01
C-suite asks AI a business question.
They want a number they can act on now, not a caveated analysis request.
Governed answers for teams already running on dbt
When someone asks AI for revenue, the answer should match your dashboard.
MetricBridge sits between your AI assistant and your warehouse, translates the question into governed dbt metrics, and returns an answer your BI team does not have to verify by hand.
Use governed metrics
Answers resolve to the same metric definitions your dashboards already use.
Replace raw warehouse prompting
Stop giving AI direct access to tables with no semantic guardrails.
Give teams something auditable
Return answers with a metric-level explanation your team can inspect.
Book a fit call
Book a call, or send your stack first so we can make the conversation specific to your setup.
The Verification Loop
Right now, AI creates one more review queue for your data team.
MetricBridge is built for teams where stakeholders already ask AI for answers, but trust still flows back through BI.
02
The answer arrives, but nobody knows if it is governed.
It may have queried raw tables, invented a metric, or ignored the team’s actual business definitions.
03
BI gets pulled into manual verification.
Instead of building leverage, the team becomes the back-office trust layer for every AI answer.
What MetricBridge Does
A governed query layer between AI assistants and your warehouse.
MetricBridge is not another dashboard. It is the control point that makes AI answers line up with the numbers your team already trusts.
AI client
Question asked in natural language
MetricBridge
Resolves the question through governed dbt metrics
Answer
Returns a number the BI team can stand behind
When the system cannot answer confidently, your team can review missed queries and decide what metric to add next.
How It Works
Three steps from raw AI answers to governed ones.
1.
Connect MetricBridge to your dbt project
Use dbt Cloud Semantic Layer or dbt YAML directly.
2.
Point your AI assistant at MetricBridge
Route questions through governed metrics instead of a raw warehouse connector.
3.
Give stakeholders answers the BI team can stand behind
Use approved metric logic and return answers your team can audit.
Works With
Designed for modern analytics teams, not a greenfield rebuild.
dbt Cloud Semantic Layer dbt YAML BigQuery Snowflake Trino MCP-compatible AI clients
Coming Soon
Planned next surfaces around the same governed core.
The roadmap is about where teams will use MetricBridge next, not about replacing the core dbt-to-AI flow.
Slack bot
Let teams ask governed metric questions in the place they already make day-to-day decisions.
Email digests
Push governed metric summaries and scheduled updates to teams that do not need to open a BI tool or chat client.
Multi-warehouse federation
Answer governed questions across more than one warehouse when a single business view spans multiple systems.
Why This Exists
Built for teams that are done babysitting AI answers.
MetricBridge is for organisations that already have metric discipline and want AI to work inside it, not around it.
Valinor AI Ltd · Open to pilot customers · Limited availability
Pilot Fit
A first call should qualify fit, not just fill a calendar.
We will use the first conversation to understand your stack and whether MetricBridge can remove a real verification burden for your team.
Before the call, we should know:
- Do you already use dbt, and if so is it Semantic Layer or dbt YAML?
- Which warehouse do you run on today?
- Which AI assistant or MCP client are people using?
- Which metrics create the most trust friction right now?
- Who would own a pilot on your side: BI, analytics engineering, or data leadership?