// The Toolkit
Production modules we've built and shipped, connecting directly to your CRM instance and running there as a live system, not a one-off configuration.
Automation only works if the data underneath it is accurate. We clean and structure your CRM first, then build on top of it.
Every tool runs against your CRM using your own credentials, through your own AI instance. We don't copy, store, or retain your data. When the engagement ends, access ends with it.
Computes a false-positive rate, overall and per signal type, from a log of previously flagged or prioritized accounts and their eventual outcomes, surfacing which triggers actually predict real movement and which are generating expensive false alarms, only judging a signal once it has enough resolved volume to mean something.
Turns your content-engagement export into a pre-call research brief per account: which topics, pricing, product, comparison, docs, demo, security, implementation, ROI, integrations, are already covered versus still a gap, plus a flag when one contact has done real research volume with nobody else at the account involved yet.
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.
Researches named competitors and builds a self-contained, interactive HTML battlecard: a head-to-head comparison matrix plus per-competitor talk tracks, honest wins and losses, and landmine questions reps can actually use before a call.
Scores and ranks a raw candidate list, a trade show roster, a purchased list, a Sales Navigator export, against your ICP definition, producing P1-P4 fit tiers with a full scoring breakdown per company. Qualifies a list you already have; feeds the accounts worth pursuing into Custom Account Scoring Model for ongoing signal-based re-scoring.
Generates stakeholder-specific one-pagers a champion can forward to each buying committee member, CFO, technical evaluator, executive sponsor, in their own language, addressing that persona's actual concerns instead of one generic pitch deck translated five different ways.
Searches review sites, press, podcasts, and communities for where a company is actually mentioned online, distinct from AEO Agent's technical crawlability check. Reports what real search actually finds rather than assuming a citation exists, including where it doesn't.
Normalizes company names, job titles, phone numbers, and websites across your CRM to a consistent format.
Scores accounts on ICP fit + engagement signals with time decay and signal combination bonuses. Prioritizes your pipeline so reps work the right accounts first.
Reads your deals tagged with a primary trust signal, referral, customer proof, review, expert content, prior familiarity, and compares win rate, cycle time, and deal size across them. Flags small samples instead of calling a lucky streak a pattern, and flags deals with no trust signal recorded at all.
Reads your touch-level activity history and flags contacts stuck in a run of consecutive unanswered touches, the same asset sent more than once to the same contact, and automated sequences still marked active after the deal's stage changed since the sequence started.
Reads your deal stage history alongside a buyer-evidence log and flags forward stage moves with no recorded buyer commitment, no new stakeholder, no procurement or legal review started, no buyer-owned next step, the advanced-on-seller-activity-alone pattern that inflates a forecast before it stalls.
Audits your handoff or routing log, lead routing, deal-to-CS, account reassignment, any type, and flags handoffs unacknowledged past their response window, handoffs with no owner ever assigned, and per-owner acceptance rates once an owner has enough resolved volume to judge fairly.
Audits whether AI crawlers can actually read your site: robots.txt access for GPTBot and Perplexity, llms.txt presence and quality, structured data, and whether your raw HTML holds real content or an empty SPA shell invisible to any crawler that doesn't execute JavaScript. The technical readiness layer, before content or positioning ever enters the picture.
Reads your deal stage history and classifies every backward move as healthy re-qualification (the economic buyer or stakeholder changed) or unexplained regression, then flags deals stalled past a time-in-stage threshold, deals with no decision deadline on record, and false reactivations, a deal that sat quiet past that same threshold and finally moved again with no new evidence attached. A stage snapshot shows where a deal sits. This shows why it got there, and whether it actually came back to life.
Reads your late-stage deal history, verbal agreement through contract, procurement, legal review, and onboarding, and flags deals stalled past a turnaround threshold, deals missing confirmed signing authority, sales promises that never made it into the onboarding brief, and deals sitting without a buyer-facing update.
A handoff-specific readiness gate, not a general completeness score. Checks the exact fields CS needs the moment a deal closes, job title, phone, lifecycle data, and blocks the handoff until they're there, catching gaps at the one moment they actually matter instead of on a periodic audit.
A funnel-level diagnostic, not a record-level fix. Surfaces where contacts get stuck between lifecycle stages and where conversion rates cliff between them, revealing systemic breakdowns in your GTM motion that no single deal or record check would catch on its own.
Pulls recent meeting notes from HubSpot, extracts pain points, objections, and champion signals, then writes structured intel back to the contact, company, and deal records simultaneously.
Scores every customer on reference potential and generates a use-case profile for each. When a prospect asks for a reference, query by industry, size, or pain point and get the right match instantly.
Classifies every contact by buying role and flags single-threaded accounts. A champion creates momentum. A buying committee creates consensus.
Audits your paid campaign and ad portfolio against a stated strategy: flags underperforming ads by impressions and engagement rate, budget diluted across too many simultaneous campaigns, audiences too small to serve efficiently, and messaging-ratio drift from plan. Broader GTM motions across segments, sequences, and strategic projects are the next phase of this audit.
Mines closed-won call transcripts for the value props and narratives that actually recur across deals, then cross-references each one against your content inventory to flag strong, weak, or missing coverage, prioritized by how often a narrative shows up and whether a missing asset would cascade into several derivative pieces. Built from what wins deals, not a guess at what marketing thinks matters.
Generates your five foundation files: ICP, personas, positioning, voice guide, and competitive landscape. Built from existing CRM data and deal notes. Gives your AI stack the context layer that makes output actually differentiated.
Scores accounts on engagement and readiness separately instead of blending them into one number, then flags the accounts where engagement is high but readiness is low, budget unconfirmed, no timeline, objections still open, the pattern most likely to mean a buyer is building a case to eliminate you rather than to buy.
Audits AI-enriched CRM data for trustworthiness rather than just presence: flags fields with no source lineage recorded, enrichment stale past a freshness threshold, and per-source override rates, only judging a source once it has enough volume to mean something.
Turns a deployed tool's task run log into a value receipt: tasks resolved, hours returned, and cost avoided, keeping measured savings strictly separate from estimated ones and flagging implausible time-saved claims instead of trusting them.
Scores sales calls against a per-call-type behavior checklist, prospecting, discovery, demo, pricing, close, citing the exact transcript moment for every behavior observed and surfacing which behaviors are most commonly missing across a group of calls.
Scores candidate market segments from real validation conversations against conversion timeline, willingness to pay, pain acuity, market growth, and accessibility, capping any segment under 10 completed conversations at "needs more validation" regardless of score, and flagging when more than one segment scores as a viable beachhead at once.
Compares recent stage velocity and forward-conversion rate against the window before it from a single stage-history export, and flags when a blended conversion number looks stable only because it is propped up by an earlier, stronger period while the most recent window has already turned over.
Flags closed-won accounts where the buying group is visibly growing since close, new stakeholders, senior titles joining, new departments getting involved, requiring at least two distinct signal types clustering together before calling an account expansion-ready, never a single new contact alone.
Tests named behavioral patterns, champion contact going silent, engagement declining, distinct-contact count contracting, against a client's own past churned accounts, validates only the patterns that actually recurred often enough to trust, then applies just those to flag which currently-active accounts are showing the same shape right now.
Benchmarks each open deal's actual remaining calendar days against how long deals from its current stage have historically taken to close, using a client's own closed-won stage history, and flags deals whose expected close date is not mathematically credible given that benchmark, regardless of what the CRM says.
Computes the overall loss-reason distribution from a closed-lost deal export, flags a reason as rising only once a recent-vs-prior window comparison clears a minimum sample size, and flags segment-level clustering, a segment losing to one reason far more than the book overall, once that segment has enough losses of that reason to mean something.
Audits a closed-won deal export to flag whether a strong period of bookings was distributed across deals, reps, and segments, or concentrated in a handful of them, the difference between a motion worth scaling and a result worth explaining. Optionally decomposes a pipeline coverage ratio into marketing or partner-sourced versus rep-created, and fresh versus stale, so an inflated coverage number can be shown for what it actually is.
Audits an open-deals export for cross-deal resource contention, the same named solutions engineer, executive sponsor, or legal reviewer required by multiple deals whose milestone dates fall within days of each other, the hidden bottleneck-person pattern that stalls an individually-healthy deal. Also flags deals missing the fields needed to capacity-plan at all, and deals whose milestone date already falls after their own last-feasible date.
Audits an ad-spend-by-campaign export against a leads export to compute two cost-per-lead numbers per channel, blended CPL (spend over every lead) and Known Qualified CPL (spend over only the leads that are identified and ICP-qualified), flagging channels that look cheap blended but are actually expensive once unqualified volume is stripped out.
Audits a per-run time log of AI-assisted workflows to compute what each one actually saves once preparation, review, correction, and manual transfer time are counted against the pre-AI baseline, then assigns a keep, fix, narrow, or stop verdict per workflow and names the cost driving it.
// Also building
Want a custom tool deployed to your stack?
Let's Build It