Products are live. The website has filters. But "Voltage", "Chuck Size", and "Motor Type" are greyed out — because the attributes that power them are locked inside unstructured titles and missing from your records.
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Running a catalogue where customers drop off because they can't narrow down to the right product
Watching competitors rank for "18V brushless drill" while your products don't surface because the attributes aren't structured
Unable to build effective product comparison pages because half the spec data is missing or inconsistent
Amazon and trade marketplace listings rejected or downranked for missing required attributes
Look at your product titles. "Makita DHP484Z 18V Li-ion Brushless Cordless Drill Driver." Voltage: 18V. Motor type: brushless. Both attributes are sitting right there — in plain English, in the title — and neither of them is powering your faceted search. The data exists. It just wasn't extracted when the product was created.
The downstream effects are compounding. Customers who can't filter leave — and they don't come back, because they've already found what they needed on a competitor's site. Products that can't be filtered can't be compared. Products that can't be compared don't get bought without a phone call. Every missing attribute is a small friction that multiplies across thousands of sessions.
And for B2B buyers — the trade customer who knows exactly what voltage, chuck size, and certification they need — a catalogue they can't filter by spec is a catalogue they won't use. They'll call your rep, or they'll go somewhere else. The specification filter isn't a nice-to-have for technical buyers. It's the entire search experience.
Three steps. The data is usually already there — it just needs to be pulled out, standardised, and connected to your filters.
AI reads product titles, descriptions, spec sheet PDFs, and manufacturer URLs — extracting every structured attribute it can find. "18V Li-ion Brushless" becomes three separate, filterable attributes in your schema. At catalogue scale, overnight.
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Extracted attributes are only useful if the values are consistent. "18V", "18 Volts", "18v" must all map to the same filter value. AI normalisation enforces your accepted value list across every record — so your Voltage filter shows one clean set of options, not a mess of variants.
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Some attributes — IP rating, chuck size, included accessories — won't be in the title or description. Enrichment Studio fills those gaps using AI, drawing on the product's spec sheet, manufacturer data, and cross-product inference. The filter works because every product has the attribute, not just the ones with good titles.
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Three capabilities in sequence: extract the attributes, standardise the values, fill the gaps.
Pulls structured attributes from titles, descriptions, PDFs, and URLs at catalogue scale.
Explore →Defines which attributes power which filters — and what the accepted values are for each one.
Explore →Standardises all value variants to your filter list. One consistent set of options, not a mess of duplicates.
Explore →Fills attributes that aren't in existing content — so filters work across 95%+ of products, not 30%.
Explore →Filterable catalogues are the output. These are the upstream problems that cause unstructured data in the first place.
If your filters are broken across a large catalogue, manual enrichment is never going to fix it fast enough.
Read more →Unstructured data often starts at the source. Fixing supplier onboarding reduces the enrichment problem downstream.
Read more →Getting products live faster only helps if they're live with complete, filterable attributes — not as incomplete stubs.
Read more →Book a 30-minute demo. We'll run an attribute coverage audit on a sample of your products — and show you exactly how many of your filters are broken, and why.
Product data enrichment that actually gets done — so your product pages convert, your search works, and your team stops living in spreadsheets.
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