Semantic Interoperability
A semantic layer translates raw warehouse tables into business concepts — metrics, dimensions, hierarchies — that every tool and every AI agent can rely on. It's the difference between data that's stored and data that's understood.
The concept
A semantic layer sits between your data warehouse and the tools that consume it. It defines business logic — what “revenue” means, how “active customers” is calculated, which dimensions apply to which metrics — in one central, governed place.
Instead of every analyst, every BI report, and every AI agent re-implementing the same calculations independently (and getting different answers), the semantic layer acts as the single source of truth for what your data means.
In the AI era, this matters more than ever. LLMs don't inherently know what your business terms mean. A semantic layer gives them something to ground against — turning vague prompts into accurate, governed queries.
How it fits in your stack
Semantic Layer
Metrics · Dimensions · Governance · Business Logic
Why it matters
Revenue means the same thing in your BI dashboard, your AI chat interface, and your exported spreadsheet. No more metric drift between teams.
LLMs grounded in a semantic layer ask about business concepts — not raw SQL columns. The result is dramatically fewer hallucinations and wildly more accurate answers.
Certified metrics live in one place. Any tool — BI, AI, notebooks, APIs — queries the same governed definitions. New tools get added without re-defining everything.
Your semantic layer becomes the translation layer between systems. Data warehouse, BI tool, AI agent, and data product all speak the same language.
The tooling landscape
The market has several mature options — each with different tradeoffs on flexibility, governance, and BI integration. We're tool-agnostic and help you choose the right fit for your stack and team.
dbt Semantic Layer
Best for dbt-native stacks
Cube
API-first, highly flexible
LookML / Looker
Google ecosystem
Omni
BI + semantic in one
Metriql
Open-source option
Atscale
Enterprise-grade
Lightdash
dbt-powered BI
Custom builds
When off-the-shelf doesn't fit
Not sure which one fits your situation? Book a free 30-minute call and we'll give you an honest, vendor-neutral recommendation.
From the blog
Semantic interoperability enables different systems, tools, and AI agents to share business logic consistently. The Open Semantic Interchange (OSI) specification is the vendor-neutral standard making it happen across your entire data stack.
Snowflake, Microsoft, dbt Labs, Salesforce, and Databricks all converged on the semantic layer as critical AI infrastructure within the same month. The Open Semantic Interchange spec is now the standard.
A comparison of BI tools with native semantic layers, from Omni and Looker to Domo's new 2026 addition. Evaluate which platform actually governs your metrics for AI readiness.
A semantic layer translates raw data into governed business definitions, giving every dashboard, report, and AI agent one consistent source of truth.
A detailed comparison of Omni vs Looker based on direct implementation experience. Covers features, pricing, migration, and which platform fits your organization.
We help ambitious teams design, implement, and maintain semantic layers — vendor-neutral, from assessment to managed services.
