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AI Agents on Your Own Semantic Layer

Agents that answer in plain language — and reconcile with the report.

Most AI agents guess at your numbers because nothing underneath them is governed. We build the warehouse, the dbt models and the semantic layer first, so an agent resolves the same metric definitions your dashboards use.

How we work

  1. 01

    Govern the foundation first

    An agent on ungoverned data is a confident guess. If the semantic layer isn't there yet, that's the first phase — and we'll say so rather than sell you the agent.

  2. 02

    Constrain the agent to it

    The agent resolves metrics through the semantic layer rather than writing free-form SQL against raw tables, so its answers and your dashboards cannot diverge.

  3. 03

    Make the reasoning visible

    Every answer ships with the query that produced it. Anyone who doubts a number can check it in one click instead of escalating.

  4. 04

    Evaluate before rollout

    We build a question set with your team, measure accuracy against known answers, and only then put it in front of anyone.

What changes

  • Answers that reconcile with the dashboard, because both resolve one definition
  • Self-serve questions that no longer queue behind an analyst
  • A verifiable audit trail instead of an unexplained number