Supplier quote documents
Classified, extracted and linked back to source evidence for reviewer control.
SKU data extraction
AI SKU data extraction and supplier SKU matching
DocBeaver helps technical distributors extract, normalize and compare SKU data from supplier files, quotes, datasheets and customer RFQs.
The workflow is designed for teams that need cleaner part numbers, units, price breaks, substitutions and product attributes before records reach ERP, PIM, ecommerce or quote systems.
35-70%
Target reduction in product setup and SKU cleanup work
Document inputs
Real documents this workflow is built around
These are the source files DocBeaver expects to map during an audit and prototype. The implementation can start with a narrow subset, then expand as extraction quality and review rules are proven.
Product datasheets
Classified, extracted and linked back to source evidence for reviewer control.
Catalogues and price lists
Classified, extracted and linked back to source evidence for reviewer control.
Customer RFQs
Classified, extracted and linked back to source evidence for reviewer control.
Purchase orders and delivery notes
Classified, extracted and linked back to source evidence for reviewer control.
Compliance and safety documents
Classified, extracted and linked back to source evidence for reviewer control.
Manual bottlenecks
Why this workflow is a strong automation candidate
Step 1
Supplier SKUs, MPNs and customer part references use inconsistent naming.
Capture supplier files, datasheets, RFQs, quotes and product master exports.
Step 2
Units, pack sizes, dimensions and attributes are normalized manually.
Extract SKUs, MPNs, product names, attributes, prices, units and substitutions.
Step 3
Substitutions and alternative products are hard to compare across supplier files.
Normalize casing, units, pack sizes, symbols, dimensions and attribute names.
Step 4
Product records need source evidence before system updates can be trusted.
Match supplier SKUs to existing products, alternatives and prior quote records.
Extraction and checks
Fields extracted and validation checks performed
The automation should produce reviewable data, not a black-box answer. Every important field or exception needs a source link, confidence signal and review route.
| Extracted fields | Validation checks |
|---|---|
| Supplier SKU, MPN, customer part number and internal SKU | Duplicate SKU and near-match detection |
| Product name, description, family, category and attribute set | Supplier SKU matched to internal SKU |
| Unit, pack size, dimensions, rating, material and compatibility | Missing attributes and incompatible units |
| Price, discount, MOQ, quantity break, currency and lead time | Price break, MOQ and currency consistency |
| Substitution, accessory, compliance and source-document references | Substitution and accessory relationship review |
Workflow outputs
What the implementation should produce
DocBeaver normally starts with a controlled workflow output: summaries, exception queues, review files, dashboards or proposed system updates. Direct writes into operating systems should be added only after review rules are proven.
- SKU extraction table
- Product match queue
- ERP or PIM update proposal
- Duplicate and uncertainty report
- Quote or RFQ matching support file
FAQ
Common questions
Can SKU extraction match supplier SKUs to internal SKUs?
Yes. Matching can combine exact identifiers, normalized part numbers, descriptions, attributes and prior records, with uncertain matches routed to review.
Can it detect duplicate or conflicting product records?
Yes. Duplicate detection and conflict checks are common parts of the review workflow before system updates are applied.
Assess this workflow using your real documents
Start with a focused audit of document types, source systems, manual checks, exception rules and review requirements.