services / bi-reporting / semantic-layer-build
Semantic layer build, one definition for every metric.
We design and build semantic layers, in dbt MetricFlow, LookML, SQLMesh or Omni, so every dashboard, tool and AI agent uses the same metric definitions.
✦ Ready for the AI era. A semantic layer is what makes AI analytics trustworthy: one definition of every metric, for people and agents.
trusted by data teams at
camino/financial
✓ Enterprise-ready. Already trusted by multiple Fortune 100 companies.
what we do
Semantic layer build, implemented and audited by experts
Implementation
- Metric and dimension design workshops
- Implementation in MetricFlow, LookML, SQLMesh or Omni
- Governance, ownership and documentation
- Connecting BI tools and AI to the layer
Audits
- Review of existing metric definitions
- Detection of conflicting definitions
- Performance and maintainability review
- Migration plan between semantic layers
rd audit --semantic-layer-build ✔ revenue defined once ✔ metrics versioned in git ! 3 definitions of active user ! dashboards bypass the layer ✗ AI answers disagree with dashboards 1 critical · 2 warnings · 2 passed
// illustrative
in practice
What working with a semantic layer looks like
How it works
- Metrics, dimensions and entities are defined once, in code, and reused by every tool
- Options include dbt's semantic layer (MetricFlow), Cube and LookML, chosen to fit your stack
- Governance covers naming, ownership, certification and change review
- AI assistants also benefit, because they can answer questions from governed definitions instead of guessing
Problems we fix most often
- Revenue, customers or conversion rate calculated differently in each dashboard
- Business logic copied across BI tools
- No owner for metric definitions
- AI tools producing numbers that do not match reports
where it fits / A semantic layer is worth it once more than one tool or team reports the same metrics. It is also what makes AI-assisted exploration reliable.
faq
Frequently asked questions
What is a semantic layer?
A semantic layer defines your metrics and dimensions once, in code, so every dashboard, notebook and AI agent gets the same answers.
Which tools do you build semantic layers in?
dbt MetricFlow, LookML, SQLMesh and Omni. We help you choose based on your stack, team and budget.
Why does AI need a semantic layer?
Without governed definitions, AI guesses how to calculate metrics and answers disagree with dashboards. A semantic layer gives AI the same definitions people use.
Can you migrate between semantic layers?
Yes. We map existing definitions, rebuild them in the target layer and validate the numbers side by side.
How do you decide what metrics to define?
We run short workshops with your stakeholders, start with the metrics that matter most and document owners and definitions.
Do you also build the dbt models underneath?
Yes. Data build and semantic layers covers the full transformation and metric layer.
How much does it cost?
Pricing is tailored to your scope. Get a quote or schedule a consultation and we will give you a clear estimate.
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how we work
From first call to running system
01 /
Discover
We learn your goals, stack and constraints.
02 /
Plan
Audit findings and a clear, prioritised plan.
03 /
Build
Implementation by engineers, reviewed with your team.
04 /
Run
Hand over, or keep us on to support it.
Tell us what you need. We'll scope it.
Pricing is tailored to your sources, volume and the parts of the platform you use. Request a demo or book a call with the team.