Accounting · Accounting firms and administrations
Document Automation and Intelligent Reading (OCR)
We turn physical documents or PDFs into actionable data automatically. At Possition, we use advanced OCR with AI to read invoices, contracts, and receipts, removing manual typing and human errors in data entry.
What this service covers
Which documents can be processed
- Invoices and receipts.
- Payment receipts and purchase orders.
- Contracts and legal documentation.
- Forms and requests.
- Delivery notes and logistics documentation.
- Scanned documents and photos of documents.
What the system does with each document
- Data extraction: dates, amounts, document numbers, parties.
- Classification by document type.
- Validation against rules and against other systems.
- Detection of missing or inconsistent fields.
- Recording in the corresponding system.
- Organized filing with full traceability.
Human review where it matters
The system processes most documents without intervention and flags as exceptions what it cannot resolve with confidence: poor image quality, ambiguous data, amounts outside range. Those exceptions go to a review queue with the document and the proposed extraction side by side.
Security
- Documents stored with controlled access.
- Traceability of each extraction and each validation.
- Information treated with confidentiality and not used to train third-party models.
Use cases
- Accounting firms that receive documents from dozens of clients.
- Administration teams with supplier invoice flows.
- Organizations digitizing historical paper files.
- Legal teams that process contracts and case files.
Manual entry, traditional OCR, and AI-based OCR: how they differ
Traditional OCR has existed for decades and works well with a fixed format. The problem shows up as soon as there's more than one vendor or document format.
| Criterion | Manual entry | Traditional OCR (template-based) | AI-based OCR |
|---|---|---|---|
| Reads variable formats (different vendors) | Yes, the person adapts | No: needs a template per format | Yes, generalizes without a fixed template |
| Accuracy with poor image quality | Depends on the person | Low: fails easily | Better tolerance; hands off if unsure |
| Cost of adding a new vendor or format | None | High: requires a new template | Low: adjusts with examples |
| Speed | Minutes per document | Seconds, if it matches the template | Seconds |
| On an uncertain case | The person decides | Extracts incorrectly, sometimes silently | Hands off for human review |
| When it fits | Very low volume | One fixed format, always the same | Multiple vendors or variable formats |
If you receive invoices from more than one vendor, each with its own format, traditional OCR forces you to maintain a template for each one — and breaks as soon as one changes its design.
Real use cases
Implementation process
- 1
Diagnostic
We analyze your process and tell you what to automate, how, and what return to expect.
- 2
Implementation
We build the solution connected to your systems, with working deliveries and real tests.
- 3
Operation and improvement
We leave it running, train your team, and measure results.
The full detail, with deliverables per stage, is in how we work.
Guides to decide
Frequently asked questions
How accurate is the automatic reading?
It depends on the type and quality of the documents; that is why it is measured with your real documents during implementation, and the system is calibrated to send to human review everything that does not meet the defined confidence threshold.
Does it work for paper documents?
Yes: scanned or photographed paper enters the same flow. Digitization quality affects accuracy, and that is also assessed during implementation.
Where are the documents stored?
In the storage defined in the project — on-premises or in the cloud — with controlled access and traceability. Location and retention policies are documented.
How is this different from traditional template-based OCR?
Traditional OCR needs a template configured for each exact document format, and fails when something shifts position. AI-based OCR generalizes from examples, without depending on the document always having the same layout.
What if a new vendor has a different invoice format?
There's no need to build a new template: the system adjusts using a few examples of the new format, instead of requiring configuration from scratch.
Does it integrate with our accounting or management system?
Yes, it's the natural next step after extraction: data gets logged directly where it belongs, with no exporting or retyping.
How long does it take to implement?
A flow limited to one document type can be operational in weeks. Processing several document types and formats takes more calibration stages.
How many documents does your team enter by hand?
Send us a sample of the flow and we will show you how it is processed on its own.