For operators in Riyadh, we run ai data engineering projects that leave you with production systems your team can maintain, not a vendor-only black box.
We build ai data engineering for teams in Riyadh that need working software, not a slide deck for next quarter's steering committee. Riyadh's pull for us is giga-projects and Saudization mandates, and government entities, PIF-backed companies, and Tier-1 banks rarely want another pilot that dies before rollout. Our approach is the data engineering that makes AI work — pipelines, quality, and access, without which the model is useless, wrapped in evaluation and monitoring so quality is a number your team owns, not a vibe check. We handle infrastructure, evaluation, and handover so your team owns the system after we leave, not a black box only we understand. We deliver across Saudi Arabia and the GCC in English and Arabic, with a project lead who owns delivery end-to-end rather than a chain of handoffs. Our differentiator for ai data engineering in Riyadh is honest scoping — if the smallest useful version fits in a month, we say so and we build that first. If you have a rough brief, we can turn it into a build plan without a two-month discovery phase that nobody remembers by launch.
Buyers in Riyadh are done with pilots. What they want now is one production system, measured, running, and reducing a real cost line or lifting a real revenue line. That's the frame we work inside.
Data-quality checks written as code and run on every pipeline run. When quality drops, you find out from monitoring, not from a customer complaint.
Modern AI systems need both structured data (rows in warehouses) and vector data (embeddings for retrieval). We build the pipelines and access patterns for both, and keep them in sync.
Row-level and document-level access is enforced at the pipeline layer, not left to the model to sort out. If a user shouldn't see a record, the retrieval layer never returns it.
Every pipeline can be re-run safely when something breaks or a source changes. Backfills are a scheduled command, not a heroic weekend project.
Warehouse plus vector store plus feature store, built with the AI use cases in mind from day one so the data works for both analytics and models.
Extract, clean, and structure data from legacy systems (mainframes, old ERPs, document stores) so AI systems can actually use it.
Streaming pipelines through Kafka or a managed equivalent so AI systems act on live events rather than yesterday's snapshot.
For Riyadh clients delivering Vision 2030 mandates, we build AI data engineering that clears NCA and SDAIA guidance, sits in a Saudi-region cloud, and integrates with the Tier-1 banking and ministry stack that most programmes already run on.
Because AI is even less tolerant of bad data than analytics. A dashboard with slightly stale data is still useful; an AI answer based on stale data is confidently wrong. We build pipelines with quality checks, lineage, and freshness monitoring so the AI has honest inputs. Most AI failures we see in the wild are actually data failures wearing an AI costume.
Whichever your team is already using, unless there's a strong reason not to. Both Snowflake and Databricks are strong choices for the analytical layer; the AI-specific work (vector store, retrieval pipelines, feature store) sits alongside them. We avoid pushing a platform change on top of an AI project — one big change at a time is enough.
Vector data (embeddings) lives close to the structured metadata it belongs to, so retrieval queries can filter on tenant, permissions, freshness, and other attributes before doing similarity search. For most projects PostgreSQL + pgvector is enough. For serious scale we use Weaviate, Qdrant, or Pinecone alongside the analytical warehouse.
Classify data by sensitivity, apply appropriate handling (masking, tokenisation, encryption at rest and in transit), and enforce access at the pipeline layer — not the model layer. Sensitive fields never reach models that aren't authorised to see them. For regulated data we document the flow and produce audit evidence as part of the pipeline itself.
A well-scoped project is usually six to twelve weeks, depending on how many source systems are in scope and how clean the data is when we get to it. Data-heavy discovery pays back on the delivery side — we'd rather spend two weeks understanding the data than three months surprised by it.
Yes. Most of our best data engineering work is alongside in-house data teams. They usually know the domain and the legacy quirks; we bring AI-specific patterns (vector stores, embedding pipelines, feature stores tuned for retrieval) and the delivery discipline. The split usually works well for both sides.
Yes. For Riyadh clients we default to Saudi-region cloud (AWS or GCP in KSA), work with local Saudi partners where Saudization requires it, and design for NCA and SDAIA guidance from the start of the engagement. The regulatory shape is treated as a delivery input, not something we discover at UAT.
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We'll scope the first release, define the eval set, and give you a build plan you can hand to any engineering team — ours or yours.