Databricks Genie in Abu Dhabi
Genie lets business users ask questions of your Databricks data in plain English. We build the governed Gold layer, the business semantics and the Unity Catalog controls that decide whether its answers are trustworthy — because a Genie space is only as good as the data and definitions it reasons over.
Genie turns plain-English questions into SQL over your Databricks data. Whether the answers are right is decided by the governed Gold layer and the business semantics underneath it — not the natural-language interface on top. Abu Dhabi organisations typically have longer project cycles and more complex contractual structures than Dubai. Government-linked companies have detailed reporting requirements that create strong demand for structured analytics — but also a bureaucratic procurement process that requires patient engagement. Mid-market private sector suppliers to ADNOC and the major EPC companies are the fastest-moving segment: they need analytics capability to maintain contracts and compete for new ones.
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Bilingual reporting delivered as standard
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Dubai-based client meetings available
What we hear from operators
The problems we solve
These aren't hypothetical pain points assembled from industry reports. They're observations from actual plant floors, warehouse ops, and finance desks — written down because they come up in almost every first conversation.
Genie answers confidently, and wrongly
Point Genie at raw tables with ambiguous measures and it returns fluent, confident SQL that is subtly wrong — one definition of revenue, a filter the user did not expect. In operations, a confident wrong answer is worse than none. The fix is a governed semantic layer with one definition of each measure, not a cleverer prompt.
Raw tables, not a curated Gold layer
Genie works over the tables it is given. If those are un-modelled Bronze or Silver tables rather than a business-ready Gold layer, users get answers that need a data engineer to interpret. The value comes from curating trusted, business-named datasets first.
No governance means Genie leaks
A Genie space that ignores Unity Catalog row- and column-level security will answer questions using data the asker should not see. Governance is a build requirement — Genie must inherit the same access controls as the rest of the estate.
Adoption stalls without trust and semantics
Business users abandon a natural-language tool after one wrong answer. Without trusted datasets, example questions, instructions and agreed business terminology, Genie never earns the trust that drives adoption across thousands of users.
How we work
Our approach
01
Govern and curate first
We put the data behind Genie on Unity Catalog with row- and column-level security, and curate a business-ready Gold layer with one agreed definition of each measure. Genie reasons over that foundation, so the governance and semantics are done before the interface.
02
Build the Genie space with business semantics
We configure Genie spaces per domain with trusted datasets, business definitions, example questions, instructions and trusted queries — so Genie interprets "revenue" or "OTIF" the way your business does, not the way the raw columns happen to be named.
03
Validate, secure and roll out
We validate Genie against test questions with known-correct answers before anyone trusts it, confirm it honours Unity Catalog permissions, and roll out per domain with monitoring of the questions asked and where it needs refinement.
What changes
Outcomes
These are specific, measurable shifts — not benefit statements. Every outcome listed here has been achieved with a client.
Question to answer: analyst backlog → plain-English answer in seconds
Business users get governed answers from Databricks data without joining the analyst queue.
Answers match the business definition of each measure
A curated Gold semantic layer means "revenue" means the same thing to every user and every Genie space.
Governed access: Genie honours Unity Catalog RLS and CLS
Wider natural-language access does not mean wider data exposure — every answer respects the same controls as your reports.
Adoption that sticks across thousands of users
Trusted datasets, examples and validated accuracy earn the trust that turns a pilot into everyday use.
Technology stack
Common questions
What buyers ask us
These are questions that come up in almost every first or second conversation. If yours isn't here, it will be in the first call.
What is Databricks Genie?
Databricks Genie is an AI/BI capability that lets business users ask questions of governed Databricks data in plain English and get answers as SQL results, charts or tables. It works over datasets you expose in a Genie space, using business context, example questions and trusted queries to interpret intent. Its accuracy depends on a curated Gold layer and clear business semantics, not just the natural-language model.
How do you stop Genie giving wrong answers?
The accuracy lives in the data and the semantics, not the prompt. We curate a governed Gold layer with one definition of each measure, provide Genie with trusted datasets, instructions and example questions, and validate it against test questions with known-correct answers before rollout. A Genie space on an ambiguous model will be confidently wrong, and no configuration fixes that — the semantic problem is solved before the natural-language problem.
How does Genie stay secure and governed?
Genie inherits Unity Catalog governance — row-level and column-level security, and the permissions on the underlying data — so a user only gets answers from data they are entitled to see. We enforce that before publishing a Genie space, so wider natural-language access never becomes wider data exposure.
Do we need Unity Catalog to use Genie well?
In practice, yes. Unity Catalog is what gives Genie governed, discoverable, access-controlled data to reason over, and lineage and audit for what it touches. Running Genie over ungoverned tables produces answers you cannot trust or secure. We treat Unity Catalog governance and a curated Gold layer as prerequisites for a reliable Genie deployment.
How does Genie compare to Power BI Copilot or a Microsoft Fabric Data Agent?
They solve the same problem on different stacks — governed natural-language analytics. Genie is the Databricks-native option; Power BI Copilot and the Fabric Data Agent are the Microsoft-native equivalents. We build Microsoft-first by default and deliver on Databricks with Genie where a client’s estate already runs on Databricks. In every case the deciding factor is the same: a governed semantic layer underneath.
How long does a Genie implementation take?
A focused, governed Genie space over a curated Gold domain is a matter of weeks, not months — the timeline is driven by the state of the Gold layer and the business-definition work, not the Genie configuration itself. Where the Gold layer and Unity Catalog governance already exist, it is faster; where they do not, that foundation is the first phase.
Related services
Further Reading
Practitioner insights on this topic
Microsoft Fabric Implementation RFP Template for Manufacturers
The business runs a proper process. Procurement issues a 20-page RFP, five firms respond, and the evaluation meeting stalls within the hour. Every bidder answered "yes, fully compliant" to every requirement. The prices sit three or four times apart for the same nominal scope. Nobody can explain the spread, so the panel decides on price and a feeling about whoever presented best — because the RFP asked product questions, and every bidder is selling the same Microsoft product.
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AdvisoryRed Flags to Watch for When Hiring a Power BI Consultant
The engagement usually looks like a success for about eleven months. A distributor signs a four-week build, the demo lands well, the invoice is paid. In month twelve the commercial director asks a new question — margin by customer by promotion — and the answer comes back at six to eight weeks and most of the original build cost, because the fact table was loaded at header grain, not line grain. Nothing was mis-sold. The consultant optimised for the demo, not the estate.
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AdvisoryWhat to Expect in a Microsoft Fabric Discovery Sprint: An Honest Scope
The proposal says two to four weeks, workshops, stakeholder interviews, a current-state assessment and a target-state roadmap. It reads well. It also reads exactly like the last three proposals you were sent, and you cannot tell from the document whether anyone is going to touch your data. Discovery is the phase where a buyer has the least ability to judge quality, because the output is paper.
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Other markets
Databricks Genie in other markets
The operational problem rarely changes at the border. The ERP estate, the compliance regime and the reporting cycle do.
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Start with a conversation, not a proposal
First call is 30 minutes with Amit. We ask about your systems, your team, and your most pressing operational problem. You get a clear view of where the gap is and what closing it looks like. No slides. No pitch deck. No obligation to proceed.