Our approach: impact first
Most of what is said about AI is generic and theoretical. We start from your actual data — your archive, your transactions, your asset register — and build use cases measured by time saved and errors avoided, not by slide decks.
Use cases
Data extraction from documents
Smart Arabic OCR that extracts fields from invoices, contracts, letters and handwritten forms straight into tables and systems.
Semantic search across the archive
Search by meaning, not literal keywords, across millions of Arabic pages, with summarised results and source references.
Internal knowledge assistants
An assistant that answers staff from approved procedures and policies only, inside your environment.
Workflow automation
Automatic classification and routing of incoming transactions, with suggested next actions — the decision stays with the employee.
Capabilities embedded in our other solutions
AI is not an island for us; every pillar carries its own intelligent capability:
| Solution | AI capability | Expected impact |
|---|---|---|
| Records Management | Automatic suggestion of classification code and retention period | Faster, consistent indexing |
| Electronic Archiving | Arabic OCR and metadata extraction | Less manual data entry |
| Asset Inventory | Discrepancy detection and record matching | Faster, more accurate reconciliation |
| Knowledge Management | Knowledge assistant over approved content | Reliable, sourced answers |
| Facility Management | Predictive maintenance from the fault log | Fewer breakdowns, lower cost |
How do we start?
- Use-case discovery workshop (half day): with process owners, to identify 3–5 cases with tangible impact and available data.
- Prototype on real data: one case within a few weeks, with success metrics agreed in advance.
- Operation and scaling: integration into existing systems, team training, and periodic quality monitoring.
Governance and security
- The solution is designed around your data classification: on-premise, local cloud, or public cloud where permitted.
- Compliance with the Personal Data Protection Law and National Cybersecurity Authority controls.
- The final decision always rests with the employee; models suggest and accelerate, they do not approve.
- An audit log of all model outputs and traceability to the source.
FAQ
Does our data leave to external cloud services?
Not necessarily. We design the solution according to your data classification: models running on-premise or on a local cloud for sensitive data, and cloud services only where your policy allows.
Where do we start?
With a half-day use-case discovery workshop in which we identify 3–5 cases with tangible impact, then build a prototype for one case on real data before scaling.