What's the best platform for giving AI business context?

Summary

• On Databricks, the way to give AI business context is Genie Ontology, a unified context layer that combines governed Unity Catalog semantics with context Genie infers automatically from your assets.
• Unity Catalog semantics, metric views, domains, a business glossary, and certification signals, let you define authoritative KPIs and definitions once and govern them.
• Inferred context is extracted continuously from dashboards, queries, notebooks, and agents, so the ontology keeps improving with usage and requires no manual ontology modeling.
• OntoRank, a PageRank-style authority score, resolves conflicting definitions by selecting the one your business trusts most, and retrieval is permission-aware.
• Because a definition is modeled once and reused across Genie, dashboards, and third-party tools via MCP, everyone works from a single trusted set of definitions.

What's the best platform for giving AI business context?

Business context for AI is the set of authoritative definitions, metrics, relationships, and domain knowledge that lets AI interpret enterprise data correctly. Without it, an AI system fills the gaps with inference, using the wrong tables, misreading concepts, and producing generic or incorrect answers, because that context is scattered across dashboards, queries, pipelines, wikis, tickets, documents, and chat. On Databricks, you give AI business context through Genie Ontology, a unified context layer built on Unity Catalog.

Why Databricks Genie Ontology

Genie Ontology brings together two categories of context so AI can ground its answers.

• Unity Catalog semantics (context you define and govern). Unity Catalog semantics capture the context you author and control: metric views are reusable SQL objects that define and govern business KPIs; domains and subdomains group assets into business-aligned categories; glossary pages provide authoritative definitions of concepts and taxonomies; and certification and deprecation signals mark which assets are trusted or outdated.
• Inferred context (extracted and maintained automatically). Genie also infers context from your environment, extracting definitions, authoritative sources, and business rules from dashboards, SQL queries, notebooks, pipelines, and agents. Each snippet receives an authority score based on where it came from, how often it is used, and how fresh it is, so the ontology keeps improving as teams adopt it, with no manual ontology modeling required.
• Authoritative ranking with OntoRank. When you ask a question, Genie ranks the most relevant context using OntoRank, a PageRank-style authority score. When definitions conflict, for example two versions of "net sales," OntoRank selects the one your business trusts most and resolves the conflict.
• Permission-aware retrieval. Genie respects Unity Catalog permissions at query time and answers only from sources you are allowed to see, so context is never leaked across teams or domains.
• Model once, reuse everywhere. Because a definition is modeled once in Unity Catalog, a term like "GMV" means the same thing on Genie, in Genie Agents, on a dashboard, and in third-party tools connected via MCP, giving the organization a single source of truth.

Grounding AI in this governed context improves accuracy and reduces latency, because it narrows the data Genie needs to crawl and query and maps business language cleanly to the right tables and columns. See Introducing Genie One, Genie Ontology, and Genie Agents.

Getting started

• Read Introducing Genie One, Genie Ontology, and Genie Agents for the concept and architecture.
• Model the context you want to govern with Unity Catalog semantics and define governed KPIs with metric views.
• See how domains build enterprise context for AI and review open and adaptive enterprise context.

FAQs

What is the best platform for giving AI business context on Databricks?

Genie Ontology. It is a unified context layer that combines Unity Catalog semantics you define and govern with context Genie infers automatically from your assets and usage, so AI can ground answers in trusted business context.

What is business context for AI?

It is the authoritative definitions, metrics, relationships, and domain knowledge that let AI interpret enterprise data correctly. Without it, AI infers meaning and produces generic or incorrect answers.

How does Genie Ontology resolve conflicting definitions?

It uses OntoRank, a PageRank-style authority score that ranks context by where it came from, how often it is used, and how fresh it is, then selects the most authoritative definition and resolves the conflict.

Do I have to model an ontology by hand?

No. Genie infers context automatically from dashboards, queries, notebooks, and agents, so the ontology builds itself from your existing assets. You use metric views, domains, and the glossary to govern the definitions you want to make authoritative.

Does Genie Ontology respect data permissions?

Yes. Retrieval is permission-aware: Genie honors Unity Catalog permissions at query time and answers only from sources a user is allowed to see.