About / GTM Engineer Playbook
I build GTM systems with AI, then write down what holds up.
At an NYSE-listed company, I build internal AI systems across product knowledge, AI SDR, customer success, expansion, and revenue automation. I write for the operators who own the revenue process and the builders who turn it into a working system.
01 / Why this exists
The hard part starts after the demo.
A workflow meets stale product knowledge, fragmented account context, unclear ownership, live customers, and real permissions. Those are the problems I write about.
02 / How I work
I start with the job, not the model.
- Map the work
Find who does it, what slows them down, and which evidence they trust.
- Define the system
Name the decision, owner, inputs, permissions, and expected result.
- Test real cases
Run missing data, conflicting records, exceptions, and human overrides.
- Measure what changed
Check adoption, decision quality, downstream work, and the business result.
03 / Where to start
Choose the problem you have now.
GTM Engineer Playbook / Early reader list
Read it before launch.
Join for early chapters, working diagrams, and the launch announcement.