Beta

Revenue Infrastructure Builder

Audits your CRM export for duplicate contacts and companies, stale open deals, and missing pipeline stage data, then scores overall data completeness. A second phase turns that clean pipeline into a weighted forecast: best/likely/worst case, commit vs upside, and gap-to-quota. Account scoring and outbound activation close the remaining phases of the sequence.

Any CRM

The problem

Forecasts built on a messy pipeline are really just guesses with a spreadsheet attached.

How it works

01Audits a CRM export for duplicate contacts and companies, stale deals, and missing stage data
02Scores overall data completeness before anything else runs
03Turns the cleaned pipeline into a weighted forecast: best, likely, and worst case, commit vs upside, gap-to-quota

Verified on real test data

Revenue Infrastructure Builder · Phase 1: CRM Data Audit

A synthetic CRM export seeded with realistic messy data: 12 contacts, 11 companies, 10 deals.

93.3/100

Completeness score

Duplicate clusters found

Stale deals flagged

→Caught 3 contact duplicate clusters, including a same-email different-casing pair and a last-name typo (Vasquez/Vazquez), plus 2 company duplicates matched on shared domain.
→Flagged 5 open deals with no activity in 45+ days, worst at 173 days idle, sorted so the coldest deals surface first.
→Caught 1 deal missing a pipeline stage entirely and 1 closed-won deal missing its deal amount, both invisible to standard reporting until flagged.

Zero false positives on the genuinely distinct records in the same test set.

Revenue Infrastructure Builder · Phase 4: Pipeline Forecast

10 deals scored against a $300K quarterly quota.

$262K

Best case

$173.2K

Likely case (weighted)

0.85x

Coverage ratio

→Closed-won revenue ($52K) derived directly from the CRM export, not entered as a self-reported number.
→Correctly downgraded a Negotiation-stage deal out of the commit tier because it had gone 68 days without activity, stage alone would have called it a safe bet.
→Flagged a 0.85x coverage ratio against remaining quota, below the 2x threshold that signals real pipeline risk rather than just weak prioritization.

Every number reconciles: best case, likely case, and worst case all tie back to the same underlying deal list.

See the full test run →

Questions about Revenue Infrastructure Builder

Does it fix the data or just report on it?

The audit phase reports and flags; a client's team or Telemeter Strategy handles the actual fixes.

What's the forecast based on?

Deal stage, activity recency, and historical close patterns from the same CRM, not a flat probability by stage.

Runs against your CRM using your own credentials, through your own AI instance. We don't copy, store, or retain your data.

Deploy this in your CRM