Tools & Frameworks
Interactive planning models, funnel analytics, and decision frameworks I've built and used to lead GTM teams. Every tool here reflects how I actually operate: data in, decisions out.
Multi-Agent BDR System
Sample outputs from the multi-agent BDR system I built at Calliope AI. Pre-rendered against publicly available signals on representative Fortune 500 targets. Not a live integration.
Selling company in these samples is a placeholder Series B AI-native security platform; not Calliope, not any real company.
Account Intelligence Brief: JPMorgan Chase
Signal density: High ICP fit score: 9.2 / 10 Recommended motion: Strategic enterprise. Six to twelve month cycle. Multi-threaded entry.
Why this account, right now: JPMC has publicly stated $17B+ annual technology spend and is the largest enterprise AI deployer in financial services. The bank has been aggressive about cloud workload migration over the last 24 months while maintaining strict data residency requirements driven by GLBA, NYDFS Part 500, and global regulator overlap. The combination of high AI deployment velocity plus regulatory complexity is the exact pattern Northstar's runtime data protection layer is built for.
Specific signals (last 90 days):
Estimated current spend profile: Northstar internal model places JPMC's adjacent-category spend at $40M-$80M annually across DSPM, CASB, and CWPP vendors. Displacement opportunity exists where runtime protection sits in the seams between those categories.
Plausible pain we can attach to: The bank operates in 60+ countries and is currently rebuilding its data classification and protection posture for the AI era. The existing DSPM and DLP tools handle data-at-rest and data-in-motion adequately but provide limited visibility once workloads are running. That visibility gap is the Northstar wedge.
Recommended sales motion:
Risk flags:
Work backwards from a quarterly bookings target to calculate exactly how many MQLs each channel needs to produce. Adjust conversion rates, pipeline source mix, and coverage ratios to see how every assumption changes your demand gen requirements. Built from a real capacity planning model used to run GTM operations at scale.
Impact
Deploying this model resulted in 10x growth in top-of-funnel leads by aligning the business on channel investments required to maintain a healthy pipeline. Contributed to a 20% increase in revenue.
Model your revenue capacity by rep across AEs, SDRs, and solutions consultants. Factor in ramp schedules, attrition, seasonality, and planned hires to see exactly when capacity gaps emerge and when you need to hire. Built from a real capacity planning model used to size and scale GTM teams.
Impact
Used to right-size GTM teams across multiple organizations, identify hiring gaps 6+ months before they impacted pipeline, and build board-ready capacity plans that connected headcount decisions directly to revenue forecasts.
An interactive calculator that helps enterprise teams compare the true cost of building an internal AI development environment versus subscribing to a managed platform. Factors in infrastructure, staffing, maintenance, and time-to-value across a multi-year horizon.
Impact
Enabled enterprise prospects to self-validate the business case for platform adoption, reducing the need for custom ROI analysis during the sales cycle and accelerating deal progression.
An interactive model that quantifies the financial impact of hiring fraud on organizations, from direct costs per fraudulent hire ($17K–$240K) to compounding effects on recruiter time, team productivity, and security exposure. Designed to help security and HR leaders build the business case for interview assurance.
Impact
Quantified the financial exposure of hiring fraud for security and HR leaders, creating the business case that moved interview assurance from a nice-to-have to a budget line item.
A production AI revenue system I architected on Claude API, integrated with Clay, Apollo, Perplexity, Gong, Clari, and 6sense. Seven custom agents cover the full top-of-funnel motion: a scout for account discovery and enrichment, a qualifier for ICP scoring, a contact researcher for persona mapping, a writer for personalized outreach, a signal monitor for buying triggers, an industry scanner for market intelligence, and a champion tracker for deal-level stakeholder identification. Not tooling adoption. System architecture.
Impact
Enabled a 3-person commercial team to run an enterprise sales motion typically requiring 12+ headcount. Validated $140K ACV with 40% conversion from enterprise design partner to paid pilot. Demonstrates what AI-native GTM operations look like in practice.
A structured approach to revenue operations that distinguishes between GTM models (strategic choices) and revenue flow models (operational systems). Five pillars: Strategy, Process, Organization, Revenue flow, and Technology, with five execution layers underneath. Used to diagnose why revenue infrastructure doesn't match how revenue actually flows.
Impact
Applied across multiple organizations to diagnose why revenue infrastructure did not match actual revenue flow, leading to GTM restructuring that improved pipeline predictability and sales cycle efficiency.
I'm continuously building the planning models and frameworks I use to run revenue operations. If you're solving a similar problem at your company, I'd like to hear how you're approaching it.
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