The problem
Documents arrive as PDFs, scans, emails and attachments in no consistent order. Someone has to open each one and type the same fields into another system.
A missed invoice or contract has a real cost, but the current process has no queue that shows what is waiting or what failed.
Exceptions are handled by the most experienced person, which makes the process dependent on one or two people.
There is no audit trail of what was read, what was extracted and what was changed before the record was written.
What you get
Outcomes
- Faster turnaround from receipt to record.
- Fewer keying errors because values are validated before the write-back.
- An exception queue that shows what needs a human, instead of hiding it.
- An audit trail per document: source, extraction, confidence, validation and outcome.
- A measurable manual-touch rate before and after.
Deliverables
- A sample audit covering the document types, layouts and failure modes.
- A field schema and validation rules for each document type.
- Classification and extraction with confidence scores.
- Routing to the destination system or a structured export.
- A review queue for low-confidence or invalid items.
- Duplicate detection and a record of what was processed.
- Error handling and a runbook.
- A before/after measurement of manual touches and turnaround time.
Fit and boundaries
This is a good fit when
- Roughly 100 or more documents or emails a month.
- A small number of repeatable document types.
- A destination system or a defined export.
- Someone who owns the validation rules and the exception queue.
This is not a fit when
- Very low volume, where a person is genuinely cheaper.
- Documents that change format every time and have no stable fields.
- A fully automated decision with no human review in a regulated process.
- No access to real samples for testing.
How it works
- 01
Audit the real documents
A sample of real items, including the ugly ones. The variety in the sample decides the extraction approach more than the vendor demo does.
- 02
Define the fields and rules
What must be extracted, what is optional, what makes an item invalid, and where the result goes. This is the contract the automation is measured against.
- 03
Build the pipeline
Classification, extraction, validation, routing and the review queue. Low-confidence items are routed to a person rather than guessed.
- 04
Measure and hand over
The same sample is processed again. You get the extraction accuracy, the manual-touch rate and the queue for the exceptions.
Pricing and timing
Fixed-price engagement
€2,500 to €5,000 2 to 3 weeks · EUR, VAT not applicable at present
Fixed price for one document pipeline, two to three weeks, including the review queue and a measured sample.
- Written scope and acceptance criteria before work starts.
- 40 to 50 percent deposit reserves the calendar slot.
- Milestone invoices and 14-day payment terms.
- A checkpoint where either side can stop with paid work settled.
Questions
How accurate is document extraction?
It depends on the document variety and the fields. I measure accuracy on your samples before committing to a scope. The workflow routes low-confidence items to a human instead of pretending the model is always right.
Can it read scanned PDFs?
Usually, with OCR before extraction. The quality of the scan matters. A clean digital PDF is easier; a photographed document with handwriting needs a different approach and a wider review queue.
Where do the extracted values go?
Into the system you already use, through an API or a structured export. The write-back is validated first, so a malformed value does not reach the record.
Is our data used to train a model?
No. The work is inference and integration. If a fine-tune is ever justified, it is a separate, explicit scope with a data agreement, not a hidden default.
Page last updated: October 2026