Paper documents, structured into usable data
Determinds designs, builds, and maintains document pipelines for banks, government offices, and enterprises working from paper and unstructured files, covering recognition, field extraction, validation, and the confidence routing that sends uncertain fields to a person, with Arabic supported as a core requirement from the start.
Banking
Paper invoices with unstructured layouts and Arabic text, converted into structured records for downstream workflows.
Public sector
Field invoicing and collection across secured government locations, where each document forms part of the audit trail.
Healthcare
Referrals, insurance forms, and reports that arrive as photographs and would otherwise be retyped into a clinical system.
01 How the pipeline works
From character recognition to structured, validated data
Character recognition performs well on clean documents. The real value lies in extraction: identifying that a number is the total rather than a line item, on an unfamiliar layout, from a supplier who changed their template last quarter.
Our team starts from your document families, works with a real sample that includes your most difficult scans, and builds extraction logic for the families that carry the most volume. Confidence thresholds decide what passes automatically and what goes to a person for review, and every correction becomes training signal that improves the next batch.
Sample and classify
Our team collects a representative spread of documents and identifies the families. A handful of layouts usually account for most of the volume, and they deliver the greatest return.
Recognize
Optical character recognition tuned for each family, with preprocessing that corrects skew, shadow, stamp, and handwriting interference on photographed pages.
Extract and validate
Field extraction with business rules attached. Totals reconcile against line items, dates fall within plausible ranges, identifiers match a known format, and anything that fails validation is flagged for review.
Route by confidence
High-confidence records go straight through. The rest queue for review in an interface built for speed, and every correction improves the next batch.
Hand off structured data
Records arrive in the downstream system in the format it already expects, whether that is an API, a database, or a file the finance team loads.
02 Banking
Document intelligence in banking
| Engagement | Challenge | What Determinds built |
|---|---|---|
| Document intelligence for a bank | Digitizing paper-based invoices containing unstructured data, including Arabic text | Optical character recognition combined with custom extraction logic, converting documents into structured data usable by downstream workflows. Python. |
| Knowledge and document assistance | Large volumes of operational and document-based information that internal teams needed to search and use effectively | Document intelligence with retrieval and assistance for banking operations teams. |
03 Getting started
A paid proof of concept on your own documents
Send a representative sample, including your most challenging documents, and our team builds a working extraction for your highest-volume document family.
What our team needs from you
- A real sample, including difficult scans
- The fields that matter most downstream
- One person who knows how the current process works
What you receive
- A working extraction on your documents
- Measured accuracy for every field
- A clear view of which document families are worth automating
04 Frequently asked
Questions about document pipelines
Common questions on accuracy, handwriting, and hosting.
What accuracy should we expect on Arabic documents?
Accuracy depends on your documents, so Determinds measures it field by field on your own sample instead of quoting a headline number. Printed fields perform well. Handwritten corrections, stamps printed over text, and photographs taken at an angle are more demanding, and measuring each field separately shows exactly how the fields you care about perform.
How many sample documents do you need to start?
Determinds works from a representative sample that reflects the real spread of your documents, including the most difficult ones. Most archives have a handful of layouts that carry most of the volume, and that distribution shows what is worth automating.
Does it handle handwriting?
Partially. Handwritten numerals and short corrections next to printed fields work well with a review step, and free-form handwritten paragraphs go to a person. Where handwriting carries the operative value, the pipeline routes it to a person by design, so every critical value is confirmed.
Can this run inside our own environment?
Yes. Determinds deploys on-premise or into your private cloud tenancy, with self-hosted models where documents must stay inside your boundary. Because this affects model selection and cost, our team covers it in the first conversation.
What happens when the system is not sure?
The system flags the uncertainty and routes the field for review. Confidence thresholds are set per field rather than per document, so a supplier name and a total are each held to the right standard. Anything below threshold, or anything that fails a business rule such as a total that does not reconcile, queues for review in an interface built for speed, and the correction feeds back as training signal.
What does the proof involve?
You send a representative sample and name the fields that matter downstream. Our team builds a working extraction on your highest-volume document family and reports measured accuracy per field on a held-out set you choose, giving you a reliable figure and a clear view of which families are worth automating.
Document intelligence