Turn documents into data your systems can actually use
Invoices, purchase orders, contracts, forms, delivery notes and scans, read and turned into structured records that land in the system of record. With a confidence score and a review step, because the last few percent is where the cost sits.
- 300+ migrations, zero data loss
- Core Web Vitals in the green
- 24/7 emergency cover
-
Classify before extracting
Knowing what a document is decides which fields to look for. Skipping this step is why generic extraction produces a field called "total" holding a date.
-
Extract against a schema
Every field declared with a type and a validation rule, so an invoice date that is not a date fails loudly rather than flowing into your ledger.
-
Score confidence, then route
High confidence goes straight through. Low confidence goes to a person with the source document open beside the extracted values. That routing rule is the whole economics of the project.
-
Write into the system of record
Extraction that ends in a spreadsheet has moved the work rather than removed it. It has to land in the ERP, the accounting system or the CRM to count.
-
Measure against a labelled set
A few hundred documents with known correct answers, so accuracy is a number you can hold us to rather than an impression after a good demo.
What we build
Invoice and purchase order capture
Line items, totals, tax, supplier matching and duplicate detection, posted into the finance system.
Contract and clause extraction
Dates, parties, renewal terms, notice periods and liability caps, pulled into something searchable.
Forms and scanned paper
OCR first, then extraction, with the handling that scans, rotations and poor photographs actually need.
Product and specification sheets
Supplier documents turned into catalogue attributes, which is usually the blocker behind a thin ecommerce catalogue.
A review interface built for the job
Source document and extracted fields side by side, keyboard driven, because the reviewer is the person whose time you are trying to save.
Ongoing accuracy monitoring
Suppliers change their templates. Accuracy drops quietly unless something is watching for it.
Why document projects disappoint
The technology is rarely the problem. These three are.
The last five percent costs more than the first ninety-five
Any tool demos well on clean documents. Your real pile contains a photograph of a fax. Price the exceptions or the project overruns.
Nobody agreed what correct means
Without a labelled set, accuracy is an argument rather than a measurement, and every disagreement becomes a meeting.
There is no review path
Straight-through processing with no human route for the uncertain cases either blocks the queue or writes bad data into your ledger. Neither is recoverable cheaply.
Reconciliation for finance, procurement and logistics
Invoice and purchase order matching
Three-way matching between the invoice, the purchase order and the goods received note, with the mismatches routed to a person and the rest posted automatically. Tolerances are yours to set: quantity, price, currency and date.
Supplier document validation
Checking that what arrived is what was agreed: the right entity, the right tax registration, the right terms, and an alert when a supplier quietly changes bank details, which is the most expensive kind of document fraud.
Receipt and expense capture
Photographs, PDFs and email attachments turned into coded expense lines, with the VAT or GST split out where it can be established from the document rather than guessed.
Duplicate and anomaly detection
The same invoice arriving twice under different references, an amount outside the normal range for that supplier, a purchase order with no matching delivery. Flagged before payment rather than found during an audit.
Contract clause extraction
Renewal dates, notice periods, price escalation terms and liability caps pulled into something searchable, so a renewal is a calendar entry rather than a surprise.
Approval workflow and system of record
Confident documents post straight through, uncertain ones queue for a named approver with the source alongside, and the result lands in the ERP or the accounting system rather than a spreadsheet.
What people ask before they start
Something not covered here? Ask us directly and an engineer will answer.
Ask a questionThat is a number, not an impression, and we hold ourselves to it. We build a labelled set of a few hundred of your real documents with known correct answers and measure against it, because accuracy claimed from a demo on clean documents means nothing.
They route to a person, with the source document and the extracted fields side by side. High confidence goes straight through. That routing rule is the whole economics of the project, and a system with no review path either blocks the queue or writes bad data into your ledger.
Yes, with OCR first and extraction after, and with the handling that rotations, poor lighting and phone photographs actually need. Price the exceptions honestly: the last five percent of a real document pile costs more than the first ninety-five.
Yes. Three-way matching between the invoice, the purchase order and the goods received note, with tolerances you set for quantity, price, currency and date. Clean matches post automatically and the mismatches go to a person, which is where reconciliation work actually gets paid for.
In your system of record: the accounting system, the ERP or the CRM. Extraction that ends in a spreadsheet has moved the work rather than removed it.
Ready to get started with AI Document Processing?
Tell us what you need. We'll come back with a plan and a fixed price, usually within a day.
-
A free audit first
We'll look at your site and tell you straight what needs doing.
-
Fixed scope, fixed price
Agreed in writing before any work starts. No hidden fees.
-
A real engineer on the call
You talk to the people who do the work, not a sales script.