For Analytics teams

The definitions your AI agents are quietly guessing.

Years of analyses, definitions, decisions, dashboards and code accumulate faster than a team can maintain their meaning. Propperly turns that history into an evidence-backed, governed source of truth — and shows where definitions disagree with each other.

The problem

Same metric, two numbers, no signal.

When an agent — or a new analyst — asks "how do we count active users?", retrieval finds every document that mentions it. It cannot tell which definition is current, which was ruled out, or whether the number is safe to state. So the conflict stays invisible until it reaches a report.

active_user
product: 28dfinance: 30d
conflict
churn
v2 defv1 in 3 dashboards
stale
qualified_lead
agreed · 2 sources
revenue
no evidence for agent
unsafe
How it works

From scattered history to a governed source of truth.

The same four steps that maintain any project's state, applied to your metrics and definitions.

01 / Reconstruct

Read the history

Propperly reads your analyses, definitions, decisions, dashboards and code, and rebuilds the current understanding of each metric.

02 / Review

A person confirms

Your team confirms, corrects, or rejects each definition. Nothing becomes the governed answer just because an AI proposed it.

03 / Use

Agents build on it

AI agents and analysts read the governed definitions directly — with the evidence, the decision behind it, and any conflict made visible.

04 / Maintain

Stay current

As definitions change, Propperly proposes revisions. Approve, edit, or reject — the source of truth stays current without losing its history.

+10.5points on the primary family-level measure
Evidence, not another AI promise

Measured on a sealed corpus.

On a sealed 893-object benchmark, Propperly's maintained state scored 10.5 points above a maintained summary on the primary family-level measure. Built by an analytics leader with a decade of experience, including leading an analytics team, and currently evaluated with analytics teams in B2B data and media.

What a POC looks like

A scoped proof of concept on your own definitions.

  • We start from a bounded slice of your real history — a set of metrics that matter.
  • Propperly reconstructs the current definition of each, with its evidence.
  • Your team reviews and governs what becomes the source of truth.
  • We surface where today's definitions conflict or have gone stale.
  • You see what an agent grounded in governed definitions answers differently.
  • No customer names, dashboards, or data leave your control without agreement.
Analytics team pilot

Request a focused pilot

This is a conversation about a bounded pilot on a slice of your own analytics history — not immediate access to a production enterprise platform. Tell us the problem worth testing and we'll follow up to scope it.

Please don't paste dashboards, data, credentials, or sensitive definitions — a short description of the problem is all we need to start.

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