What is AI document extraction and analysis?
Document extraction uses AI to read unstructured documents — a PDF invoice, a scanned form, a signed contract — and pull out the specific data fields your system needs. Rather than a human manually re-keying "Supplier: Boral, Invoice Number: INV-0042, Amount: $4,320.00, GST: $432.00", the system reads the document and populates those fields automatically.
This is made possible by combining two technologies: OCR (optical character recognition) to convert images and scanned PDFs into machine-readable text, and a large language model to understand the structure and extract the right values even when document formats vary between suppliers or counterparties.
The result is automation of data entry workflows that are currently expensive, slow, and error-prone — particularly in accounts payable, insurance claims processing, contract management, and compliance.
When does your app need it?
- Your business or your customers receive supplier invoices in varying formats and manually enter data into an accounting or ERP system
- You process insurance claims that include photos, repair quotes, or medical documents that need to be read and categorised
- You manage contracts and need to extract key terms — parties, dates, renewal clauses, obligations — without reading every document in full
- You have a compliance or onboarding workflow that requires reading identity documents, licences, or certificates
- Your operations team processes inbound paperwork (delivery dockets, timesheets, council approvals) and you want to reduce manual data entry
- You want to build an accounts payable automation feature into a platform serving Australian SMEs
How much does it cost?
Adding AI document extraction and analysis typically adds 11–21 hours of development — roughly $2,000–$5,000 AUD.
At the simpler end, this is a pipeline that handles one document type (e.g. supplier invoices in common formats) with a defined set of fields to extract. At the more complex end, it handles multiple document types, validates extracted data against business rules, routes low-confidence extractions to a human review queue, and integrates with downstream systems to act on the extracted data.
How it's typically built
The pipeline starts with document ingestion: the user uploads a PDF or image, or it arrives via email. If the document is a native PDF with selectable text, the text is extracted directly. If it is a scanned image or a photographed document, it passes through an OCR service — AWS Textract, Google Document AI, or Azure Form Recognizer are the most capable options for Australian applications and each have pre-trained models for invoices and forms.
The extracted text (along with layout information in more sophisticated setups) is then sent to an LLM — GPT-4 Vision, Claude, or a similar model — with a prompt that instructs it to extract specific fields and return them as structured JSON. A validation layer checks the output for common errors (missing GST amounts, invalid ABN formats, totals that don't add up) before writing to the database. Extractions that fall below a confidence threshold are flagged for human review. This human-in-the-loop step is important — no extraction pipeline is 100% accurate, and an unreviewed error in an accounts payable system causes real financial harm.
Questions to ask your developer
- What document types and fields need to be extracted? Each distinct document type (invoice, contract, form) is a discrete prompt engineering and testing effort.
- What format will documents arrive in? Native PDFs, scanned images, photographed documents, and mixed formats each have different OCR requirements.
- How will errors and low-confidence extractions be handled? A human review queue is strongly recommended for production use.
- Where will documents be stored, and for how long? Document storage has cost, privacy, and compliance implications — especially for contracts and financial records.
- Does extracted data need to flow into an accounting or ERP system? Integration with Xero, MYOB, or similar adds scope beyond the extraction pipeline itself.
See also: AI text generation · Inbound email processing · App cost calculator