Case Tested
Turning Supplier Documents Into Structured Product Data
Supplier invoices, price lists and spec sheets arrived in every format and were re-keyed by hand. We built rule-based extraction and matching that turns them into structured product data, flags every ambiguous match for a person, and never fills a value it cannot source. In a matching trial, 44 of 45 models matched against about 6,500 records.
Status: Tested on real data — results reviewed and accepted by the business; not yet a production pipeline.
The problem
Supplier invoices, promotional price lists and spec sheets arrived as PDFs and spreadsheets, each supplier in its own format. The same model could appear with a prefix, extra spaces or a variant suffix. Staff re-keyed invoice lines, looked up specifications one by one, and cross-checked everything against internal records.
What we built
- Invoice PDF → structured sheet — one fixed format per invoice: model, net unit cost, quantity, supplier, invoice number, status and notes, with a final total row that reconciles cost × quantity. Tax, freight and backorder-only lines are left out; credits keep a negative quantity.
- Supplier and invoice identification — supplier name variants mapped to one canonical name; invoice numbers extracted.
- Model matching — exact match first, then normalized (prefixes, spacing, case), then a controlled fuzzy fallback. Anything that matches more than one record is flagged, never picked silently.
- Specification enrichment — dimensions, unit conversion and technical specifications filled from official sources in a fixed priority order, with the source recorded. If no reliable source exists, the field stays blank and is listed as an exception.
- Price and product sheets — filterable product lists from supplier PDFs, price write-back by model, and combined product, price and stock sheets with gaps marked.
- Image mapping — official product images mapped to SKUs, up to three per SKU, on a tested sample.
Results Tested
- Model matching trial: 44 of 45 models matched against ~6,500 records; 10 ambiguous matches sent to a person; 1 reported as unmatched
- Invoice rules applied to real supplier invoices, with the total reconciled on every sheet
- Product specification sheets produced with dimensions, conversions, source notes and an explicit exception list
- Image mapping tested on a 10-SKU sample; large-scale runs are still being made reliable
These workflows have run on real business data, and the business reviewed and accepted the results. They are not yet a production pipeline: every output is checked by a person, and we do not publish accuracy rates we have not measured.
Where people stay in control
- Any match with more than one candidate
- Whether two variant model numbers are the same product
- Whether a source is reliable enough to fill a missing value
- The final check of every sheet before it is used
Key principle Leave a field blank rather than guess it. A visible gap costs less than a silent error.
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