Hard-won patterns on GTM systems, AI agents in production, ADHD as an operating advantage, and what I'm learning restoring a 1710 colonial — one system at a time.
You have intent data, enrichment tools, and a CRM. But the space between signal detection and actual revenue action is where most seed-stage startups quietly hemorrhage pipeline. Here's the architecture to close it — before your next board deck asks why CAC is climbing.
Every demo looks great. Then real data hits and the agent hallucinates, over-sends, or goes silent. The problem isn't the model — it's the missing eval layer.
Hyperfocus is a superpower when aimed correctly and a liability when it isn't. The time-blocking and systems framework that keeps multiple ventures moving in parallel.
Most startups outgrow their CRM data model before Series B. Here's how to architect it right the first time.
Full autonomy is the wrong goal. The HITL checkpoints that keep your agents useful without creating new bottlenecks.
The scheduling architecture that lets me run InflectionEngine, InsightsFactory, and a home restoration simultaneously.
Arguing over firmographics in a room is the symptom. The real issue is you don't have a feedback loop connecting closed-won data back to targeting.
Three centuries of ad-hoc renovations on a flawed original foundation. What Abiel Stevens' house is teaching me about technical debt and compounding decisions.
You wouldn't deploy a CRM migration without QA. Why are we deploying revenue-touching agents without structured evals?
CRM architecture, signal pipelines, ICP frameworks, and the revenue infrastructure that compounds.
Production architecture, HITL patterns, eval suites, and why most AI agents fail before they ship.
ADHD as a system, time blocking, portfolio thinking, and building multiple things without losing your mind.