What Affinda is
A document AI platform for organisations processing documents in volume. Where OCR turns an image into text, this predicts the fields you need, checks them against rules you define, and flags what fails for a person to review.
The supported set is broad, and the platform is built to hand structured data on to other systems rather than stop at extraction.
What you can do with it
- Upload documents and get predicted fields back
- Write validation rules in natural language
- Route exceptions to manual review instead of letting them pass
- Push results into Dynamics 365, Salesforce, Xero or your own API
Who it is for
- Operations, finance, recruitment, insurance and logistics teams with a steady document flow
- Any team turning unstructured paperwork into usable data
What to watch out for
- Extraction quality follows input quality: scan clarity, table structure, handwriting and how sharply fields are defined
- Anything financial, contractual or compliance-related needs review before it is relied on
- Documents usually contain personal data, so check security posture, retention and processing region first
- Recognising one image a month is cheaper with plain OCR
Pros & cons
✓ What we like
- Structured fields rather than raw text
- Validation rules written in plain language
- Exceptions go to human review
- Integrations and API for downstream systems
! What to watch out for
- Accuracy depends on scan quality and field definitions
- High document volumes require real due diligence on data handling
FAQ
How is this different from OCR?
OCR converts images to text. This predicts the fields you need, validates them, flags exceptions and passes structured data onward.
Which documents does it handle?
Resumes, invoices, contracts, forms, and industry paperwork such as insurance, logistics and financial services documents.
Does it need a large training dataset to start?
The vendor says a proof of concept can run in days without large labelled datasets, though results depend on document type and field clarity.
Last reviewed: 2026-09-15
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