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 Support offering.

• →The CFO card leads with cost predictability and bounded scope. 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.

GTM Initiative Audit

12 LinkedIn ads across 7 campaigns and 4 topics, checked against a stated strategy (planned topic ratio, budget, and audience guardrails).

• →Correctly flagged 5 ads exceeding 1000 impressions at under 0.4% engagement, and correctly left low-impression ads unflagged since there wasn't enough data yet to judge them.
• →Correctly excluded a paused ad from every calculation instead of letting it skew the active portfolio's numbers.
• →Caught real messaging-ratio drift: one topic running 22.7 points over its planned share, two others running 15.9 points under, both past the threshold worth flagging.

Every flag traces back to a stated rule, not a gut call on which ads look tired.

Pipeline Velocity Monitor

A synthetic deal stage-history export: 5 deals, including one healthy re-qualification, one unexplained regression, two stalled deals, and one closed-won deal used to check the tool correctly excludes closed deals from open-pipeline flags.

• →Correctly classified a deal that moved from Proposal back to Discovery as healthy re-qualification because the economic buyer changed, and a separate deal that moved from Negotiation back to Evaluation as an unexplained regression because the buyer field stayed identical.
• →Flagged both deals sitting past the 30-day stale threshold, one at 41 days, one at 36, while correctly leaving a deal at 21 days in its current stage unflagged.
• →Left the closed-won deal out of every stalled and missing-deadline flag despite it having no decision deadline on record and its last stage entered 60 days ago, since closed deals aren't open-pipeline risk.

Zero false positives across a clean deal, a closed deal, and both regression types in the same test set.

Attribution Builder

14 synthetic deals tagged with a primary trust signal (Referral, Customer Proof, no signal recorded, and a deliberately tiny Expert Content sample), run twice: once at the default 5-deal reliability threshold, once at 3.

• →At the default threshold, correctly called every signal too small to trust yet, including a 75% win rate, rather than presenting a 4-deal sample as a real finding.
• →At a lower threshold, correctly ranked Referral (75% win rate, 4 deals) above Expert Content (100% win rate, but only 1 decided deal), the exact case where a naive "best win rate wins" ranking would get it wrong.
• →Cleanly separated the 4 untagged deals into their own list as a CRM hygiene gap rather than folding them into a misleading "Not recorded" performance score.

The tool's job is knowing when a number is too small to trust, not just computing the number.

Buyer Readiness Score

5 synthetic accounts covering all four combinations of engagement and readiness, including one boundary case scored exactly at the threshold on one axis.

• →Correctly flagged a heavily-engaged account (100/100 engagement) with no budget confirmed, no timeline, and only 1 of 5 objections resolved as engagement without readiness, exactly the pattern a blended score would have called "hot."
• →Correctly classified a low-activity account with budget, timeline, and the competing alternative all confirmed as a fast mover, the shape a referral or prior-relationship deal actually takes.
• →Handled a boundary case scored at exactly the engagement threshold and one point under the readiness threshold correctly, sorting on the right side of the line rather than rounding generously.

Two accounts that looked identically "engaged" on a single dashboard score turned out to need completely different next steps once readiness was scored separately.

Why publish Beta tool results instead of only finished ones?

Because the proof is in what a tool actually produces, not its status label. Real output at every stage says more than a polished demo of a finished one.

Is this test data or a live client's data?

Test data, seeded with known scenarios so the output can be checked against a known-correct answer before a tool ever touches a live CRM.

Can I see a tool run against my own CRM data?

Get in touch and we'll walk through it live against a sample of your data.