AI Systems
AI in the architecture.
Not bolted on at the end.
Most teams add AI to tasks. I add it to systems. The difference is whether you get a faster copywriter or a different operating model: one that compounds, self-corrects, and runs without a human in every loop.
$ claude run gtm-agent --client acme-saas
▸ Loading CLAUDE.md operator identity...
▸ Connecting MCP tools (Ahrefs, HubSpot, LinkedIn)...
▸ Reading client memory: acme-saas.md
▸ Fetching ICP signal for 247 prospects...
▸ Scoring leads against ICP matrix...
✓ 12 high-fit leads enriched
✓ Sequences drafted and staged in HubSpot
✓ Memory file updated with new signals
Cost: $3.40 · Time: 4m 12s
$ _
How I think about AI
Four principles.
01
Fix the system first
AI doesn't fix a broken GTM. If the pipeline leaks at qualification, AI-personalized sequences still go nowhere. I design the system first, then find where AI creates real leverage.
02
Operator-grade, not demo-grade
Generic AI wrappers don't survive contact with real work. I build tools that are instrumented, specific to the workflow, and used in production, not shown in a slide deck.
03
Compounding, not one-shots
The goal is a workflow that gets faster and smarter over time. Not a prompt that impresses once, breaks on the second run, and gets abandoned after two weeks.
04
Every loop is measured
AI workflows without feedback loops don't improve — they drift. Every system I build has measurement baked in, so you know what's working, what's not, and what to change.
The operating loop
Where AI sits in the system.
Not a task assistant bolted on at the end. AI runs the enrichment, scoring and execution; the operator owns the decision; every loop is measured and feeds the next.
Tech stack
Tools I actually use.
Reasoning
Coding & agents
Automation
Analytics
CRM
Enrichment
What I've built
AI workflows and tools in production.
Ads · LLMs · GTM
AdsAI / Ad Assistant
One full ad lifecycle in AI: brief → generate → score → iterate → prepare variants for deployment. Built for GTM operators, not agency workflows.
Claude Code · Agents · Open source
Claude Code GTM Agent Starter Pack
Open-source foundation for building GTM agents with Claude Code. Designed so operators can ship without starting from scratch.
macOS · Swift · Codex
Notch, native macOS app
Built with Codex and Xcode. Proof that AI-assisted development covers native apps, not just web, extending the operator's reach beyond the browser.
AI adoption · Framework · B2B SaaS
AI Adoption playbook for B2B SaaS
Internal framework for compounding AI adoption across a B2B SaaS company: from growth and marketing through to product and ops.
CRM · Outbound · AI
CRM + outbound AI workflows
Prospecting, sequencing, scoring, and CRM enrichment, redesigned with AI in the loop to cut manual work and improve signal quality.
SEO · Content · AI
SEO + content AI workflows
From keyword research and brief generation to programmatic content scoring. Built to scale content production without scaling headcount.
Live demo
A shipped AI tool, running here.
AdsAI — an AI-native Google Ads cockpit: spend, ROAS, ICP fit and next-best actions in one workspace. Load it inline, or open it in a new tab.
Work with me
If you want AI in your GTM stack, done right.
Not a workshop. Not a deck of use cases. An actual system: built, shipped, and measured.
AI GTM audit: where does AI actually help vs. add noise
Workflow redesign with AI in the critical path
Custom agent and tool development
Team onboarding to AI-native operating patterns
Ongoing iteration and measurement
Next step
Want AI in your GTM stack, done right?
Not a workshop. Not a use-case deck. An actual system: built, instrumented, and running. Book a call.