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Business ontologythat your agents reason on
Give AI agents the business context to interpret data correctly, and they will output answers you can trust.
What is business ontology
A business ontology is a structured, machine-readable record of your organisation’s definition of key concepts, such as customer, order or conversion, and the rules that connect these concepts.
The ontology also defines your metrics, with the precise calculation logic behind each one, so there is full agreement on what goes into revenue, when a customer is considered churned, and which approval rules apply in which context.
Flowline takes all of this knowledge and makes it explicit and consistent across systems. This makes it available to AI agents, which start speaking your organisation’s business language.
Business ontology in the enterprise agentic stack
A reliable agentic enterprise has three layers:
- An AI layer of agents that reason, retrieve, and act.
- A business context layer that gives those agents a structured understanding of your organisation's own concepts and metrics.
- A modern data platform that is fast, governed, and built for AI workloads.
The business ontology sits directly on top of the data platform. It converts the raw data into a structured model of how your organisation defines its own concepts. Then come the agents that can query through that model.
The sequence matters: the ontology can only be as efficient as the platform beneath it, and agents can only be as reliable as the ontology they reason within. Flowline’s agentic enablement helps you build all three layers that make AI output is relevant and trustworthy.
Building the business ontology with Flowline
The business logic your agents need already exists inside your organisation. It is encoded in legacy pipelines, stored procedures, and transformation logic accumulated over years. Now, it is time to surface it, structure it, and serve it consistently, so both your teams and your AI agents start working with the same definitions.
This is how Flowline builds the ontology:
Step 1
Crawl the legacy logic
During the ETL migration, Flowline reads the business logic embedded in your existing pipelines, such as transformation rules, metric calculations, entity relationships, approval hierarchies.
The logic that your systems have been running on for years, much of it undocumented, is surfaced automatically as part of the same process.
Step 2
Structure the first-pass ontology
Flowline uses the logic to create a machine-readable map of the entities in your business, how they relate to each other, and the rules that govern those relationships.
The first-pass ontology captures concepts like customer, order, revenue, and approval with the definitions your systems have actually been using to reflect the reality of your business.
Step 3
Reconcile conflicting definitions
Most large enterprises have multiple definitions of the same concept living in different systems. Flowline surfaces these conflicts explicitly and works with your teams to address them, so you can get canonical definitions that everyone has agreed on.
The result is a single source of truth for every metric and entity your agents will query.
From first-pass ontology to production-ready semantic model
The first-pass ontology Flowline produces is a starting point that captures what your systems have been doing.
To refine this product, Infinite Lambda works with your stakeholders across data, finance, operations, and product, to create a validated, production-ready semantic model.
During the refinement stage:
Canonical metrics are defined, reviewed, and signed off
Entity relationships are confirmed against how the business actually runs today
The refined model is connected to the semantic layer via dbt and Omni
The result is a governed interface that AI agents query. From that point, every time an AI agent asks a question, it uses the definitions your organisation has explicitly agreed on, delivering output that is reproducible and auditable.
Benefits
A production-ready ontology and semantic model changes what your agents are capable of and what your organisation can rely on them for.
- Governed, consistent context that agents can query
- One definition of every metric
- No more conflicting answers from different teams
Ready for modern BI
The semantic model built through the ontology process is also the foundation your BI estate runs on.
Dashboards and reports that draw from the same canonical metrics as your agents produce consistent numbers across every surface: whether a stakeholder is reading a dashboard or asking an agent a question, the answer comes from the same governed source.
Flowline's BI modernisation capability takes this further, migrating your reporting estate onto the semantic layer and rationalising it in the process.
Get in touch
Building a business ontology starts with understanding what logic already exists in your estate. Flowline surfaces that as part of the migration process, giving you a first-pass ontology without starting from scratch. Talk to our team and we will show you what that looks like on an estate like yours.