Two specialized models. Seven benchmark validations. Complete data control. Deploy on-premises without vendor lock-in or unpredictable costs.
Flagship — complex reasoning, drafting, policy.
Efficient — high-volume, edge, fast inference.
Four things generic LLMs don't do well: route traffic by complexity, understand Arabic the way a native does, stay inside your perimeter, and price predictably. We rebuilt each from the ground up.
Optimize costs by intelligently routing traffic based on complexity. Light classification hits LLM-S; long-form reasoning hits LLM-X. Both run on the same infrastructure.
Not just trained on Arabic. Deep understanding of MSA and every major dialect, morphological awareness, and seamless code-switching between Arabic and English.
Your data never leaves your infrastructure. No foreign APIs in the loop. Air-gap capable, GCC-compliant, and defensible under attorney-client privilege.
Flat licensing against a predictable capacity ceiling. No per-token meter. 100 billion tokens per month costs the same as 10 billion — no month-end surprises.
Average across 7 Arabic benchmarks. 29 models evaluated. Stanford CRFM's HELM Arabic leaderboard is the most rigorous independent evaluation of Arabic language models in existence.
Three deployments. Three different problems. One common thread: a sovereign Arabic LLM unblocked what generic cloud AI couldn't deliver.
Cloud APIs struggled with Gulf Arabic (68% accuracy). Data sovereignty blocked cloud deployment.
LLM-S for intent classification, LLM-X for policy questions. On-prem deployment.
Real-time fraud detection required <100ms. External APIs were too slow (200ms+).
LLM-S on edge nodes. Fine-tuned on 5 years of proprietary fraud data.
Global LLMs missed dialectal legal terms (72% acc). Privilege blocked foreign APIs.
LLM-X for deep analysis, fine-tuned on historical contracts. Air-gapped deployment.
Three production-ready speech products on the same sovereign infrastructure. No cloud dependency. Voice data never leaves your perimeter.
Schedule a technical consultation to discuss deployment architecture, ROI analysis, and industry-specific use cases.