Define the operating model, priorities, ownership, and measures the system must support.
GTM Engineer Playbook / Book in progress
Turn your revenue operating model into working AI systems.
A practical book for RevOps leaders, GTM operators, and technical builders responsible for making AI useful across the revenue cycle.
Join the waitlistWhat you will learn
From revenue process to a workflow in production.
The book starts with one business decision, not a tool. You will translate policy and process into a trigger, data, owner, permitted actions, review point, and measure of success.
Then you will apply the same method to inbound, outbound, customer success, and expansion, with examples you can adapt to your company.
The system
One context layer. Four revenue workflows.
GTM context layerProduct knowledge + account context
Docs · CRM · usage · calls · tickets · policy
Outbound Research AgentInbound Qualification AgentCustomer Success AgentExpansion Agent
Human controlPolicy · permissions · review queues · evaluation · feedback
Who it is for
For the people who design and run the revenue system.
Turn process and policy into data flows, AI workflows, controls, and observable outcomes.
Keep frontline judgment, account context, and customer outcomes inside the design.
Inside the book
Eight chapters, from first workflow to production.
- 01
Why go-to-market became an engineering problem
Move from disconnected tools to systems with owners, interfaces, and feedback.
- 02
Map the internal AI stack
Connect product knowledge, account context, agents, tools, permissions, and evaluation.
- 03
Choose the first workflow
Turn a broad AI idea into one business decision with a clear boundary and owner.
- 04
Build product knowledge agents can use
Create a maintained source of truth instead of sending every document to a model.
- 05
Design inbound and outbound AI SDR
Build research, qualification, routing, drafting, and review around pipeline quality.
- 06
Build customer success and expansion AI
Use account context to surface risk, next actions, and credible growth openings.
- 07
Set permissions, approvals, and escalation
Place human control where mistakes carry business or customer consequences.
- 08
Measure quality, adoption, and revenue impact
Connect output quality and workflow health to the outcome the team cares about.
Written from practice
I am writing the book I needed when I started.
I build internal AI systems at an NYSE-listed company across product knowledge, inbound, outbound, customer success, expansion, and revenue automation.
The book turns that work into diagrams, implementation choices, and complete workflow examples you can adapt to your own company. Why I am writing it.
Early reader list
Read the first chapters before launch.
Join the waitlist for early chapters, working diagrams, and the launch announcement.