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.
GrowthHub: a B2B SaaS growth dashboard with live funnel metrics, pipeline snapshots and ICP segment tracking — synced nightly from Pipedrive.
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.