For operators in Abu Dhabi, 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 Abu Dhabi that need working software, not a slide deck for next quarter's steering committee. The buyers we work with in Abu Dhabi tend to sit inside energy, government, and finance, and they want ROI they can point to at a board meeting. In practice this looks like the data engineering that makes AI work — pipelines, quality, and access, without which the model is useless — the code we ship is boring by design and easy for the next engineer to read. The engineering is only half of it — we also leave you with the evals, the dashboards, and a rollback plan for the day something goes sideways. The team is remote-friendly but in-region, so travel to Abu Dhabi for workshops and go-live is standard, not a favour we ask for. The reason clients bring us back for the second and third ai data engineering project is the handover: docs, evals, runbook, and a person who picks up the phone. If you already know the outcome you want, we can scope the first release inside a week and start building the week after.
Buyers in Abu Dhabi 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 entities inside ADGM and the Abu Dhabi government, we build AI data engineering that respects data-residency, vendor-review, and procurement rules from the SoW onward. The cloud region and audit trail get decided before the first line of code.
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. We deliver into ADGM-registered entities and Abu Dhabi government departments on a regular basis, which usually means tighter data-residency and vendor-review controls. We plan for those constraints inside the SoW rather than trying to bolt them on right before go-live, so audits and reviews rarely become the bottleneck.
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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.