How good is Arabic handwriting recognition?

Good enough to be useful with review, not good enough to trust unattended. We set the confidence threshold so handwriting routes to a human by default and measure the straight-through rate on your own sample before promising anything.

Do documents leave the country?

Not unless you decide they can. The extraction stack runs in-region or on-premise for regulated document types.

Education & EdTech · Document & claims AI

Transcripts, credentials and enrolment documents

Turn Arabic and English paperwork into structured, checked, auditable data.

Book the Data Foundation Assessment

Straight-through processing rateCost and time per documentField-level error rateRework and exception volume

The problem

What this fixes

Applications arrive with transcripts, equivalency letters and identity documents in several languages and formats, and each one is checked by hand against admission rules.

Extraction, classification and validation across contracts, invoices, claims, KYC packs, medical reports and government forms, including handwriting and stamped Arabic documents. Every field carries a confidence score, and anything below the threshold is queued for a human rather than written silently into the system of record.

Education & EdTech

Transcript extraction

Grades and credits structured and mapped to your own scale.

Education & EdTech

Credential verification support

Inconsistencies flagged for the registrar instead of discovered after enrolment.

Education & EdTech

Scholarship and financial-aid files

Supporting documents assembled and checked for completeness.

Education & EdTech

Archive access

Historic student records made searchable without exposing them beyond the registry.

In this sector

Where it earns its place

Delivery

How we build it

Foundation check

Before any build

We inspect the systems, the data behind them and the quality of the records this capability will depend on, then write down what has to be fixed before anything is built.

Narrow first release

First release

One workflow, one team, real traffic, measured against the baseline we captured before we started.

Controls and evaluation

Runs in parallel

Guardrails, approval gates, evaluation sets, logging and the evidence pack your auditor and your regulator will ask for.

Scale and hand over

After sign-off

Additional workflows, sites or languages, then runbooks and training so your team can operate it without us.

Data foundation

What has to be right first

A representative sample of real documents, including the ugly ones.
The master records used for validation.
A written policy for what a human must always review.
Retention and residency rules for document images.

Controls

Guardrails and KPIs

A human owns every consequential decision

Agents draft, route and execute reversible steps. Anything that moves money, changes a record of consequence or affects a person's rights stops at a named human approver with the reasoning attached.

Every action is logged and replayable

Inputs, retrieved context, model version, output and the human decision are written to an immutable log, so an auditor can reconstruct any single interaction months later.

Data stays where your regulator requires

In-country, in-region or on-premise deployment, with the model portfolio right-sized to the workload instead of routing everything to a frontier endpoint abroad.