Five challenges. Five services. One methodology.
Each service maps to a challenge Gulf financial institutions face today. Every engagement uses our Ontology-First AI™ approach — domain model first, technology second.
AI Strategy
- Size the AI opportunity against your P&L, ROE, and market cap — not generic benchmarks
- Prioritise use cases by value and feasibility, aligned to Vision 2030 / QNV 2030
- Design the target-state AI operating model — roles, governance, and org structure
- Deliver a board-ready strategy with investment case, KPIs, and accountability map
A signed-off AI strategy with use-case portfolio, investment envelope, operating model blueprint, and board accountability — ready to execute, not shelve.
AI Governance
- Map your regulatory obligations — SAMA, SDAIA AI Ethics, NDMO, QCB, CBUAE, EU AI Act
- Build a governance framework: risk taxonomy, model inventory, escalation paths
- Design model risk management aligned to SR 11-7, SS1/23, and SAMA CSF
- Run red-teaming and adversarial testing before the regulator does
A regulator-ready AI governance framework with risk taxonomy, model oversight processes, and clear ownership — built by people who've been in the room with SAMA and SDAIA.
Data Foundation
- Assess data quality, completeness, and lineage across your AI-critical data estate
- Build classification frameworks aligned to NDMO and SDAIA standards
- Design the data governance operating model — stewards, councils, escalation
- Deliver PDPL readiness assessment and remediation roadmap
An AI-ready data architecture with measurable quality baselines, classification framework, and governance operating model — the foundation every AI model needs before it touches production.
Execution & Delivery
- Deploy Claude/Anthropic, OpenAI, and GCP engineers — specialists who build on these platforms daily, not generalists learning on your budget
- Pair every technical sprint with change management experts who drive adoption from day one — not after go-live
- Run 10–12 week delivery cycles with quality gates, milestone reviews, and board reporting — business value unlocked in a single quarter
- Senior advisory governs the delivery end-to-end: vendor management across HUMAIN, AWS, OCI, GCP, Azure — so you get one accountable team, not five
Production AI systems with measurable P&L impact in 10–12 weeks. Platform engineers who know your stack, change managers who drive adoption, and senior advisors who report to your board — not a pyramid of juniors writing slides.
Enterprise AI Rollout
- Run an AI readiness assessment — maturity scoring by business unit, persona mapping, friction analysis
- Design persona-based rollout waves with governance guardrails per user tier
- Build a champions network — recruit and enable 50–100 internal AI ambassadors
- Deliver change & communications playbook with adoption KPIs tied to business outcomes
Enterprise-wide AI adoption — not 10 people in the innovation lab. A persona-based rollout plan, champions network, governance matrix, and board-ready adoption dashboard showing real usage, not just login counts.
The ontology sits at the centre. Not the LLM.
Most AI programmes start with a model and hope the data catches up. Ours start with the business ontology — the semantic layer your agents reason over. It's what makes AI in a bank reliable, auditable, and production-grade.
- Enterprise data ontology — the semantic objects, relationships, and knowledge graph your AI reasons over
- Agent orchestration built on structured business logic, not prompt engineering
- Data quality, lineage, and governance wired in from day one — not bolted on after the regulator calls
- Production-grade architecture — not a demo that works in the lab and breaks in production
How Enterprise.AI maps to Vision 2030
- Digital transformation of capital markets infrastructure
- Increase non-cash transactions to 70%
- Grow Saudi capital market to top 10 globally
- AI Ethics Principles compliance for all FS institutions
- NDMO data classification and cross-border residency
- Responsible AI adoption aligned to SAMA CSF
- HUMAIN sovereign compute for national AI workloads
- PIF-backed AI ventures and digital infrastructure
- Localise AI value chain — reduce technology dependency