Invoice Document Intelligence for an Australian Manufacturer

Challenge

A manufacturer received supplier invoices across inconsistent layouts and formats, and its accounts-payable team keyed every field by hand. The manual workload created processing backlogs, transcription errors, and SKU mismatches that delayed payment approvals. Slow, error-prone cycles strained supplier relationships and obscured spend visibility for finance.

Solution

The approach: a document intelligence pipeline that ingests supplier invoices, extracts fields with OCR and NLP, and validates them against SKU and pricing master data. Confident, clean invoices flow straight through to approval, while ambiguous ones are flagged for a human reviewer, keeping a person in control of edge cases. Reviewer corrections feed back into the models, so extraction accuracy and straight-through rates improve as the system processes more documents.

Approach

1. 1

   We mapped the existing invoice intake and approval workflow, defined the target fields, and set accuracy and exception thresholds with the finance team.

2. 2

   We built an OCR layer to digitise scanned and PDF invoices, then applied NLP to extract line items, totals, and supplier details.

3. 3

   We added validation that matches extracted SKUs and amounts against master data and routes only low-confidence cases to a human reviewer.

4. 4

   We deployed the pipeline behind a monitored approval flow, then tuned extraction models against reviewer corrections to raise accuracy over time.