Not a claim. A test run.

Revenue Infrastructure Builder · Phase 1: CRM Data Audit

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

• →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.

• →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.

ICP List Builder

7 raw candidate companies scored against an ICP: software, SaaS, or fintech, 50-500 employees, $5M-$50M revenue, US or Canada.

• →Correctly tiered a 650-employee SaaS company as P2 rather than P1, oversized on headcount and undersized on revenue, with partial credit rather than a hard zero for the ambiguity.
• →Zeroed out a non-profit candidate entirely on the disqualifier rule, regardless of how well it scored on every other dimension.
• →Every score ships with a full per-criterion breakdown, so a fit tier is never a black box.

A ranked, explainable list, not just a yes/no filter.

Coach Card Generator

Three buying-committee personas (CRO, Head of RevOps, CFO) for our own RevOps Foundation offering.

• →The CFO card leads with cost predictability and a bounded two-week timeline. The CRO card leads with pipeline and rep-productivity impact. Same offering, translated for what each person actually asks about.
• →Every card includes a real proof point pulled from an actual completed engagement, not a generic value-prop line.

Swap the headers between any two cards and they stop making sense, which is the actual test of whether a card is tailored.

AEO Agent

Audited this exact site (telemeterstrategy.com) against a synthetic unoptimized single-page app with no llms.txt, no schema, and a JS-only shell.

• →Correctly gave this site full marks for AI crawler access, llms.txt quality, and raw-HTML content, the exact infrastructure built earlier in this same engagement.
• →Correctly zeroed out the unoptimized comparison on llms.txt and structured data, and caught its raw HTML holding only 9 characters of real content behind an empty script shell.
• →The one gap the audit honestly flagged on this site: only 2 of 6 valuable schema types on the homepage specifically, since FAQPage and Service schema live on other routes it didn't check.

The scoring differentiates a real site from a fake one instead of returning the same reassuring number regardless of input.

Brand Citation Scanner

Ran a real scan on our own name across review sites, press, podcasts, and communities, no synthetic data this time.

• →A broad search on the company name alone found nothing in press. A targeted follow-up search found a real, confirmed citation we already knew existed.
• →Correctly reported zero results for review sites and podcasts rather than assuming a citation exists somewhere unseen.
• →Did not surface a separate, independently-known citation, a video panel appearance, even with a fairly targeted search. We reported that gap honestly instead of hiding it.

A tool that admits what search cannot find is more useful than one that quietly assumes coverage it does not have.