment and wondering if there's a better way
The enrichment backlog doesn't stay still. New products come in from suppliers every week. The team processes last month's backlog while this month's grows behind it. It's a treadmill — and the only conventional solution is to hire another person to run faster on it. Most teams have tried this. It doesn't work at scale.
The quality problem compounds the volume problem. Manual enrichment is inconsistent — one person's "Brushless" is another person's "brushless motor" is another person's "BL". Units vary. Values vary. Descriptions vary. You end up with a large catalogue and a structural quality problem baked into every record. Scale without consistency makes search and filtering worse, not better.
And the opportunity cost is invisible until you calculate it. Products with complete, accurate attributes convert at higher rates, rank better in search, and generate fewer returns from customers who received the wrong item. Every under-enriched product is leaving money on the table — you just can't see exactly how much.
The goal isn't to replace your team's judgement — it's to stop them spending 90% of their time on work that doesn't require it.
Enrichment Studio reads every attribute you have and uses AI to infer the ones you don't — from the product's own content first, then external sources. Confidence scores flag anything uncertain for human review. 50,000 products enriched while your team is asleep.
.png)
Most enrichment work is finding data that already exists somewhere — in a product title, a PDF spec sheet, a manufacturer URL. AI reads all of it and extracts structured attributes automatically. The data was always there. The manual work was finding and copying it.
.png)
Manual enrichment creates inconsistency. AI enrichment doesn't. Every value is normalised to your schema during enrichment — "brushless", "Brushless", "BL Motor" all become "Brushless". The same attribute, the same value, across 50,000 products.
.png)
Three platform capabilities working in sequence — extract, enrich, normalise — applied automatically across every product.
AI fills attribute gaps at any volume. Confidence scoring routes uncertain values for human review.
Explore →Extracts structured attributes from titles, descriptions, PDFs, and URLs - at catalogue scale.
Explore →Consistent values enforced across every enriched product. No more "brushless" vs "Brushless".
Explore →Completeness scoring tells you what the AI enriched, what is flagged, and what'
s ready to publish.
Once enrichment is handled, these are the challenges teams focus on next.
Fast enrichment is step two. Fast onboarding is step one. Fix the full pipeline.
Read more →Enrichment gives you the attributes. Structured schema makes them searchable and filterable.
Read more →Better supplier data at the source means less enrichment needed on your end.
Read more →Book a 30-minute demo. We'll run a live enrichment pass on a sample of your products — and show you the completeness score before and after, in real time.
Product data enrichment that actually gets done — so your product pages convert, your search works, and your team stops living in spreadsheets.
.png)
