SeenPixelService
Semantic layer and BI modernization
One definition of every metric, in Cube, dbt metrics or LookML, read by dashboards and by agents alike. Looker-to-X migrations with parity tests, and LookML models brought back under control.
Why the semantic layer is the foundation for both BI and AI
A dashboard with the wrong number is embarrassing. An agent with the wrong number is a liability, because it answers with confidence and nobody checks. Both problems have the same cause: the metric is defined in five places and none of them agree.
A semantic layer puts the definition in one place, under version control, with tests. BI tools read it. An MCP server exposes it to agents. Finance signs off on it once. That is why we treat it as the first step of any AI-over-data work, and why we do not build an agent over raw tables.
Gartner reached the same conclusion: by 2028, 60% of agentic-analytics projects relying solely on MCP will fail without a consistent semantic layer (Gartner, May 2026). And 71% of data practitioners already worry about hallucinated numbers reaching stakeholders (dbt Labs, State of Analytics Engineering 2026).
What we deliver
Semantic layer build
Metrics, dimensions and joins defined once in Cube, dbt metrics or LookML, with tests, ownership and a change process. Dashboards, notebooks and agents all read from it.
Looker-to-X migration
Moving from Looker to Cube, Omni, Lightdash or Sigma without losing the LookML logic: inventory, automated translation where it is safe, manual rewrite where it is not, parity tests before cut-over.
LookML rescue
Explores nobody dares touch, duplicated measures, slow dashboards. We refactor the model, add tests and a CI pipeline, and document what each metric means.
BI modernization
Consolidating tools, retiring extracts and copies, moving reporting onto the warehouse and the semantic layer so there is one number per question.
Every deliverable lands in your repositories with tests, CI and documentation. Pairs with the ask-your-data agent when you are ready.
Questions we get
Why a semantic layer before an AI agent?
Because the agent has to query something that knows what 'revenue' means. Gartner expects 60% of agentic-analytics projects that rely only on MCP to fail by 2028 without a consistent semantic layer (Gartner, May 2026). The layer is the governance.
Which tools do you work in?
Cube, dbt metrics (the dbt Semantic Layer), LookML and Looker, and the warehouse-native options in Snowflake, BigQuery and Databricks. We recommend by fit, not by partnership.
How do you migrate without breaking reports?
An inventory of every explore, dashboard and metric; automated translation for the mechanical parts; parity tests that compare the old and new numbers on the same dates; a cut-over only when they match.
Can this be a pilot?
Yes. One semantic-layer migration or one domain's semantic model is a 6–8-week pilot with a stop-point at week two. See how we work.
Show us a metric that has two definitions
Every company has one. Tell us which, and we will tell you what fixing it properly involves.