Stop debating which numbers are correct. Build a single source of truth for every metric in your company.
A centralized semantic layer and metric catalog that eliminates inconsistent definitions, empowers both technical and business teams, and makes AI-powered analytics actually reliable. Built by senior experts who understand why metrics break.
See semantic clarity done right
We build metric definitions, governance, and documentation so everyone trusts the numbers.
Book a free call with our expertEvery team reports different numbers
Revenue, active users, and churn differ by department; alignment is lost.
Metric logic scattered across tools
Definitions live in dashboards, notebooks, pipelines, and spreadsheets with no single owner.
Trust in dashboards keeps eroding
Changes ship silently and no one knows which metric is official.
AI tools fail without semantic clarity
NL2SQL and assistants misinterpret metrics when definitions are ambiguous.
"The hardest part of building reliable analytics and AI tools is not the technology — it is getting everyone to agree on what “revenue” or “active user” actually means. Semantic Layer & Metric Catalog solves this with clear definitions, governed logic, and a shared language that both humans and AI can rely on."
Radek Duha, Head of Data
The problems you are likely facing
Metric chaos destroys trust and slows decisions. These are the patterns we see most often.
Meetings start with “which number is correct?” because revenue, active users, or churn vary by team.
Critical business logic lives in 50 places — BI tools, notebooks, pipelines, spreadsheets — with no source of truth.
Definitions change silently or differ by team, so leadership stops believing the numbers and decisions slow down.
Every new report rebuilds metric logic from scratch; senior talent spends time on plumbing instead of insights.
LLMs cannot generate correct queries when “revenue” means three different things in three different tables.
Our Semantic Layer & Metric Catalog process
Six senior-led steps to deliver reliable, governed, and explainable metrics.
Map current metrics, inconsistencies, and where business logic lives today to target the biggest risks.
Design a unified metric architecture with clear definitions, relationships, and calculation logic.
Build or enhance the semantic layer with dbt, Cube, Looker, or custom solutions to connect data to business concepts.
Create a searchable catalog with definitions, owners, and lineage so teams can self-serve without guesswork.
Implement automated tests, validation rules, and change management so definitions stay accurate over time.
Enable technical teams to maintain the layer and business teams to use the catalog with clear ownership and workflows.
What you will get from Semantic Layer & Metric Catalog
One shared language for metrics, faster delivery, and AI that finally works because the foundations are clear.
One shared definition for every metric
No more “which number is correct?” debates. Every team uses the same definitions to move faster.
Trusted, consistent reporting across the company
Dashboards, reports, and AI tools pull from a single source of truth, restoring confidence in decisions.
Faster analytics and fewer redundant queries
Reusable definitions cut development time by 40–60% and keep analysts focused on insights.
AI tools that actually work
Semantic clarity and structured metadata make NL2SQL, conversational analytics, and agents reliable.
Better collaboration between technical and business teams
Everyone speaks the same data language; business users understand metrics and technical teams know what to build.
Lower maintenance costs and technical debt
Centralized metric logic replaces scattered SQL, so changes happen once instead of 50 times.
Who gets the most value
Where Semantic Layer & Metric Catalog delivers the fastest impact.
Companies where teams report different numbers for the same KPIs
Metric inconsistencies slow decisions and erode trust in data.
Data teams overwhelmed by metric maintenance
Analysts spend more time rebuilding logic than analyzing, and technical debt keeps growing.
Organizations scaling analytics or adopting AI
Teams that need a semantic foundation before rolling out conversational analytics, NL2SQL, or AI agents.
Technical leaders who want governance without bureaucracy
Heads of Data, Analytics, and BI who need structure and maintainability without slowing delivery.
Business teams that struggle to understand metrics
Product, marketing, finance, and ops want clarity on what metrics mean and confidence in the numbers they use.
FAQ
Answers before we start
Do we need a mature data stack before building a semantic layer?+
No. A semantic layer can be built on most modern warehouses (Snowflake, BigQuery, Postgres, etc.). We assess your environment and design what fits.
How long does a Semantic Layer & Metric Catalog project typically take?+
Most implementations take 4–8 weeks depending on scope and metric complexity. The goal is fast clarity and adoption without compromising quality.
What tools do you use for the semantic layer?+
We are tool-agnostic. Common choices include dbt metrics, Cube, Looker, or custom solutions based on your stack and maintainability needs.
What if our metrics are undocumented and scattered everywhere?+
That's normal. Our process is built for messy, inconsistent metrics. We turn the chaos into a clear, governed structure.
How do you handle metric governance without slowing teams down?+
We design lightweight governance — automated tests, clear ownership, and change management — that keeps metrics accurate without bureaucracy.
Can business users actually use the metric catalog, or is it just for technical teams?+
Both. We build catalogs that technical teams can maintain and business users can search, understand, and trust — no SQL required.
Next step
Speak directly with Radek Duha
A short expert call to evaluate your metric environment and whether Semantic Layer & Metric Catalog is the right move.
Clear guidance. Senior expertise. No sales talk.
