A semantic layer is where your business logic lives once, so every dashboard, query, and AI agent computes revenue, churn, and active patient the same way. We define and certify that layer on top of your warehouse, then wire it into the tools and agents that depend on it. When the number is defined in one place, the report stops disagreeing with the board deck and the AI stops guessing.
We build and certify the semantic layer that defines your metrics once so every tool and AI agent computes them the same way. Warehouse-native, governed, AI-ready.
A semantic layer is the place where business metrics are defined once and served to every downstream tool. Instead of each dashboard and AI agent re-deriving revenue or churn with its own logic, they all read the same certified definition, so the numbers agree and an AI system can be trusted to reason on them.
A semantic layer is where each business metric is defined once and served to every downstream tool, so dashboards and AI agents stop re-deriving revenue or churn with their own logic. Thinklytics builds and certifies that layer on top of your warehouse, wires it into Tableau, Power BI, and your agents, and gives every definition a named owner.
A single certified definition for each metric that matters, built on top of the warehouse you already run.
Wired into the tools that consume it: Tableau, Power BI, the warehouse, and any AI agent that needs a trustworthy number.
Versioned and owned. Every definition has a named owner and a change history, not tribal knowledge.
Tested. A definition change runs against fixtures before it reaches a single report.
A new BI tool. The semantic layer sits under the tools you already have.
A data warehouse migration. We work on the platform you run today.
A one-time glossary document. A spreadsheet of definitions nobody enforces is the problem, not the fix.
A rip-and-replace. We certify the metrics that drive decisions first, not all of them at once.
A certified-metric inventory: the 15 to 40 metrics that drive decisions, each with one definition, one owner, and one source.
The semantic layer built in your stack (dbt, the warehouse, or your BI tool's modeling layer) and connected to the consuming tools.
A governance model: how a definition changes, who approves it, and how the change propagates without breaking reports.
An enablement transfer so your team owns and extends the layer after we leave.
Revenue discrepancy resolved. Six conflicting metrics reconciled to one certified ARR definition. The data foundation that makes sales AI work.
Tableau Server response time after rationalization. $6.2M migration avoided. The clean foundation reporting automation runs on.
Report run time after migrating 140 Crystal Reports to Power BI in 20 weeks. $1.1M a year in licensing saved.
Each report re-derives it with its own SQL. There is no single certified definition to read from.
Finance, sales, and ops each computed the metric their own way because nothing forced one definition.
The AI assistant gives a confident wrong answer about churn.
It was pointed at raw tables with no semantic layer, so it guessed at what churn means.
The definitions live in people's heads, not in a governed layer they can read.
A semantic layer is where business metrics are defined once and served to every downstream tool. Instead of each dashboard or AI agent re-deriving revenue or churn, they all read the same certified definition, so the numbers agree and AI can be trusted to reason on them.
An LLM pointed at raw tables guesses at what your business terms mean, which is how you get confident wrong answers. A semantic layer gives the model the certified definition of churn, active patient, or ARR, so it reasons on your logic instead of inventing its own.
No. The semantic layer sits on top of the warehouse you already run and under the BI tools you already use. We build it in dbt, the warehouse, or your tool's modeling layer, whichever fits your stack.
A data dictionary is a document nobody enforces. A semantic layer is executable: the definition lives in code, every tool reads from it, and a change runs through tests and an approval before it reaches a report.
We certify the 15 to 40 metrics that drive decisions first, which is usually an 8 to 12 week engagement. Less critical metrics follow once the foundation and the governance model are in place.
You do. Every metric gets a named internal owner, and we run an enablement transfer so your team can extend and change the layer without us.
We certify the metrics that drive decisions first. These are the factors that move the effort.
The 15 to 40 metrics that drive decisions come first; the long tail follows.
Whether the layer lives in dbt, the warehouse, or your BI tool affects the build.
How many conflicting versions of each metric exist sets the reconciliation work.
Each tool and AI agent wired to read the layer adds integration.
An executable semantic layer, not a glossary nobody enforces
A data dictionary is a document people ignore. A semantic layer is code every tool reads from. Here is the difference.
A change runs through tests and approval before it reaches a report.
Agents read the certified definition and reason on your logic.
You want AI and dashboards to read one certified definition.
The underlying warehouse and pipelines are broken: start with Data Foundation.
You need ownership, access, and policy enforcement: see Data Governance.
You want the AI question layer on top: see Agentic BI Implementation.
The AI layer that reads your certified metrics and acts on them.
Govern the definitions, owners, and access the semantic layer depends on.
The warehouse and pipeline work underneath the semantic layer.