Where data becomes decisions. We build sovereign AI pipelines that read your Arabic content, retrieve from your own documents, and reason at frontier scale — without sending a byte across the border.
Six capability areas, all production-grade, all built to operate inside the Kingdom's regulatory perimeter.
Morphology-aware chunking, root-form normalization, and Gulf-dialect embeddings — so retrieval works on classical, modern, and dialect Arabic without losing context.
LoRA / QLoRA / full fine-tuning of ALLaM, Jais, and Nemotron on your domain corpus. Evaluation suites for hallucination, refusal, and Arabic linguistic correctness.
Continuous training, drift detection, A/B routing, and prompt versioning. Observability stack with token-level cost attribution and per-tenant audit trails.
Risk classification, model cards, bias evaluation, and policy gates aligned to SDAIA Ethics Framework, ISO 42001, and the EU AI Act for export-ready engagements.
From spreadsheets to data lakehouses — ingestion, cleansing, semantic catalogs, and feature stores that make your AI systems actually useful in production.
Independent red-teaming and benchmark suites. Arabic-specific test sets for fairness, factuality, and refusal behavior across regulated domains.
Sovereign-first, with a tight allowlist of frontier models reachable only through a policy router — never by default.
A focused diagnostic across data quality, infrastructure, regulatory posture, and use-case fit. Output: a prioritized backlog with business case for the top three opportunities.
One narrow use case, deployed end-to-end on sovereign infrastructure. Measurable against your KPIs, with a go/no-go gate before scale.
Hardening, integration, governance, and rollout across functions. Includes user training, change management, and observability stack.
Embedded pod or 24×7 managed service. Continuous evaluation, retraining, drift response, and incident handling. SLA-backed.
Every AI engagement is delivered against a baseline of Saudi and international AI/data regulation.
The disciplines compose. AI & Data is the substrate every other practice draws on.
SAMA-aligned credit decisioning, fraud detection, and Arabic customer intelligence on private LLMs.
Pilgrim-experience copilots, dialect-aware support, and crowd-aware operations intelligence.
Clinical decision support, Arabic radiology reports, and SFDA-aware AI device validation.
No, by default. Sovereign LLMs (ALLaM, Jais, Nemotron) run on in-kingdom hardware. Frontier models are reachable only through a policy router — and only after we co-author the policy with you. Most engagements never need to invoke a frontier call.
Translation breaks on Arabic morphology, diacritics, and dialect. Tokenizers built for English split Arabic words mid-root, embedding models miss semantic similarity, and dialect (Najdi, Hejazi, Khaleeji) is treated as noise. Arabic-native pipelines retain meaning end to end — the difference shows up in retrieval recall and hallucination rates.
Up front, we co-define KPIs tied to a business outcome — cycle time saved, decision accuracy, deflection rate, regulatory finding closure. Pilots have a go/no-go gate against those KPIs before scaling.
Every nuqta-grounded answer carries page-level citations. Outputs without retrievable sources are flagged or refused. We run domain-specific factuality benchmarks before deployment, and continuously after.
Yes. Fine-tuning runs on air-gapped hardware. Datasets are classified per NDMO, access is logged, and model artifacts inherit the classification of their training data. Defence-grade engagements run under separate enclaves.
We build. Most of the firm is engineering. Advisory is the front door, not the product.
Thirty-minute working session with our AI & Data lead. We'll map your three highest-leverage AI use cases and a sovereign deployment shape before the call ends.