Ontology & Semantic Layer
An ontology models your business — entities, relationships and measures defined once — so dashboards, agents and people all reason over the same meaning. We build that shared context on Microsoft Fabric IQ or a Databricks/Unity Catalog semantic layer, because an AI agent is only as good as the business meaning it reasons from.
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What we hear from operators
The problems we solve
Agents and reports reason from raw tables
Without a modelled semantic layer, every dashboard author and every AI agent reconstructs what the business means from raw tables — inconsistently. The result is numbers that disagree and agents that guess. An ontology is the shared meaning they all reason from.
Every team defines the same measure differently
Finance, operations and supply chain each have a definition of OEE, OTIF or margin, so the same question returns different answers. An ontology forces one definition of each entity and measure, which is the prerequisite for trust.
The data is not unified, so there is no context to model
A semantic layer models relationships across the business, which needs the data in one governed place to relate. If sources have not met in OneLake or a governed lakehouse, there is nothing coherent to model on top of.
Agentic ambition on an ungoverned foundation
Leadership wants AI agents acting on operations. Built on fragmented, undefined data, those agents act confidently on wrong context — a bigger risk than the manual process they replace. A governed ontology is what makes agentic operations safe.
Who this is for
Who ontology & semantic layer is built for
The roles that feel the problem first — and what we build for each of them.
Chief Data Officer
The problem
Every AI initiative rebuilds its own understanding of the business, so effort never compounds and definitions keep diverging.
What we build
A reusable ontology that analytics, operational agents and Copilot/Genie all reason from.
One modelled context reused across every AI use case
Chief Operating Officer
The problem
Wants agents acting on operations, but the numbers disagree across functions so none can be trusted.
What we build
An ontology defining operations entities and measures once, on a governed foundation.
Agents act on one agreed version of the business
Head of Analytics
The problem
Dashboards contradict each other because each is built on its own definitions.
What we build
A governed semantic layer/ontology feeding every report from one set of definitions.
Reports agree because they share definitions
CIO
The problem
Pressure to deploy agentic AI, and concern about agents acting on ungoverned, fragmented data.
What we build
An ontology on a governed OneLake or Unity Catalog foundation, bounded by governance and human approval.
Agentic operations on a governed foundation
Measurable outcomes
What changes after implementation
Specific shifts from delivered ontology & semantic layer work — the before, and the after.
One definition of each entity and measure across the business
OEE, OTIF and margin mean the same to finance, operations and every agent, so answers agree.
Agents reason from business meaning, not guessed joins
AI agents ground on the ontology rather than inferring structure from raw tables, so their reasoning follows how the business works.
Reuse across analytics, operations and AI
The same modelled context serves dashboards, operational agents and natural-language analytics, so the investment compounds instead of being rebuilt per tool.
A foundation agentic operations can be trusted on
Governed, shared meaning is what makes AI agents safe enough to act on real operations.
By market
Ontology — market-specific pages
Each page below covers what ontology & semantic layer looks like specifically in that market — the local ERP landscape, compliance context, and the operational patterns we actually see there.
Singapore & Malaysia
United Kingdom
North America
Technology stack
Common questions
Ontology — frequently asked
What is an ontology in data terms?
An ontology is a model of your business in terms of entities (order, customer, plant, shipment), the relationships between them, and the measures that describe them (OEE, OTIF, margin) — defined once and consistently. It sits on top of your governed data and gives both people and AI agents a shared, machine-understandable meaning to reason over, rather than each reconstructing it from raw tables.
How is an ontology different from a semantic model?
They overlap: both encode business meaning. A Power BI or BI semantic model is optimised for reporting over a dataset; an ontology is a broader, cross-domain model of the whole business designed to be reasoned over by AI agents and operational processes as well as analytics. In practice the ontology often reuses the definitions your semantic models already hold and extends them across domains.
Why does an ontology matter for AI agents?
Because an AI agent acting across operations needs a coherent, cross-domain understanding of the business, not one report’s dataset. Point an agent at raw tables and it guesses the structure inconsistently; give it an ontology and it reasons over your actual business meaning. The ontology is the shared context that makes agents trustworthy — which is why it is a foundation for agentic operations, not a reporting upgrade.
What is the hardest part of building an ontology?
Agreeing the definitions, not the modelling. Finance, operations and supply chain typically define the same measure differently, and an ontology forces one definition — so someone with business authority has to decide. The technical modelling simply encodes those decisions; skip the agreement and you encode the ambiguity the ontology was meant to remove.
Do we build the ontology on Microsoft Fabric or Databricks?
Both are viable and we build Microsoft-first by default — on Microsoft Fabric IQ, where the ontology and OneLake context are native. Where a client’s estate runs on Databricks, we model the semantic layer on Unity Catalog and the governed lakehouse. The platform follows the estate; the discipline — unify, define once, govern, connect to agents — is the same.
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