Extract Product Data From PDFs, Datasheets & Images (with AI)

Why so much product data is stuck in documents

Ask a supplier for product data and you'll often get a CSV with a name, a price and an EAN — and a link to a PDF catalog or a folder of datasheets for "everything else." The material, the dimensions, the compliance info, the technical specs, the care instructions: all of it lives in documents built for a human to read, not for a system to import. Getting it into your catalog means someone reads each PDF and retypes the fields. Across a range, that's the single most tedious part of onboarding a supplier.

AI changes the economics of that step — but only if it does more than read characters.

OCR reads; AI understands

It's worth separating the two, because "OCR" gets used loosely:

• OCR converts a scanned page or image into raw text. Useful, but you still get an undifferentiated blob — it doesn't know that "1,2 kg" is a weight or that "Art.-Nr. 4711" is your SKU.
• AI extraction reads that text (or the image directly) and interprets it into structured fields: weight, material, dimensions, EAN — in the right unit and format, mapped to your attributes.

For product data you need the second. A plain OCR tool leaves you with text to sort by hand; the win only comes when the output is structured attributes you can publish.

What AI can pull out of a datasheet or catalog page

• Attributes & specs: material, dimensions, weight, capacity, technical values — including from tables and multi-column layouts.
• Identifiers: EAN/GTIN, article numbers, model codes.
• Descriptive text: feature lists and care instructions, ready to rework into channel copy.
• From images, not just text: packaging photos and labels, where the data is printed on the product itself.

Building it yourself — and the ceiling you hit

For one supplier with one consistent datasheet template, a script is viable: loop over the PDFs, send each to a vision-capable LLM API with a fixed extraction prompt, write the JSON to a spreadsheet. That's a good weekend project and genuinely useful.

The ceiling arrives with reality: every supplier's layout is different, so one prompt doesn't generalize; there's no mapping from the extracted fields to your attribute schema; units and formats aren't normalized (kg vs g, comma vs dot); and there's no review step, so you either trust the output or re-check everything. Maintaining a prompt-per-supplier zoo becomes the job. See the broader trade-offs in AI automation for bulk product data mapping.

How Productbay does it

In Productbay, documents are just another source into the same enrichment flow. The AI reads uploaded PDFs, datasheets and images, extracts the fields, maps them to your attributes and normalizes units — then routes every value into the AI review queue, marked so you can tell document-extracted data from manually entered data. Where the document is missing a spec, the AI can research it on whitelisted manufacturer sources rather than leaving a gap. Approve in bulk, and the structured result flows on to enrichment, categorization and channel export — no separate OCR tool, no copy-paste.

This table was compiled from publicly available information. We aimed to bring transparency to the market — details may change over time. When in doubt: check both providers yourself and decide based on your own evaluation.

Extraction is the front door; see the whole journey in AI for product data maintenance and how missing values get filled in AI web research for missing product data.

Frequently Asked Questions