Clinical and claims document AI
Turn Arabic and English paperwork into structured, checked, auditable data.
What this fixes
Referrals, discharge summaries, lab reports and insurance pre-authorisations move as PDFs and scans between providers and payers, and coding teams retype them under time pressure.
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.
Pre-authorisation packs
Coding support
Denial analysis
Records migration
Where it earns its place
How we build it
Foundation check
Before any buildWe 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 releaseOne workflow, one team, real traffic, measured against the baseline we captured before we started.
Controls and evaluation
Runs in parallelGuardrails, approval gates, evaluation sets, logging and the evidence pack your auditor and your regulator will ask for.
Scale and hand over
After sign-offAdditional workflows, sites or languages, then runbooks and training so your team can operate it without us.
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.
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.
Accuracy is monitored, not assumed
A held-out evaluation set runs on every model or prompt change, with drift alerts and a documented rollback path when quality moves.
KPIs it moves
- • Straight-through processing rate
- • Cost and time per document
- • Field-level error rate
- • Rework and exception volume
- • Cycle time to decision
Integrations
Proof in this sector
Proof in this sector
Anonymised clientsCloudpital: AI-run electronic health record and patient journey
Clinic and hospital network running CloudpitalNational analytics programme and Tableau deployment
National health authority in the GulfLive operational monitoring for pilgrimage seasons
National health authority, mass-gathering operationsDemand forecasting and triage support for emergency departments
Hospital group in the NordicsClinical documentation and coding assistance
Primary-care clinic group in Australia