AI & Machine Learning Company in Abu Dhabi

Abu Dhabi AI budgets track the sensitivity of the data far more closely than the size of the model. Engagements land in one of three bands: a scoped AI proof of concept at Abu Dhabi rates, a custom production ML model (fraud scoring, churn prediction, document extraction, predictive maintenance) including MLOps, monitoring, and a PDPL-aligned model card, or a full production AI system with RAG on Jais or Falcon, multiple models, fine-tuning, and enterprise integrations into ADNOC, Etihad, or ADGM stacks. Scope drivers include data audit, a baseline model, and a hosted demo. Abu Dhabi rates sit above Dubai for sovereign and ADNOC-adjacent work because of clearance, escort, and sovereign cloud overhead. We give fixed-fee proposals in AED rather than open T and M estimates.

The UAE Personal Data Protection Law (Federal Decree-Law 45/2021) is the federal data protection regime administered by the UAE Data Office. It applies outside the financial free zones and imposes obligations on lawful basis, cross-border transfers, automated decision-making notices, data subject rights, and breach reporting. If your business operates inside ADGM, the ADGM Data Protection Regulations 2021 apply instead and align independently to GDPR with their own Commissioner of Data Protection. Our discovery phase runs a PDPL or ADGM classification on your use case, and high-impact builds ship with a model card, bias audit, and monitoring plan. AI-specific obligations are also shaped by the UAE National AI Strategy 2031, the Charter on Responsible AI, and the Cloud Computing Regulatory Framework for data classification.

Yes. Falcon and Falcon Mamba are open-weight models from TII with strong Arabic performance, and Jais from G42 and Inception is the leading Arabic-first instruction-tuned family. We fine-tune both on client Arabic corpora using LoRA, QLoRA, and full-parameter approaches depending on budget and licence. Khaleeji dialect, Modern Standard Arabic, and code-switched Arabic and English content all need separate tokeniser and evaluation work, and we build dialect-balanced eval sets per project. For ADNOC, sovereign, or Hub71 fintech clients we host fine-tunes on G42 Cloud or AWS me-central-1 GPU instances inside UAE residency. Deliverables include a model card, an Arabic-specific bias review, and prompt injection defences tested on Arabic adversarial inputs.

When a project requires genuine research (novel Arabic NLP architectures, multimodal Arabic and English, rare-event detection in oil and gas data, Sharia-compliant reasoning), we scope collaborations with MBZUAI faculty in Masdar City or TII researchers at ATRC rather than overselling in-house capability. MBZUAI is the first AI-only graduate university globally and has industry sponsorship programmes our clients have used. Our core team handles applied engineering, MLOps, and productionisation, which is where most Abu Dhabi AI projects actually stall. For standard work (RAG on Jais or Falcon, fine-tuning, classical ML, computer vision on known architectures) no academic partner is needed. We will tell you up front which bucket your problem fits into.

Arabic NLP is not one language. Modern Standard Arabic (MSA) is the written register used in ADNOC reports, ADGM filings, and government correspondence. Khaleeji is the spoken Gulf dialect used in customer support transcripts, call centre logs, and informal messaging. Egyptian, Levantine, and Maghrebi appear in pan-Arab content. We build tokenisation and embedding pipelines that respect this, evaluate on dialect-balanced sets (we maintain internal Khaleeji eval data), and use CAMeL Tools, Farasa, AraBERT, and the Jais and Falcon tokenisers depending on the task. For voice and ASR we benchmark Whisper, ElevenLabs, and Arabic-native models on Khaleeji audio rather than trusting English benchmarks. Code-switching with English is handled explicitly in eval.

We default to G42 Cloud (Abu Dhabi-native sovereign), AWS me-central-1 (UAE region), and Azure UAE North (Dubai but UAE residency) for storage, training, and inference. For LLMs that must stay in the UAE under CCRF classification (sovereign data, ADNOC operational data, ADGM client data) we use Jais and Inception endpoints inside G42 infrastructure or self-host Falcon and Falcon Mamba on UAE GPU instances. Cross-border is acceptable only when a PDPL transfer assessment or ADGM cross-border instrument signs off, typically for non-sensitive internal tooling. We document residency in the model card and the data processing agreement so PDPL, ADGM, and FSRA auditors have a clear answer.

A typical ADGM fintech model (fraud scoring, credit decisioning, KYC document extraction in Arabic and English, AML transaction monitoring) takes fourteen to twenty-two weeks from kickoff to production, assuming clean historical data and FSRA documentation as part of scope. Week 1 to 4 is data audit and baseline. Week 5 to 12 is modelling, iteration, and challenger testing. Week 13 to 18 is MLOps, monitoring, shadow mode deployment, and FSRA review. Week 19 onward is gradual rollout under the ADGM RegLab sandbox if applicable. If you are pre-data or need Arabic labelling, add six to eight weeks. We have shipped to this cadence inside ADGM-regulated stacks for Hub71 cohort companies.

Yes. The UAE Cloud Computing Regulatory Framework (CCRF) classifies data into tiers and dictates which workloads can sit on which infrastructure. Sovereign and confidential tiers typically require G42 Cloud or other UAE-resident sovereign providers and may prohibit foreign-controlled cloud regions for production data. We map your data to CCRF tiers in discovery, build pipelines that respect those tiers end to end, and produce attestation artefacts showing where each model component runs. For ADNOC OT and ICS adjacent work we add IEC 62443 segmentation and never let model inference cross the IT or OT boundary without an explicit security review. CSC and NIA controls are layered on top where required.