AI replaces manual invoice handling
A large Australian organisation was processing thousands of supplier invoices manually each month, requiring a small team to review invoices, capture key billing information and validate payment claims.
Skillfield developed an AI-powered extraction solution that combines computer vision with an automated batch-processing pipeline. The solution now supports invoice assurance at scale, dramatically reducing processing effort while improving traceability, discrepancy detection and confidence in financial data.
The Problem
The organisation’s finance team needed to validate and process around 5,000 supplier invoices every month to complete billing settlements. A small team manually read each invoice, located key information such as supplier details, ABNs and totals, then transcribed the details into internal systems.
At full volume, the process consumed around 160 hours each month. Supplier invoice formats also varied considerably, with inconsistent terminology and overlapping identifiers. This made the work slow, repetitive and difficult to scale, while increasing the risk of transcription errors and inconsistent validation outcomes.
The Solution
Skillfield built an AI-powered document extraction solution using a computer vision model designed to read and interpret document imagery rather than relying on a general-purpose language model. Invoices are uploaded to a folder, where an automated pipeline collects them on a schedule, processes them in batches, and writes the extracted results to a table that the finance team can query directly.
To manage inconsistent invoice formats, Skillfield engineered the solution to recognise terminology variations and distinguish supplier details from biller details. Each extracted figure receives a confidence score, with uncertain results automatically flagged for manual review. Batch processing was deliberately chosen to keep ongoing operating costs low while supporting high invoice volumes.
The Outcome
The solution is in active use and has transformed invoice assurance. During production testing, it achieved 100% accuracy, giving the organisation confidence in the reliability of the extracted data.
The organisation also reports an estimated $2M in annual cost avoidance from identifying incorrect claims and discrepancies, plus around $500k in annual support cost savings.
A process that previously required about 160 hours per month now takes about one hour, with audit and validation efforts expected to fall by 60-70%. The operating costs remain low at only a few dollars per day. The business has gained stronger traceability, clearer visibility into discrepancies, and greater confidence in its financial data. Teams can now focus on flagged exceptions rather than manually reviewing every invoice, while the solution scales with growing volumes without a corresponding increase in review overhead.
