AI document processing
AI document processing that reads your paperwork and shows its confidence
The hard part is not reading a clean invoice. It is knowing which fields to trust. We build the reading, the checking and the human review as one pipeline.
In short
AI document processing turns invoices, receipts, forms, contracts, statements and emails into structured data. A pipeline receives the file, classifies it, extracts fields with OCR and a language model, validates them against rules and your records, and routes low-confidence items to a person. The result goes to your accounting, CRM or database.
The pipeline, stage by stage
| Stage | What happens | What can go wrong |
|---|---|---|
| 1. Intake | Files arrive by email, upload or folder | Photos at an angle, password-protected PDFs |
| 2. Classify | Invoice, receipt, contract or other | New document types nobody planned for |
| 3. Read | OCR for scans, direct text for digital PDFs | Low-quality scans, handwriting |
| 4. Extract | A model returns fields in a fixed format | Wrong field picked, such as ship-to for bill-to |
| 5. Validate | Totals add up, dates make sense, supplier exists | Rules too strict or too loose |
| 6. Review | A person checks items under the threshold | Queue grows if the threshold is set too high |
| 7. Export | Data goes to your system through an API or file | Duplicates on retry |
Stage 5 does the most for accuracy. A model may read 1,240.00 as 1,210.00, but a check that line items add up to the total catches it. See what is OCR for how the reading step works.
Which documents are easy and which are hard
| Document | Difficulty | Why |
|---|---|---|
| Digital PDF invoices from a few suppliers | Low | Consistent layout, real text inside |
| Invoices from hundreds of suppliers | Medium | Every layout differs, but fields are standard |
| Scanned receipts and photos | Medium to high | Image quality drives accuracy |
| Contracts and long agreements | High | Clauses vary, reading needs judgment and a lawyer's check |
| Handwritten forms | High | Accuracy limited, so review share is large |
We say honestly when a document type is not ready for automation. For contracts we usually extract and flag clauses for a lawyer rather than judge them.
Measure accuracy on your own documents
A vendor claim of 99 percent accuracy means little until it is measured on your files. This is our method.
- Collect 100 to 200 real documents across suppliers, formats and quality levels.
- A person fills in the correct values for each field, creating the answer key.
- The pipeline runs on the same files. We compare field by field, not document by document.
- We report accuracy per field. Total amount and date may be high, while line-item descriptions lag.
A worked example: 150 invoices with 8 key fields each give 1,200 field checks. If 1,188 are correct, field accuracy is 99 percent. If those 12 errors fall on 9 of the 150 invoices, only 94 percent of documents are fully right. Both numbers matter, and the second is the one your finance team feels. Review thresholds are set so that doubtful fields reach a person before they reach your ledger.
Want AI document processing done for you?A short call, a written plan and a fixed quote.
Get a proposalOur process
- Document auditWe look at your documents, volumes, systems and the rules your team applies when checking by hand.
- Answer keyTogether we build the labeled sample used for all measurements.
- PrototypeA first pipeline on the sample with field-level scores.
- Rules and review screenValidation checks and a simple screen where reviewers see the original next to the extracted values.
- IntegrateConnection to your accounting, ERP, CRM or storage, with duplicate protection.
- Parallel runThe pipeline runs next to your manual process until the results agree to your satisfaction.
What you get and how long it takes
- A working pipeline connected to your inbox or storage and your target system.
- A review screen for exceptions, with an audit trail of who changed what.
- The answer key, test script and field-level accuracy report.
- A data note covering storage, retention and the model provider's role, for GDPR, UK GDPR or CCPA review. Invoices and forms often hold personal data.
One document type from a handful of sources can reach a parallel run within a few weeks. Many layouts, scans or ERP integrations add time. The quote gives dates for your scope.
How we report and related work
During the parallel run you receive a weekly note with documents processed, share sent to review, field accuracy and corrections made. Afterwards the same figures are in the review screen.
We have no published document processing case study, and no real project in our portfolio that fits closely enough to borrow. We would rather say that than stretch an unrelated project. For automation around the pipeline, see AI integration.
Updated 30 October 2026
Questions, answered
Can it read scanned and handwritten documents?
Does it replace our data entry staff?
Where do our documents go?
Can it handle several languages?
What if a document type changes?
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Send a few sample documents
Share anonymized examples on the contact page and we will say which fields are easy to read and which will need review.