cash collected at Planswell
Enterprise AI · Revenue · Go-to-marketCanada-based · Canada/U.S. remote · Business travel
I turn complex AI into revenue systems customers trust and teams can scale.
Strong technology does not commercialize itself. The work is to connect customer discovery, product behavior, sales execution, pricing, adoption, and economics—so the right buyers act, deployments create value, and every outcome improves the next decision.
Commercial value engineSignal → decision → outcome
Operating judgmentOne learning
systemCustomer · product
revenue · delivery
systemCustomer · product
revenue · delivery
01
Customer signalNeed · friction · urgency
02
Commercial decisionFit · value · economics
03
Product + workflowBehavior · handoff · proof
04
Adoption + revenueTrust · use · expansion
Observed outcomes
$4M+collected
~40%team LTV lift
5AI apps live
How the work compounds
- 01Customer evidence
- 02Product decisions
- 03Adoption
- 04Revenue
- 05Next decision
External learning signal · CurrentApplied AI depth, reinforced by MIT and Stanford programs and Google Cloud certification.
MIT Professional EducationApplied AI & Data ScienceCapstone + applied modeling hackathon
MIT Sloan School of ManagementApplied Agentic AI for Organizational TransformationExecutive program · 2026
Stanford HAI / Stanford OnlineGenerative AI: Technology, Business & SocietyProfessional program · 2026
Google Cloud CertifiedGenerative AI LeaderCertified 2026 · Valid through 2029
Fast path for a hiring decision
Choose the evidence closest to your mandate.
Three one-page briefs translate the same operating record for different executive problems. The facts stay consistent; the decision lens changes.
01
Enterprise AI
For leaders commercializing AI across product, revenue, operations, and adoption.
Open selected impact 02Financial Services + AI
For regulated, data-intensive mandates where trust and economics both matter.
Open selected impact 03Zero-to-One GTM
For teams that need the offer, sales motion, operating system, and feedback loop built together.
Open selected impactduring tenure across a 40+ AE organization
team LTV improvement helped drive
live AI applications deployed
Where the value compounds
Win the customer, improve the product, and make the motion repeatable.
The unusual advantage is not sales plus a collection of technical projects. It is the ability to follow one commercial problem across the buyer, product, workflow, team, economics, and implementation—then make those parts reinforce one another.
Find the real buying constraint
Separate a product limitation from a trust, workflow, packaging, urgency, or economics problem—before the team builds or sells the wrong answer.
Turn the field into product intelligence
Convert discovery, objections, call behavior, funnel movement, and customer outcomes into decisions about the product and the motion around it.
Evaluate agents against the outcome
Measure behavior, diagnose failure points, change prompts, workflows, routing, configuration, or handoffs, and validate whether the commercial result improves.
Build a system the team can run
Align owners, incentives, enablement, delivery, retention, and feedback so strong individual performance becomes durable organizational capability.
Lead professional case · PlanswellFollow the system logic
Top-performing sales judgment became AI decision logic—and a learning revenue system the company could scale.
Top-ranked performance during my Planswell tenure expanded the work from selling into redesigning the system: discovery, qualification, AI behavior, seller training, pricing, packaging, handoffs, and the evaluation evidence used to improve them.
5deployed AI applications
1,000+mature outbound attempts/day
$4M+cash collected
01ProspectAttention + relevance
02DiscoverFit + disqualification
03GuidePlan + objection logic
04HandoffRouting + live transfer
05DeliverValue + retention
Learning loop
Calls · transcripts · agent behavior · conversion · order size · LTV
Changed together
AI behaviorSeller trainingPricingPackagingRelease acceptance
Flagship technical proof · Agent infrastructure
Agents made execution faster. C2 Lattice made the work accountable.
C2 Lattice is the full local control plane: ownership, authority, dependencies, evidence, file contention, and recovery made explicit. Control Tower Lite proves how far that operating model can be compressed without hiding the core guarantees.
Early signalBuilt privately · Jan 2026
Public source · green hosted CI on Python 3.12 / 3.13.
Healthy4 agents · 6 open
Full control plane22 tools · 465 local tests
Run management, worker spawning, versioned memory, task DAGs, locks, signed identity, a live operator dashboard, and recovery—supported by public source, green hosted CI, and a clean two-client acceptance run.
Inspect this edition ↗︎Why the range matters
The AI work sits on top of real operating accountability.
Revenue, capital, company-building, and people leadership create a wider diagnostic surface: the ability to improve a commercial system without breaking delivery, economics, risk, or the organization around it.
Revenue systems
Planswell · $4M+ collected · finished top AECustomer truth, selling behavior, pricing, packaging, retention, enablement, and AI workflows treated as one commercial system.
Capital judgment
SME finance · fund management · proprietary tradingUnderwrote business needs across industries, built and managed investment infrastructure, and carried direct P&L accountability.
Company building
Two locations · 20+ staff · $1M+ peak annual revenueBuilt a healthcare-services company from zero, aligning demand, practitioner capacity, operations, service quality, and retention.
Large-team leadership
Future Shop · largest Canadian store · first $2M+ dayTurned around departments, led managers and frontline teams, developed future leaders, and translated performance into repeatable practice.
Independent systems · Different proof
The throughline is inspectable judgment.
Financial data, secure private model infrastructure, and evaluated machine learning are separate systems. Together they show the ability to move from problem framing into architecture, implementation, evaluation, and operating boundaries.
Point-in-Time Decision Intelligence03
A decision should be judged by what was knowable then—not by revised data available now.
HOUST · Dec 2023Thousands, SAAR
+7.4% between the first and latest stored vintages—enough to change a historical narrative.
39.1M observations · 161,525 seriesOpen case
Secure private AI operations04
Relay makes private AI workflows observable, governable—and therefore improvable.
Actual operating screen · 17 Aug 2026Observe · compare · decide · improveOpen case
Evaluated machine learning05
The strongest model was selected from the error pattern—not the prestige of its architecture.
Final capstone deck · measured model verdict6 models · 76.56% held-out accuracyOpen case
Operating method
Make the small decisions compound toward one outcome.
Prompts, handoffs, prices, scorecards, and workflows can each improve locally while the customer system gets worse. The advantage comes from making them learn together.
- 01
Name the consequential outcome
Start with the customer, decision, behavior, and economic consequence—not the technology.
- 02
Find the binding constraint
Separate the visible symptom from the decision or handoff actually suppressing value.
- 03
Connect the microprocesses
Make product behavior, workflow, authority, handoffs, delivery, and retention reinforce the same outcome.
- 04
Ship a bounded proof
Build the narrowest complete system with explicit evidence, economics, risk, and acceptance criteria.
- 05
Turn use into leverage
Feed observed behavior back into product, GTM, operations, training, and the next decision.