Process automation / Documents
Documents that
process themselves.
Capture, read, extract and classify the data in your documents —invoices, contracts, forms— without typing it by hand. The data enters once, correct, and travels on its own to your systems.
What it is (IDP)
The document enters once,
and the data travels on its own.
Document automation —or IDP, for Intelligent Document Processing— means automatically capturing, reading, extracting and classifying the data that someone today types by hand from invoices, contracts, delivery notes or forms.
The journey is always the same: documents come in through any channel (email, scanner, portal, phone photo), an OCR + AI engine reads them, the relevant fields are extracted, the document type is classified and validation rules are applied. When something is unclear, a person reviews it; when everything adds up, the data is integratedstraight into your ERP, your document manager or your database.
It is not the OCR of old. Classic OCR turns an image into text and little more: it needs fixed templates and gets lost the moment the format changes. IDP understands the document —it tells an amount from an IBAN, it knows which type of invoice it is looking at— and works withunstructured, varied documents, the ones every supplier sends their own way. It is part of our process automation practice, and it is usually the first link: almost every process starts with a document.
Problems it solves
What every document
is costing you.
Retyping data from every document by hand
Someone copies amounts, dates, references and invoice numbers by hand, document after document. It is slow, tedious and adds no value.
Keying errors that get expensive
One wrong digit in an amount or a mistyped IBAN spreads through the accounts and surfaces weeks later, when it is already costly to fix.
Slowness and bottlenecks
Invoices and contracts pile up waiting for someone to enter them. The process moves at the speed of the inbox.
A thousand formats, a thousand channels
PDF, scanned paper, phone photo, email attachment, every supplier with their own template. No rigid tool covers that variety.
Data trapped inside the document
Valuable information lives inside PDFs and paper where no system can search it, cross-reference it or analyse it.
Hard to audit
“Which invoice did this entry come from?”, “who validated this contract?”. Without traceability, every review becomes a manual hunt.
What you can automate
The documents
to start with.
The best candidates are high-volume documents with clear data and a more or less recognisable template. For example:
- Supplier invoices
- Delivery notes
- Orders and purchase orders
- Contracts and annexes
- Forms and applications
- ID documents and onboarding (KYC)
- Emails with attached documents
- Tickets, receipts and expense notes
Use cases by department
A case for every
area.
| Department | Document to automate | Result |
|---|---|---|
| Finance | Receiving and posting invoices (accounts payable) | Automatic posting, fewer cycle days and zero misfiled invoices. |
| Legal | Reading and classifying contracts | Parties, dates, clauses and expiry dates extracted and searchable instantly. |
| Procurement | Matching purchase order, delivery note and invoice | Discrepancies caught before payment; automatic three-way matching. |
| HR | Document onboarding for employees | New hires with validated, filed paperwork, no gaps and no forgotten access. |
| Customer service | Incoming forms and requests | Data pushed into the system and routed to the right team with no retyping. |
The core idea
Data you never retype is data that doesn't go wrong.
Benefits and results
What you can
expect.
Processing speed
A document goes from needing minutes of typing to being processed in seconds. The read, validate and post cycle shrinks dramatically.
Accuracy
By reading and validating with rules instead of typing, transcription errors and the mismatches that carried from stage to stage disappear.
Traceability and audit
Every data point stays linked to its source document, to who validated it and when. Auditing turns from a hunt into a simple query.
Hours saved
The team stops spending whole days keying data and focuses on reviewing exceptions and on the work that truly requires judgement.
Compliance and controlled retention
Encrypted documents, role-based access and a retention policy that removes what should no longer be kept. GDPR by design.
Capacity that scales
A month-end or seasonal spike is absorbed without hiring extra staff: the same system processes 100 or 10,000 documents.
How we do it with control
Automatic, but
under control.
Automating is not the same as looking away. AI does the heavy lifting and a person keeps the last word where it truly matters.
Human validation when in doubt
When the system is not sure, it does not guess: it sends the document to a person to confirm or correct. AI proposes; the human decides where it matters.
Confidence thresholds
Every extracted field carries a confidence level. Above the threshold it passes on its own; below it, it jumps to review. You decide where the line sits.
Business rules
Validations specific to your operation —amount against order, valid tax ID, consistent dates— that catch what a plain OCR would never see.
Security and GDPR
Encryption in transit and at rest, least privilege, auditable logs and processing in line with GDPR, with the required data-processing agreements signed.
Continuous model improvement
Every human correction teaches the system. Over time, more documents pass on their own and fewer exceptions are left to review.
Common integrations
The data reaches
your systems.
Working with a system that is not on the list? If it has an API or supports imports, we can almost always push the data into it.
The CPPA methodology
From a stack of paper to data
that processes itself.
Analyse documents and volume
We collect a real sample of your documents, measure volumes and variety, and identify which fields matter and which exceptions appear. This is the baseline we measure savings against.
Design capture, extraction and validation
We define where documents come in, which data is extracted from each type, the validation rules and —crucially— the confidence thresholds and the points where a person steps in.
Build and integrate with your systems
We assemble the OCR and AI pipeline and connect it to your ERP, document manager or database through robust APIs. We test it with real documents, not lab ones.
Run, measure accuracy and improve
We launch in phases, measure accuracy and the share that passes without intervention, and tune rules and model until savings stabilise. The system is documented and ready to scale.
Automation examples
How it looks
in practice.
Accounts payable (invoice → ERP)
- Invoice received by email or portal
- Document capture and OCR
- Extraction of supplier, amount, taxes and lines
- Matching against the order and delivery note
- Human review only if there is a mismatch
- Automatic posting into the ERP
Contract digitisation
- Scanned contract or PDF
- Classification by contract type
- Extraction of parties, dates and key clauses
- Validation of expiry dates and renewals
- Indexed, searchable archive
- Automatic renewal alerts
Customer document onboarding
- Documents received (ID, supporting papers)
- Reading and identity verification (KYC)
- Extraction and cross-checking of data
- Human validation in doubtful cases
- Creation in the CRM and systems
- Complete, traceable file
Form processing
- Form on paper or PDF
- OCR and field recognition
- Structured extraction of answers
- Validation and completeness rules
- Push to the destination system
- Routing to the responsible team
Risks and mitigation
How not to do it.
Automating documents well is as much about knowing how to read them as knowing when to distrust them. These are the risks we see most and how we mitigate them:
Scan and OCR quality
A blurry or skewed document lowers accuracy. We mitigate it with image pre-processing, confidence thresholds and review of everything that does not clear the bar.
Non-standard documents
Unusual templates or fields confuse any engine. We resolve them with human validation and continuous learning of the model.
GDPR and retention
Documents contain personal data. We work with encryption, least privilege and a retention policy that removes what should no longer be kept.
Over-reliance on AI
Taking everything the machine extracts at face value is a mistake. We keep sample-based review and human control over amounts and sensitive cases.
New suppliers and templates
Every new supplier brings a different format. We plan for a supervised learning period before letting it pass automatically.
Fragile integrations
Pushing data through clicks or screenshots breaks at the first interface change. We prefer APIs and stable contracts between the document engine and your systems.
Frequently asked questions
What is the difference between OCR and IDP?
Which documents can you process?
What accuracy can be achieved?
Does it integrate with my ERP or accounting?
Is it secure and GDPR-compliant?
How long does it take to implement and how much is saved?
Related services
Keep exploring.
Workflow automation
Almost every document is just the trigger for a larger process. Workflow automation chains together what happens after the data is extracted.
View →
AI agents
When a document requires interpreting free text or deciding with judgement, an AI agent handles the step, supervised by a person.
View →
Business process automation
If the challenge is not a document but a full end-to-end process, BPA redesigns it in its entirety.
View →How many documents are you still typing by hand?
Request a proposal →Want to see how we analyse a real operation end to end? Read ourCPPA X-RAY on 100 Montaditos.