AI Document Data Extraction Services
Someone in your business opens a PDF, reads six fields off it, and types them into another system. A few hundred times a month. Extraction automates the reading and the typing, and routes the cases it is unsure about to a person instead of guessing.
Discuss your projectDocuments we automate
The pattern that makes a document a good candidate: fixed fields, high volume, and a destination system that already exists.
Supplier invoices
Supplier, invoice number, date, line items, tax, total, purchase order reference. The highest-volume case in most businesses and usually the first one worth automating.
Purchase orders and delivery notes
Matched against invoices so the three-way match stops being a manual comparison across two screens.
Contracts and agreements
Parties, dates, term, renewal and notice windows, value, governing law. Most valuable as a register of what you have agreed to, because renewal dates are what get missed.
Bank and card statements
Transaction lines out of PDF statements where no feed exists, including the multi-page tables that break naive parsers.
Forms and applications
Onboarding forms, claims, applications — including scanned and photographed pages, which need different handling from digital PDFs.
Identity and compliance documents
Where you must record fields from documents a customer uploads. Retention and access rules are designed in from the start, not added afterwards.
How it works
- 01
Documents arrive
A watched mailbox, a folder, an upload in your own application, or an API call. Most clients start with a mailbox because that is where the documents already go.
- 02
Classify, then extract
First what kind of document it is, then the fields for that kind. Digital PDFs are read directly; scans and photographs go through OCR first, which is where accuracy is won or lost.
- 03
Validate against what you know
This is the part that distinguishes a usable pipeline from a demonstration. Does the supplier exist in your system? Do the line items sum to the total? Is the date plausible? Is this invoice number already recorded? Arithmetic and cross-reference checks catch errors a model cannot catch about itself.
- 04
Route by confidence
Above an agreed threshold, and passing validation, it posts automatically. Below it, or failing a check, it goes to a review queue with the extracted values pre-filled and the source document beside them. A person confirms or corrects in seconds rather than typing from scratch.
- 05
Write to the destination
Into your accounting system, ERP, database, or spreadsheet. Extraction with no destination is a demo; the integration is the half of the project that delivers the saving.
Where the data goes
Chosen against the system that holds the record today, not the one we would prefer to integrate with.
- Accounting: QuickBooks, Xero, Sage, or a draft bill awaiting approval rather than a posted transaction.
- ERP and inventory systems, including the ones whose integration path is a nightly file rather than an API.
- Your own database or application, where the document is part of a workflow you already run.
- A spreadsheet or data warehouse, where the immediate need is reporting rather than posting.
- The original file preserved alongside the extracted fields, so any figure can be traced back to the page it came from — usually an audit requirement, and always useful when someone disputes a number.
Custom pipeline or an off-the-shelf tool
Products like Rossum, ABBYY, Docparser and Nanonets are good at this and we do not claim otherwise. Here is where each makes sense.
| Off-the-shelf tool | Custom pipeline | |
|---|---|---|
| Time to first results | Days | Weeks |
| Cost model | Per document or per page | Build cost, then hosting and model usage |
| Cost at high volume | Rises in step with volume | Largely flat |
| Unusual document layouts | Depends on the vendor's templates | Built for yours |
| Validation against your own data | Limited | Any rule you can express |
| Destination systems | The connectors offered | Anything with an API or a file drop |
| Data residency and retention | The vendor's policy | Your choice |
| Who maintains it | The vendor | You, or us on a retainer |
Our usual advice: if a vendor handles your document types and the price at your volume is acceptable, buy it — we will tell you so and lose the build. Custom earns its cost at high volume, with document types vendors handle badly, where validation has to consult your own records, or where the data cannot leave your infrastructure.
Common questions
Tell us what you need
Describe the problem and we will come back with scope, timeline, and a rate. No obligation to proceed.
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