AI document processing
Pulls the data out of contracts, delivery notes and forms, validates it and sends it to your systems. No retyping.
Which documents can AI process?
Any document that a person reads to pull out data and copy somewhere else. These are the ones we automate most:
- Contracts: parties, start and end dates, auto-renewal, amounts, penalties and the clauses you want to monitor.
- Delivery notes: supplier, references, quantities and signed receipt, matched against the purchase order.
- Forms and applications: customer onboarding, job sheets and warranty claims, handwritten or in PDF.
- Identity documents: ID cards, residence permits or passports for customer or tenant onboarding, with expiry checks.
- Technical or legal documents: deeds, land registry extracts, certificates or reports that need summarising.
- Invoices: they have their own workflow, explained in invoice automation.
How is traditional OCR different from AI document processing?
Traditional OCR turns an image into text. To pull one specific value it needs you to tell it which zone of the page it sits in, so it fails as soon as the document layout changes.
AI document processing combines OCR with a language model that understands the content. You ask for "contract end date and whether it auto-renews" and it finds the answer even if it sits on page 7 and is worded differently. It also classifies: it tells a lease from a services agreement, or a payslip from a tax certificate, and decides what to do with each.
03How is the extracted data validated?
Extraction is not enough: a misread value that enters your ERP is worse than a missing one. So every workflow has validation rules. We check formats (a valid tax ID, a coherent date), cross-checks with your systems (the customer exists, the order is open, quantities match) and the model's own confidence level.
What passes the rules goes through on its own. What doesn't reaches a person with the document on one side, the data on the other and the doubtful field highlighted. Reviewing a document this way takes seconds, against the minutes it takes to type it.
04Example: a law firm reviewing client contracts
A commercial law firm receives dozens of supplier contracts each month from its clients for review. A junior associate spends hours reading them and filling in a sheet with parties, term, notice period, limitation of liability and governing law.
With the AI workflow, the contract is uploaded to the document management system and within a couple of minutes the complete sheet appears, each value linked to the paragraph it comes from. The system flags clauses that depart from the firm's criteria, such as auto-renewal without notice or a disproportionate penalty, and generates a one-page summary. The associate reviews the sheet and spends their time on the flagged clauses. Documents are processed on European servers and are not used to train models, a requirement for professional privilege; more detail in AI for law firms.
How we do it, step by step
- We choose the documents
We select the document type with the most volume or the most typing hours and gather a real sample.
- We define what to extract
The fields, formats, classification and summary you need, and where each piece of data has to end up.
- We tune the extraction
We test with your documents, including the worst scans, until the accuracy rate is stable.
- We add validations
Format rules, cross-checks with your systems and confidence thresholds that decide what goes to review.
- We integrate and measure
We connect your ERP, CRM or document system and measure accuracy, volume and time saved each week.
Frequently asked questions
How much does AI document processing cost?
A workflow for one document type, with extraction, validation and integration with one system, is budgeted per phase with a fixed price agreed before we start. The cost depends on the number of document types, formats, validation rules and systems to connect. Each additional document type is usually one more workflow. AI cost per document is a few cents, though long contracts use more than a delivery note, and it is paid directly to the provider.
Is it safe to process confidential documents with AI?
Yes, with the right configuration. We use providers with data processing agreements, EU-region processing when possible and options that stop your documents being used to train models. For very sensitive data we can use models hosted on your own infrastructure.
Can it read handwritten documents?
Yes, current models read reasonably clear handwriting, such as forms or job sheets. Accuracy is lower than with printed text, so we tighten the validations to send doubtful fields to review.
Can it be used to verify customer ID documents?
It can extract the data from an ID, check the format and expiry date and compare them with what the customer entered. Legally valid identity verification (KYC) requires specialised providers, which we integrate if your industry demands it.
What is the difference between AI document processing and a document management system?
A document management system stores and organises files. AI document processing reads what is inside and turns the content into usable data. They are normally combined: the AI processes the document and the system archives it with the data as metadata.
What is intelligent document processing (IDP)?
Intelligent document processing is the industry term for what this page describes: OCR plus machine learning to capture, classify and validate data from documents. Modern IDP uses large language models, which handle changing layouts without per-template setup.
Keep exploring
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AI invoice processing: reading, validation, approval and posting to QuickBooks, Xero, Sage or Odoo without typing.
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Client intake, scheduling, drafts and deadline tracking for law firms, with attorney-client privilege protected.
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AI for accountants
Invoice capture, reconciliation, document chasing and deadline reminders connected to QuickBooks, Xero or Sage.
LLM (large language model)
A large language model (LLM) is an AI model trained on enormous amounts of text to predict the next word, which lets it understand questions and write, summarize, translate, classify and reason about text.
RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation) is a technique where an AI system first searches your documents or databases for relevant information, then generates its answer using only that content instead of relying on what the model memorized.
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