AI Adoption at SITE Cloud

SITE Cloud runs a full sovereign AI platform inside Saudi Arabia: LLM inference, embeddings, vector store, reranking, vision, document parsing, and MLOps. Every model runs on infrastructure hosted within the Kingdom's borders. For government, finance, and healthcare buyers, that changes the fundamental question of AI adoption: it's no longer "can we use AI while staying compliant?" but "which use cases deliver value first, and how do we make them work in production?"

My role is the bridge between that platform and the organisations adopting it, helping customers separate real AI opportunities from hype, design the pipelines, and get something running that their teams can own.

• Data residency: For most global AI platforms, customer data crosses borders. For a Saudi bank or government entity, that's often a non-negotiable, not a preference.
• RAG pipelines are more than a model: A useful assistant needs embeddings, a vector store, reranking, and evaluation on top of the LLM. Most teams underestimate that plumbing.
• Compliance is architecture, not an afterthought: NCA, SAMA, and PDPL expectations shape where data sits, who can access it, and how it's governed. Getting that right from the start is cheaper than retrofitting it.
• Cost and evaluation: Model choice, prompting, and evaluation drive both quality and cost. Without structure, AI pilots stall after a demo.

• Use-Case Scoping: Working with customers to identify where AI genuinely helps (conversational assistants, document intelligence, semantic search, visual inspection) and prioritising by value, data readiness, and compliance fit rather than novelty.
• RAG Architecture: Designing retrieval-augmented generation on sovereign components such as embedding models, vector store, reranking, and LLM inference, so answers are grounded in the customer's own data and stay inside the Kingdom.
• Vision & Document Workflows: Scoping multimodal and document parsing use cases: extracting and understanding PDFs, scanned records, and images, for sectors where unstructured data is the bottleneck.
• Proof-of-Value to Production: Structuring the path from a pilot to a production workload: model selection, evaluation, cost visibility, and the MLOps plumbing (monitoring, retraining) that keeps it running.
• Compliance Positioning: Making data residency and NCA/SAMA alignment part of the design itself, so the compliance story holds up for procurement and regulatory stakeholders, not just engineers.
• Enablement: Handing customer teams the patterns, runbooks, and hands-on guidance to extend and operate their AI workloads themselves.

The pattern I use to take AI from idea to production:

1. Discovery: understand the business problem, the data, and the compliance constraints before any model is discussed.
2. Prioritise: pick one or two use cases with clear value and data readiness; avoid spreading a pilot across everything.
3. Proof-of-value: build a small, measurable pilot on sovereign components; evaluate quality and cost against the real workload.
4. Production: design the architecture for scale (RAG pipelines, access control, MLOps, monitoring) and migrate the pilot into it.
5. Scale and enable: hand over patterns and runbooks so the customer's own teams extend the platform without starting over.