beyond data
    Document & Invoice Automation

    Read, extract, and post the document. Leave the humans to handle the exceptions.

    Document & Invoice Automation before and after

    Document & Invoice Automation uses language and vision models to read supplier invoices, claims forms, delivery dockets, and contracts, extract the fields that matter, and post them straight into your ERP or claims system. A human only sees the exceptions. Built for AP, claims, and contracts teams who are drowning in PDFs.

    The Problem

    What usually breaks

    You are paying an AP clerk $75,000 a year to open PDFs. Every week. That clerk spends most of the day retyping the same seven fields from a supplier invoice into MYOB, Xero, NetSuite, or Pronto. On the good days they close 120 invoices. On the bad days a supplier sends a scan of a scan and the whole thing slows down.

    Claims assessors do the same work in a different uniform. Damage report arrives as a PDF. They read it, type the extracts into Guidewire or Duck Creek, and move on. Contracts administrators do it with subbie agreements. Council officers do it with development applications. The cost is hidden because nobody books it as a line item, but it is the single biggest drag on the back office of every insurer, builder, council, and logistics operator we meet.

    The work is boring, error-prone, and hard to scale. When volume spikes you either pay overtime, let the backlog grow, or miss the early-payment discount. None of those options are good.

    In Your Business

    How this lands inside your operation

    Inside your business it looks like this. A supplier emails an invoice to ap@yourcompany. The system reads it, pulls out supplier name, ABN, invoice number, date, line items, GST, and totals, matches it to the PO in NetSuite, and posts it. The AP clerk sees a queue of three exceptions for the day: one GST mismatch, one unknown supplier, one line item that does not reconcile. They fix those and go home on time.

    Claims runs the same pattern. PDF damage report hits the shared inbox. The system extracts claimant details, policy number, incident date, damage description, and estimated costs. It lands in Guidewire with a confidence score. The assessor opens only the ones flagged for review.

    This is for you if…

    • A CFO or head of finance watching AP headcount grow faster than revenue
    • A claims manager whose assessors type more than they assess
    • A contracts administrator drowning in subbie paperwork from 80 active sites
    • A council CFO whose staff process thousands of applications a year by hand
    • Any business where the ERP is MYOB, Xero Business, NetSuite, or Pronto, or the claims system is Guidewire, Duck Creek, or homegrown

    This probably isn't the right fit if…

    • Organisations processing fewer than a few hundred documents a month. The ROI is not there.
    • Teams that need every document approved by a human regardless of confidence. You will not get the efficiency gain.
    • Businesses with no system of record to post into. Fix that first.
    How It Works

    The engagement, step by step

    1. 1

      We catalogue your document types, sample 200+ real examples, and agree which fields matter for each one.

    2. 2

      We stand up a processing pipeline that reads email inboxes, shared drives, or scanner folders where documents arrive today.

    3. 3

      Vision and language models extract the structured fields, with a confidence score attached to each value.

    4. 4

      High-confidence records post straight into your ERP or claims system through the native API or flat-file import.

    5. 5

      Low-confidence records land in a review queue with the original document and the proposed extraction side by side.

    6. 6

      Every human correction feeds back into the model, so the confidence threshold covers more of the volume each month.

    7. 7

      An audit trail records which document was processed, what was extracted, what the human changed, and who approved it.

    Before and after

    Before and after

    Before and after
    Document scope

    Documents we handle well vs still need a human

    Being honest about where the AI works and where it does not is part of the sell. This table goes on the page.

    Document typeHow we handle itConfidence
    Supplier invoices (PDF, scanned, emailed)Extract, match to PO, post to ERPStrong
    Delivery docketsExtract line items, reconcile against receipted quantityStrong
    Expense receiptsExtract vendor, date, amount, GST, categoryStrong
    Simple contracts (standard templates)Extract counterparty, term, value, renewal dateStrong
    Insurance claim forms (digital)Extract claimant, policy, incident, damage, estimateStrong
    TimesheetsExtract worker, hours, cost code, approverStrong
    PO acknowledgementsExtract PO reference, confirmed price, confirmed dateStrong
    Complex multi-party contracts with legal nuanceHuman reviews first passNeeds a human
    Handwritten forms in poor-quality scansHuman reviews first passNeeds a human
    Documents in a language we have not trained forHuman reviews first passNeeds a human
    Forms with non-standard layouts we have not seenHuman after first 20-50 samples, then AI takes overTrained in

    If you send us 20 samples of a document type we have never seen, we will tell you honestly whether it sits in column one, column two, or column three before you commit to the build.

    Outcomes

    What changes once this is in

    AP and claims teams get their week back

    Manual keying drops by most of the volume inside 90 days. AP and claims staff get their week back for exception handling, supplier management, and the judgement work they were hired for.

    Payment cycles shortened

    Invoices post the same day they arrive. Early-payment discounts that used to slip through the cracks get captured. Late-payment penalties disappear from the ledger.

    Error rates below the human baseline

    Within 90 days, extraction error rates fall below what a fatigued clerk produces at 4pm on a Friday. Every error is traceable to a specific document and a specific field, so fixing them is quick.

    A defensible audit trail

    Every decision logged. Every correction logged. Every approval logged. When your auditor asks why that invoice was posted at that GST rate, the answer is one click away.

    Capabilities

    The capabilities behind this

    AI Development

    AI agents, chat interfaces, and multi-agent systems, built end to end by the engineer who scoped them. Deployed, monitored, and handed over.

    Learn more

    Advanced Analytics & ML

    Predictive models, recommendation systems, forecasting, and MLOps. Every model is measured against the decision it was built to serve.

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    Insight-to-Action Systems

    The architecture that carries a number from your data to the person who acts on it, built so the loop closes instead of stopping at a dashboard.

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    Common Questions

    Frequently asked

    Let's talk about Document & Invoice Automation.

    30-minute call, no slides, no obligation. We'll tell you plainly whether this is the right fit for what you're trying to do.

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