From Sales Rep to AI Operator. In 16 weeks. On real work.
A full-time program that turns experienced, non-technical revenue professionals into operators who can audit, build, evaluate, and deploy AI systems inside a real go-to-market organization — assessed entirely on shipped work.
The role every revenue org will be hiring for — and few know how to fill.
Revenue operations is among the fastest-changing functions in any company: the tools beneath it are repricing and re-architecting quarterly. That volatility is exactly why companies need operators who can evaluate tools rather than be sold them — practitioners who decide where AI belongs, build it, prove it works, and put it into production responsibly.
In sixteen weeks we move a mid-career domain expert from "AI-curious" to "ships a working, evaluated AI system the business can deploy" — and the same sixteen weeks bank twelve graduate credits.
Assessment is the work
No quizzes. Each week the learner ships one real artifact. The artifact is the grade.
Respects existing expertise
Bolts AI machinery onto the judgment learners already have. That's why 16 weeks is realistic.
Honest about numbers
True per-unit cost of every task, hidden costs included. Measured cost-per-outcome, nothing hidden.
Banks a graduate semester
Structured as four 3-credit courses — twelve credits, transferable into a master's.
Supercharge your sales org with the AI stack that's actually shipping.
You won't just learn about AI tools — you'll build working systems on top of the platforms revenue teams already run. The difference between someone who watched a demo and someone who shipped a pipeline.
You become the person who wires it all together.
Most sales teams buy six tools and connect zero. You'll leave with the skill to audit, integrate, and govern a GTM AI stack end to end — enrichment through close, measured at every step.
AI-powered prospecting & pipeline
Build enrichment waterfalls, personalized multi-channel sequences, and guard-railed reply agents — with deliverability done right.
You'll build
You'll prove
Conversation intelligence & forecasting
Turn every call into structured pipeline data. AI-assisted deal scoring, summarization, and forecasting the business can actually trust.
You'll build
You'll prove
Automation, orchestration & governance
The layer that makes everything work together — cross-tool workflows, human checkpoints, agent guardrails, and an AI-use policy that legal will actually sign off on.
Three real jobs. Real salary bands.
The track is built backward from three jobs that exist today and that a graduate can credibly target. US base pay, 2026 market data.
Revenue Operations Analyst (AI-augmented)
Owns pipeline data, forecasting, funnel reporting and CRM hygiene — now with evaluated AI assists doing the heavy lifting on data work.
AI Pipeline / Outbound Operator
Runs the AI prospecting, personalized-outreach and deliverability engine: enrichment, sequencing, reply agents, sender reputation — built and governed correctly.
GTM Systems Operator
Owns the tech-stack architecture, cross-tool automation and agent governance — the operator who runs the machine and keeps it safe.
Radically transparent pricing.
One subscription, no hidden fees, no textbooks to buy. Cancel anytime — you keep the artifacts you've shipped and the credits you've earned.
Your pathway to a master's degree
Sound like the right fit?
Tell us about your background and the GTM problem you'd want to solve in your capstone.
Nine weeks of shared core. Seven weeks of specialization on real data.
Everyone learns the same first nine weeks on a shared teaching case. At Week 10 the cohort diverges into the three role tracks — each capstone runs on a real RevOps problem, not a sandbox.
Identity Reset & How AI Actually Works
Reframe domain expertise as the asset; install the cost/latency/accuracy/reversibility lens.
Prompting as Engineering, Not Art
Six prompt patterns plus a labeled evaluation set: pass rate is evidence, anecdotes aren't.
Tool Selection & the Working Stack
Map the real GTM-AI landscape; build-vs-buy; total cost of ownership; debunk vendor math.
Grounding the Model in Your Data
Retrieval over CRM and documents; never state a number or policy you weren't given.
Structured Extraction & the Clean Pipeline
Turn call notes, emails and web data into clean CRM fields with measured accuracy.
Automation & Orchestration
Multi-step no-code workflows with human checkpoints, error handling and reversibility.
Agents & Where to Trust Them
What an agent actually is; the autonomy ladder; guardrails and red-teaming.
Measurement, ROI & the Honest Dashboard
Define the metric that matters; cost-per-outcome; retire "AI saved X hours" as a metric.
Risk, Compliance & Governance
CAN-SPAM / GDPR / CCPA, deliverability done honestly, and an AI-use policy.
Divergence: Pick Your Machine
Commit to a track and a real problem — your employer's or a partner's; scope the capstone.
Build, Evaluate, Harden
Core engine → intelligence layer → evaluation harness. Fix the dominant failure mode.
Integration, Business Case & Portfolio
Runbooks and change management; honest ROI; final portfolio and live demo.
Four graduate courses. One full semester.
The sixteen full-time weeks are structured as four graduate courses of three credits each — twelve credits, a full semester. Each course is a coherent four-week block with its own learning outcomes.
Foundations of Applied AI for Revenue Operations
Identity reset & mental model · prompting · tool selection · grounding
- Explain LLM behavior in operational terms and judge where it fits a revenue task.
- Engineer prompts as repeatable specifications evaluated against a labeled test set.
- Select a tool stack using TCO and build-vs-buy analysis.
- Ground a model in proprietary data and constrain it to cited sources.
Building & Evaluating GTM AI Systems
Extraction · automation & orchestration · agents · measurement & ROI
- Design extraction pipelines from unstructured GTM inputs with measured accuracy.
- Orchestrate multi-step automations with human checkpoints and error handling.
- Specify and red-team bounded agents using an explicit autonomy framework.
- Define honest outcome metrics and quantify cost-per-outcome and ROI.
Governance & Specialization Foundations
Risk & compliance · divergence & charter · capstone Build I & II
- Apply CAN-SPAM, GDPR and CCPA, deliverability standards, and AI-use governance.
- Scope a production-grade capstone aligned to a target role.
- Build the core engine and the intelligence layer of a role-specific AI system.
Deploying a Production GTM AI System
Evaluation & hardening · integration & handoff · business case · portfolio
- Construct an evaluation harness and harden a system against its dominant failure mode.
- Produce handoff documentation and a change-management plan for non-technical teams.
- Make and defend an evidence-based business case to executive stakeholders.
- Assemble a professional portfolio demonstrating role-ready competence.
Three tracks. One choice at Week 10.
Weeks 11–16 share a spine — Build → Evaluate → Integrate → Business Case → Portfolio — but the build is yours, on real data.
RevOps Analyst Capstone
A forecasting and pipeline-health system — AI-assisted deal-risk scoring and call-intelligence summarization feeding a forecast the business can trust.
Pipeline / Outbound Capstone
An AI pipeline-generation engine — enrichment, personalized multi-channel sequences, a guard-railed reply agent, and a correctly configured deliverability setup.
GTM Systems Capstone
A governed systems build — stack architecture, cross-tool orchestration, and an agent operating under explicit monitoring and controls.
A scaffold to learn on. Real data to prove on.
Weeks 1–9 run on a single realistic teaching case so skills compound. From Week 10, every learner solves a genuine RevOps problem — their own employer's, or one supplied by a partner company. "I built this on real company data" is the line that moves a hiring decision.
The partner-and-employer flywheel
Real problems in
Partner companies and learners' employers supply real RevOps problems and anonymized data.
Learners ship work
Working, evaluated systems built against those problems during the capstone.
Demo day
Portfolios show measured outcomes to the contributing companies.
Hire & sponsor
Companies hire graduates and sponsor future cohorts — seeding the next round.
Mid-career revenue professionals. 3–10 years of GTM experience. No code required.
RevOps & Sales Ops practitioners
You already own pipeline data, forecasts and CRM hygiene. Now you want evaluated AI doing the heavy lifting — and the credibility to lead the rollout.
SDRs, AEs & outbound leaders
You've used the prospecting tools. You want to build and govern the AI engine behind them — enrichment, sequencing, reply agents, deliverability.
GTM enablement & systems owners
You connect the stack. Now learn to architect cross-tool orchestration and run agents under monitoring and controls.
Career movers from adjacent functions
Marketing ops, customer ops, BizOps. Curiosity plus operational judgment is the entry ticket — the program supplies the AI machinery.
Sixteen artifacts. The grade and the hiring portfolio.
One shipped artifact per week. Together they form both the graded assessment and the graduate's hiring portfolio. The week is not complete until the artifact is shipped and reviewed.
Workflow audit of a real task (step / time / AI-handling + autonomy) plus a human-vs-AI break-even calculator.
Library of five evaluated prompts for real tasks, plus a prompt-evaluation tracker.
Stack decision memo with a total-cost-of-ownership comparison model.
Grounded question-answering assistant specification with source-citation evaluation.
Structured-extraction pipeline specification with accuracy evaluation on a labeled set.
Documented automation: runbook, flow diagram and human-checkpoint design.
Agent design document for one bounded task, including a red-team test.
ROI and measurement plan with an honest dashboard mockup.
Governance & compliance pack for the company's AI-in-GTM program.
Scoped capstone charter on a real, sourced problem (employer- or partner-supplied).
Build I milestone — the core engine.
Build II milestone — the intelligence layer.
Evaluation harness with measured before/after results.
Handoff kit — runbooks and a team-training plan.
Executive business case — ROI, rollout plan and risk register.
Final portfolio and live demonstration.
Practitioners who've shipped it. Not just studied it.
Every instructor has built and deployed AI systems inside real revenue organizations. They teach from production experience, not slide decks.
15+ years in revenue operations and GTM strategy. Built and deployed AI systems across three Series B–D SaaS companies, reducing pipeline-to-close cycles by 40%. Former VP RevOps at a leading sales-tech company.
Former Head of Sales Engineering at an AI outbound platform. Designed enrichment and sequencing systems processing 2M+ leads/month. Specializes in deliverability, AI agents, and building things that don't break at scale.
Common questions
Is this a technical course? Do I need to code?
No. The program is built for non-technical revenue professionals — people who understand pipelines, quotas, forecasts and customers but who do not write code. The AI machinery is bolted onto judgment you already have.
What is the weekly time commitment?
Full-time: approximately 30–35 hours per week. Tuesday and Thursday 9am–1pm are live instructor-led sessions; Monday and Wednesday are structured solo work toward the week's artifact; Friday is portfolio review plus a 1:1 mentor meeting.
What does "assessment is the work" actually mean?
No quizzes, no multiple choice. Every week you ship one real artifact reviewed by a mentor against a published rubric. Official submission is upload to the cohort folder by 9:00 AM Friday plus the Friday 1:1. No artifact, no progression. Under-deliverers get one 48-hour resubmission window.
Where do the capstone projects come from?
From real companies. Most learners bring a live problem from their current employer — the strongest outcome, since they return to work with a deployed system. Learners without a suitable problem are matched to a partner company that has contributed a genuine problem and anonymized data. The teaching scaffold remains available as a fallback so no learner is ever blocked.
How does the graduate-credit mapping work?
The sixteen weeks are structured as four graduate courses of three credits each — twelve credits, a full semester. Effort basis is ~512 learner hours across 16 weeks ≈ 128 hours per course, consistent with a standard 3-credit graduate course. The weekly artifacts serve as the performance assessment for credit.
Will I learn how to build AI agents?
Yes — but more importantly, you learn when to trust them. Week 7 covers what an agent actually is, the autonomy ladder, and how to red-team one. Your capstone in Track B or C will involve building a guard-railed, governed agent against a real problem.
Is there a job guarantee?
No, and any program that promises one is misleading you. The program produces direct evidence of capability — sixteen real artifacts, a portfolio built on real company data, and an alumni and employer network designed in from day one. That is what moves hiring decisions.
When does the next cohort start?
Cohort dates are announced via the application list. Apply to be considered for the next intake.
Ready to ship real work?
Applications for the next cohort are open. Tell us about your background and the problem you'd want to solve in your capstone.
Start your application →