10-metric trust layer pilots

Start with the 10 metrics AI must answer the same way.

ChatData turns semantic definitions, blessed dashboards, owners, and approved answer paths into a Slack analyst your team can trust.

Map my 10 metrics Read the trust-layer playbook

Pick 10 decision-critical metrics. Reconcile definitions. Validate answers. Then open Slack access.

#metric-trust-layer

Pilot scope

10 metrics

The smallest trust layer that proves AI analytics can answer consistently.

Official definition, owner, and allowed caveats.

Blessed dashboard or source model AI should trust first.

Approved answer path for recurring Slack questions.

AI analytics adoption starts with trust

Dashboards are not the onboarding plan. Ten trusted metrics are.

The practical move is to pick the smallest set of metrics that drive decisions, reconcile the trusted artifacts, and expose that context to AI workflows.

Semantic layer

Each pilot metric gets one official definition, owner, grain, exclusions, caveats, and status.

Blessed dashboard layer

ChatData learns which dashboard, model, or artifact should be trusted first when multiple numbers exist.

Metric store

Recurring executive questions get approved answer paths instead of fresh warehouse guessing.

The onboarding motion

Turn 10 metrics into a Slack-ready trust layer.

Each metric gets the context AI needs before it answers: meaning, trusted artifact, freshness rules, caveats, owner, and approved answer behavior.

Name the 10

Select the metrics that appear in weekly reviews, executive questions, pipeline reviews, and founder updates.

Scope10 decision-critical metrics

Ownerbusiness + data owner assigned

Riskconflicting definitions or dashboards

Define once

Write the official meaning, grain, filters, exclusions, freshness expectation, and known caveats.

metric activation_rate

definition activated_accounts / eligible_accounts

grain weekly, account cohort

owner growth analytics

Bless the artifact

Choose the dashboard, model, query, or report AI should trust first when the team asks for the number.

Team reviewing trusted dashboard artifacts together

Artifactweekly funnel dashboard wins first

Approve the path

Store the answer recipe for recurring questions, then review Slack outputs before the path becomes reusable.

question why did activation move?

validate dashboard total +/- tolerance

save approved path after owner review

Expose trust to the tools already in the loop

Slack is the first surface. The 10 metrics are the foundation.

The pilot begins with definitions and trusted artifacts, then connects to the systems your team already relies on for metric review.

Slack

Snowflake

BigQuery

Looker

dbt

PostHog

HubSpot

Salesforce

Stripe

Sheets

Trust compounds

Every accepted answer strengthens the metric store.

ChatData maps each of the first 10 metrics to source tables, owner caveats, dashboard references, validation rules, and accepted explanations.

trusted metrics

starter eval questions

$3k

pilot MRR test

Semantic definitionmeaning, grain, exclusions

Blessed artifactdashboard, model, report

Caveat memorytagging, schema, freshness

Approved pathready for the next thread

10-metric pilot

Small enough to finish. Important enough to prove.

Start with 10 metrics that must always return the same answer. Expand only after the Slack answers are trusted.

Startup pilot

$3k / month target

10 decision-critical metrics
Owner and definition reconciliation
30 recurring eval questions
Weekly answer-quality review
Private pilot support channel

Request access

Design partner

Custom / approved scope

More than 10 metrics
Custom data-source plan
Governed memory and review flow
Founder-led implementation

Talk through fit

Get early access

Which 10 metrics must always return the same answer?

Join the early list if your team is rolling out AI analytics and needs a trust layer before opening access to Slack.