Built by humans · Checked by humans
Your AI is guessing.
Because your data never told it the truth.
We hand-build a verified semantic layer for your business, so every AI agent understands your data like your best analyst does. Built by humans. Checked by humans. Nothing automated.
The problem
You bought AI. So why does it keep getting your business wrong?
Confident, wrong answers
It picks the wrong column and tells you the number with total confidence. You only find out in the board meeting.
Every team has a different truth
Finance, sales and ops each define a metric differently. Your AI inherits all three, and blends them.
Tools nobody trusts
The shiny copilot gets quietly abandoned after the first bad number. Trust, once lost, doesn't come back cheap.
Knowledge stuck in one head
The one analyst who knows what the numbers really mean is your bottleneck, and your flight risk.
The hidden cost
The problem isn't your AI. It's that your data can't explain itself.
of enterprise AI & data initiatives fail to deliver lasting value.
conflicting definitions of core metrics can hide inside a single enterprise.
of analyst time lost each week re-deriving the same numbers by hand.
The solution
A semantic layer is the translator between your data and your AI.
A verified dictionary and map of your data: what every metric means, which tables to trust, how to calculate it, so your AI answers like a senior analyst instead of guessing.
The differentiator
Automatic tools guess at meaning. We don't guess.
Auto-tools infer meaning from column names, so they inherit the exact ambiguity you're trying to fix, at scale. We build every layer by hand, capture your team's real logic, and a human verifies each definition.
How it works
From "our AI can't be trusted" to production-ready, in weeks, not quarters.
Discovery & Audit
We map your stack and find exactly where definitions break.
We hand-build your layer
Metrics defined in code, each one verified by us.
Connect to your AI & stack
Warehouse, BI and agents all route through the truth.
Handover & care
Docs, training, optional retainer. You own it.
What you get
Everything you need to trust a number again.
A version-controlled semantic layer, living in your environment.
Every metric documented: definition · formula · source · owner · verified sign-off.
Live connections to your LLMs / agents, BI and warehouse.
A data-truth audit report: the conflicts you didn't know existed.
Team enablement: documentation plus hands-on training.
An optional ongoing verification retainer.
The outcome
The shift you can feel within weeks.
of in-scope metrics documented, owned, and human-verified.
source of truth your AI, BI, and agents all read from.
automated guesses — a human signs off on every definition.
The bar we build to: when your AI gives an exec a number, nobody opens a side spreadsheet to check it. That's the whole game.
Who it's for
If your AI touches internal data, this is for you.
Teams rolling out AI copilots / agents on internal data and hitting trust issues.
Data teams with drifted metric definitions across departments.
Leaders who want AI-ready data without a six-month rebuild.
Mid-market → enterprise on Snowflake, BigQuery or Databricks.
Not for you if you have no warehouse yet, or you're happy letting AI guess.
WHERE events.qualifying AND week = CURRENT
FAQ
The questions a data buyer actually asks.
A tool generates a guess from your column names, inheriting the ambiguity you're trying to fix. We capture your team's real logic and a human verifies every definition before your AI ever sees it.
Discovery is about one week. A working, connected layer lands in weeks, not quarters. Fixed-scope, so you know what you're getting.
No. It's built in code, in your stack, and fully handed over. The retainer is optional, you own everything.
Snowflake, BigQuery, Databricks, dbt, Cube, and any LLM or agent. The semantic layer is the neutral source of truth they all read from.
The layer lives in your environment — we don't move your data out of it. We work under your NDA and access controls, request only least-privilege access for the duration of the work, and hand everything over in your own repositories. More detail on our security page.
Stop letting your AI guess. Give it a source of truth it can't get wrong.
Book a free data audit, we'll map where your definitions break down and show what a verified semantic layer would change. No obligation.