SCANNED DOCUMENT TO STRUCTURED DATA
From scanned page to structured row.
Data entry automation can extract supplier, invoice number, dates and amounts from documents into a draft record. Supported confidence scores are one check, not proof of accuracy. The proposed workflow checks required fields, totals, currency, supplier identity and duplicates against the original; a person approves accounting entries before posting.
Updated
What you’ll find here.
Shows one fictional, illustrative invoice next to the structured data it becomes, with the extraction rule behind each field named, rather than a general promise that 'AI can read your documents'.
- Built on documented document-processing services from Microsoft and Google, not an unnamed 'AI tool'
- The before and after example is shown in full, field by field, not summarised
- Supported confidence scores are combined with field checks and human approval before accounting entries are posted
The job, broken down.
- Reading scanned or emailed invoices, receipts and forms
- Extracting the fields a specific document type needs
- Checking supported confidence scores and flagging missing or uncertain required fields
- Flagging anything below the threshold for a person
- Writing confirmed data into the spreadsheet or accounting system
The same job, handled.
| The task today | With AI doing the legwork |
|---|---|
| Invoice details are typed in by hand from a scanned copy | Fields can be extracted into a draft and checked against the original |
| A mistyped figure is only caught if someone notices later | Confidence and validation checks support a person's approval before posting |
| The original document gets filed separately from the data | The original is retained or linked where the destination supports it |
Built in steps.
Connect
We connect the inbox, scanner folder or upload point documents already arrive at, so nothing needs saving somewhere new first.
Extract
Each document is read using a document-processing service, and the fields your business actually needs, supplier, invoice number, date, amounts, are pulled out by name.
Check confidence
Where the selected model supplies a confidence score, it is checked against an agreed threshold. Missing scores or required values also require human review rather than automatic posting.
Hand over
Required fields, supplier identity, currency, invoice number, totals and duplicate checks are reviewed against the original. A person approves accounting entries before posting; the original document is retained or linked where the destination supports it. Payment is outside this scope.
Do the maths.
Typing supplier invoice details into the accounting system by hand from a scanned copy.
| Input or step | Value |
|---|---|
| Times a week | 30 |
| Minutes each time | 6 |
| People doing it | 1 |
| Cost per hour | £14 |
| Hours a year | 156 (about 21 working days) |
| Cost a year | £2,184 |
Illustrative inputs, not a client's figures: thirty scanned invoices a week, six minutes each to read and type up by hand. Change the inputs to your own with the calculator. See how we calculate this.
These are illustrative workflow scopes. The audit checks the actual software, supported access, licences, permissions and human review rules before a build is agreed.
A worked example: one invoice, before and after
This is an illustrative, invented invoice, not a real supplier’s document.
Before, as a scanned page might read:
EXAMPLE SUPPLIER LTD
Invoice No: INV-20456
Date: 12/09/2026
Due: 12/10/2026
Description: Fixings, assorted, x40 boxes
Net: 340.00
VAT: 68.00
Total: 408.00After, as structured data:
| Field | Value | Extraction rule |
|---|---|---|
| Supplier | Example Supplier Ltd | Taken from the document’s letterhead line |
| Invoice number | INV-20456 | Matched against a label such as “Invoice No” |
| Invoice date | 2026-09-12 | Matched against a label such as “Date” |
| Due date | 2026-10-12 | Matched against a label such as “Due” |
| Net | 340.00 | Matched against a currency figure next to “Net” |
| VAT | 68.00 | Matched against a currency figure next to “VAT” |
| Total | 408.00 | Matched against a currency figure next to “Total” |
Each row above is an intended field mapping: the workflow looks for the relevant label and value, then checks the extracted result against the original. When a document does not match the expected layout closely enough, for example a supplier’s invoice in an unfamiliar format, a confidence score may not reveal the problem. The proposed workflow therefore validates required fields, totals and layout assumptions and sends failed checks for human review rather than relying on confidence alone.
What is actually doing the reading
The extraction itself runs on a document-processing service built for exactly this job. Microsoft describes its Azure AI Document Intelligence as “a machine-learning based OCR and intelligent document processing service to automate extraction of key data from forms and documents,” and Google’s Document AI is described the same way: it “takes unstructured data from documents and transforms it into structured data,” including “parsing receipts and invoices for expense report validation” as a named use case. Neither vendor publishes a guaranteed accuracy figure for an arbitrary document, which is exactly why the confidence threshold and human check sit on top of the extraction, rather than trusting every field blindly.
Where this saves the most time
The businesses that benefit most are the ones with a steady stream of the same kind of document, supplier invoices, delivery notes, expense receipts, because the extraction rules are built once for that document type and then apply to every one that follows. A one-off, unusual document is rarely worth automating on its own; a weekly stack of the same invoice layout usually is.
On the tools you already use.
- Xero, QuickBooks or similar accounting software
- A scanner or phone camera
- Google Sheets or Excel
- n8n
- Make
Who needs it most.
What it costs.
Every price is published and fixed in writing before any work starts.
AI Opportunity Audit
In person across Leeds and West Yorkshire, or by video call. A written report of your top 5 automations, yours to keep.
AI Lead & Ops Sprint
Enquiry capture through to booking, follow-up and CRM logging, plus one ops automation. The monthly fee covers monitoring, tuning, a monthly review and up to 2 hours of changes.
Fractional AI Team
Ongoing AI builds, staff training and a monthly review.
Prices exclude VAT where applicable.
The build guarantee. We agree written acceptance criteria before any work starts. If the build doesn’t meet them, we fix it at no extra cost, and if we can’t, you don’t pay for it.
Where the facts come from.
- What is Azure AI Document Intelligence?Microsoft Learn
- Document AI overviewGoogle Cloud
Questions, answered straight.
What does data entry automation actually read?
It reads documents such as scanned or emailed invoices, receipts and forms, using a document-processing service such as Microsoft's Azure AI Document Intelligence or Google's Document AI, both of which are built to turn unstructured documents into structured, database-ready data.
What happens if a field cannot be read confidently?
Confidence-score coverage depends on the chosen model and field. The proposed validation rule flags missing required values or scores and scores below the agreed threshold for human review before posting. Confidence alone does not establish accuracy.
Is this the same as OCR software I could buy myself?
It uses the same kind of underlying document-processing service, but the rules for which fields to pull, where to send them and when to flag a person are built around your specific documents and your accounting or record system, not a generic template.
What does data entry automation cost?
A workflow like this is usually a fixed-price package agreed in writing before any work starts (most clients begin with the AI Opportunity Audit at £495, credited against a build). The Fractional AI Team at £1,500 a month covers ongoing work if you want it looked after. Prices exclude VAT where applicable.
Can it handle handwritten documents?
It depends on how legible the handwriting is; printed and typed documents are read far more reliably than handwriting. The AI Opportunity Audit checks a sample of your actual documents before anything is quoted.
Where to go next.
Extracted. Checked. Recorded.
Start with a 20-minute call.
Tell us the job that keeps coming back. We’ll tell you honestly what’s worth automating and what isn’t, and put a number on each one.