AI document processing services for extraction you can measure and trust

What we build

Every document type has its own hard part. Invoices are easy to read and hard to match to purchase orders. Contracts are easy to match and hard to read. Handwritten forms defeat most OCR. We scope a project by the document types and the fields that matter, not by the technology.

How an AI document processing pipeline works

1. Ingest: upload in the browser, email-in, an SFTP drop or an API. Files go straight to object storage through presigned URLs, never through your web server.
2. Pre-process: split multi-document PDFs, fix rotation and skew, convert images, detect whether a PDF already has a text layer so you do not pay for OCR you do not need.
3. Classify: decide the document type (invoice, statement, ID) with a cheap classifier, so each type goes to the extractor that suits it.
4. OCR and layout: read text, tables, key-value pairs and checkboxes with positions on the page.
5. Extract: map the text to your fields, using a template for fixed layouts or a language model for variable ones, with a confidence score per field.
6. Validate: run business rules on the result: totals add up, dates are in range, the supplier exists, the VAT number has the right format.
7. Review: anything below a confidence threshold or failing a rule goes to a review queue with the page image beside the fields.
8. Export: push approved data to your ERP, CRM or database by API, CSV or webhook, with an audit trail of who changed what.

OCR engines versus LLM extraction

Cloud OCR services are fast, cheap per page and return coordinates, so a reviewer can see exactly where a value came from. Language models understand variable layouts and free text far better, but cost more per page and need guarding against invented values. Most production systems use both: OCR for the text and positions, a model for mapping messy text to fields. Prices below are pay-as-you-go list prices from each vendor's pricing page, checked October 2026; they vary by region and volume.

A worked example at 10,000 pages a month: plain OCR on any of the three clouds is about $15, Azure layout extraction about $100, Textract with forms, tables and queries about $700. A language model pass on top typically costs less than the richer OCR features if you send only the relevant pages. We model this per document type during discovery.

Validation rules and human review

Extraction accuracy is never 100%, so the system has to know when it is unsure and route that work to a person. The review screen matters as much as the model.

• Confidence thresholds per field, not per document: a low-confidence IBAN is reviewed even if the rest of the invoice is clean.
• Cross-field rules: line items sum to the subtotal, subtotal plus tax equals the total, the period end is after the period start.
• Reference checks: supplier, customer or policy number looked up in your system of record before the data is accepted.
• Duplicate detection: same supplier, amount and date, or the same file hash, flagged before payment or posting.
• Review queue: page image with the extracted value highlighted, keyboard shortcuts, and every correction stored as training and evaluation data.
• Never invent: if a field is not on the page, the model returns null, and the prompt and schema say so explicitly.

Measuring accuracy field by field

"95% accurate" means nothing until you say what was counted. We measure on a labelled test set of your own documents, split by document type and layout, and report:

Multi-tenant SaaS or internal tool

Multi-tenant document products need the same foundations as any SaaS; our multi-tenant SaaS development page covers isolation and billing in depth.

What AI document processing costs

The MVP figure comes from our app development cost calculator, which you can adjust feature by feature. The AI document processing cost guide explains each line, and the intelligent document processing guide covers the approach in more depth. OCR and model fees are paid to the vendor and come on top.

Privacy and compliance

• Data stays in your chosen cloud region; we pick an OCR vendor and model endpoint in the same region where residency matters.
• Personal data in documents is covered by GDPR or UK GDPR, and medical documents by HIPAA in the US; we sign the relevant agreements with each vendor and set retention periods for originals and extracted data.
• Language model calls go through enterprise endpoints such as AWS Bedrock where documents must not leave your account.
• Every extracted value keeps a link to its page, and every human correction is logged with the user and time.

Document processing we have shipped

• Edge OCR: a confidential multi-tenant AI document processing SaaS. OCR runs on AWS Lambda with template-based and free-form extraction, AWS Bedrock handles document understanding and Adobe PDF Services handles complex PDFs. Each organisation gets its own MongoDB database. Users bulk upload by drag and drop, preview with Tesseract.js, edit extracted values and export to CSV or PDF, and Stripe enforces Free, Starter, Growth and Scale plans with scan limits, overages and rollover.
• GovDoc AI: reads government RFPs, RFQs, SOWs and MSAs that can run to hundreds of pages, extracts 50+ data points per contract with Claude on AWS Bedrock, and gives page-level references for every value. A self-correcting three-pass analysis catches sections the first pass missed, and a typical deep analysis of a large contract takes two to four minutes.

When the goal is answering questions over a document library rather than extracting fields, see RAG development. To route extracted data into approvals and other systems, see AI automation, or go back to AI and data services.