Insights
Field notes on AI systems,
GTM, and revenue architecture.
Practical breakdowns from systems I build and run. Not theory: architecture, stack choices, and operator lessons from live deployments.
The Claude Code GTM Agent Starter Pack
The Claude Code GTM Agent Starter Pack
How I built a four-layer AI system that runs outbound research, drafts sequences, and enriches CRM records: without a single external enrichment tool.
- A four-layer Claude Code agent stack handles outbound research, sequence drafting, CRM enrichment, and ICP scoring. No Clay. No enrichment credits. No n8n flows.
- 50-account enrichment run: 75 minutes total (45 unattended + 30 review) vs. 3-4 hours with a Clay + copywriting workflow.
- The system runs at 5-10% of equivalent Clay cost, with better output quality on the reasoning and copy layers.
4 articles
How I use Claude Code in client GTM work
How I use Claude Code in client GTM work
Beyond the outbound agent stack: how Claude Code runs content operations, CRM hygiene, and research inside real client engagements: what the delivery model looks like from the outside.
The AI stack I actually run in production
The AI stack I actually run in production
A complete tour of every AI tool in active use across my client work and products: what earned its place, what got cut, and what the operating logic looks like across the stack.
Why CRM-first beats prompt-first in AI adoption
Why CRM-first beats prompt-first in AI adoption
Most B2B AI adoption starts with prompts and ends in chaos. The entry point isn't the tool: it's the data layer. Here's why CRM hygiene is the most important AI project most companies aren't running.
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