Invoice and receipt capture
Header and line-item extraction into the finance system, with totals validated against the arithmetic.
The problem
The document arrives as a scan, and everything after that is a person retyping. It is slow, it introduces errors nobody catches, and it puts a hard ceiling on how fast the process behind it can run. Arabic makes it harder: connected script, diacritics, mixed Arabic–English documents and poor scan quality defeat most general OCR, so the work stays manual long after the English-language equivalent was automated.
Who this is for
- Back-office and shared-services operationsVolume processed without the retyping step
- Finance and claims teamsInvoices and forms captured into the system, with the doubtful ones flagged
- Records and archive ownersA searchable archive instead of a room of boxes
What people use it for
Forms and applications
Structured fields from a known layout, including handwritten entries where legibility allows.
Identity and supporting documents
Capture into onboarding or claims workflows, with data minimised and retention set deliberately.
Archive digitisation
Bulk conversion of historical paper into a searchable, retrievable store — often the input to a knowledge assistant.
What it needs to work
- Representative samples — including the bad scans, not the clean ones
- The fields you actually need, which is usually fewer than the document contains
- The target system and format for the extracted data
- Your position on retention and on personal data in the images
How it works
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Prepare the image
Deskew, denoise and enhance. More accuracy is won here than by changing model, especially on poor scans.
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Recognise the text
Arabic and English, including mixed documents, connected script and tables where the layout carries meaning.
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Extract the fields
The values you asked for, located by layout and by context rather than by fixed coordinates that break on a new template.
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Validate
Checksums, arithmetic, format and cross-field consistency — an invoice whose lines do not sum is caught before it is posted.
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Route by confidence
High-confidence extractions flow through; the rest go to a person with the field highlighted on the image.
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Learn from corrections
What the reviewer changed feeds back, so a recurring template stops needing review.
How we deliver it
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Sample assessment
Real documents at real quality. This is where achievable accuracy per document type is established, on your material.
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Configure and tune
Per document type, with the validation rules that catch the errors that matter to you.
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Review workflow
The confidence threshold and the reviewer screen, because how fast a person can correct decides the real throughput.
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Integrate and scale
Into the downstream system, then widened by document type as accuracy is proven on each.
Where it runs
- On-premises, where documents must not leave — common for identity and finance material
- Private cloud on your tenancy
- API or batch, integrated into the workflow that consumes the output
Security and governance
- Documents processed inside your boundary where policy requires it
- Field-level confidence exposed, so review effort follows risk
- Retention of images and extracted data set separately — you rarely need both for as long
- Personal data minimised at extraction: capture the fields needed, not the whole document
Timeline
A single well-defined document type moves quickly. What determines the schedule is the number of distinct layouts and the quality of the worst scans you must still handle — and both are established in the sample assessment rather than estimated from a description.
What you receive
- Measured accuracy per document type and per field, on your own samples
- A configured extraction pipeline with validation rules
- A reviewer workflow with confidence-based routing
- Integration into the system that consumes the data
- A retraining route for new templates, runnable by your team
Related work
Published projects where we did this.
What this does not do
Accuracy depends on scan quality and on the document, and no honest supplier quotes one figure for all of it. Handwriting is materially harder than print, poor photocopies of Arabic text remain the hardest case in this field, and some documents will always need a person. The right design is not perfect extraction — it is confidence routing, so the difficult ones reach a human instead of being guessed at.
Questions we are asked
What accuracy do you achieve?
It depends entirely on your documents, so we measure it on your samples and report it per document type and per field. A single headline number across mixed document quality would be marketing rather than information.
Does it handle handwritten Arabic?
Partially, and it is the hardest case. We assess it on your samples and are explicit about which fields are viable and which should stay with a reviewer.
About the figures on this page
This page describes capability and method. It does not publish accuracy figures, throughput numbers or delivery dates, because those depend on your data, your systems and your scope — and a number published here would be wrong for most readers. You get them, in writing and against your own data, at scoping.
A first call is a technical conversation, not a pitch: what you have, what you need, and whether this is the right approach at all.
Key capabilities
- Banking
- Government
- Real estate
Built on the Unified Intelligence Layer
Every InsAI product runs on the same four-stage backbone.
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Data Integration
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Forecasting, detection, optimization
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Acting on predictions, end to end
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From the floor to the boardroom
OCR Documents Digitization
Arabic & multilingual OCR; E-KYC / E-KYB.