- E-commerce / RetailIndustry
- E-commerce & retailClient
- Automation & OptimizationCategory
- Fast to valueTime to impact
The challenge
- E-commerce teams receive product information as PDFs from dozens of suppliers, each laid out differently.
- Re-keying it into the product catalogue by hand is slow, error-prone and never keeps up with new ranges.
Pulls product data out of messy supplier PDFs and feeds a clean, structured PIM.
An extraction pipeline for e-commerce teams that takes the stream of supplier PDFs, each in its own format, and uses AI to pull out every product’s details, structured and ready to load into a PIM.
What we built
- 01Ingests supplier PDFs in whatever format each supplier uses.
- 02Extracts every product’s attributes, names, specs, codes, pricing, with AI.
- 03Structures the data to match the PIM’s schema.
- 04Delivers clean, consistent records ready to load into the PIM.
How it works
What it can do
Any-format ingestion
Handles supplier PDFs regardless of their layout.
Attribute extraction
Pulls names, specs, codes and pricing per product.
PIM-ready structuring
Maps extracted data to your catalogue schema.
Bulk processing
Processes large batches of supplier documents at once.
Who stays in the loop
The call the system deliberately does not take, and what the person is given to take it with.
An extraction pipeline for e-commerce teams that takes the stream of supplier PDFs
The catalog manager
Decideswhat is published into the PIM
Seesthe extracted attributes next to the page of the supplier PDF they came from
The payoff
What is different once it is running. No invented numbers: these are the changes the work was built to make.
A pile of supplier PDFs in twenty different layouts becomes clean, structured product records, ready to load, not re-typed.
Product data extracted without manual re-keying.
New ranges go live faster.
Consistent records regardless of supplier format.
Built with
The layers this runs on, from what comes in to what it plugs into.
- Data in
- Document parsing
- PDF.js
- Structured extraction
- Model layer
- LLM
Common questions
The same answers as above, written out.
An extraction pipeline for e-commerce teams that takes the stream of supplier PDFs, each in its own format, and uses AI to pull out every product’s details, structured and ready to load into a PIM.
E-commerce teams receive product information as PDFs from dozens of suppliers, each laid out differently. Re-keying it into the product catalogue by hand is slow, error-prone and never keeps up with new ranges.
Upload a batch of supplier PDFs. AI extracts each product’s attributes. Structured records are exported to the PIM.
The catalog manager decides what is published into the PIM. They see the extracted attributes next to the page of the supplier PDF they came from.
Data in: Document parsing, PDF.js, Structured extraction. Model layer: LLM.
Product data extracted without manual re-keying. New ranges go live faster. Consistent records regardless of supplier format.
Where this fits
The practice it belongs to, how an engagement like it runs, and what we have written about the subject.
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