Tactical writing for founders building AI products. What we learn shipping marketing, healthtech, and fintech systems, written down while it is fresh.
AI-built apps break first at authorisation, database access rules, leaked secrets and unverified payment webhooks, not at the feature they were built to demo. They fail at the thing nobody demonstrate
Most RevOps automation starts as duct tape: a Zapier zap here, a webhook there, a spreadsheet that a human syncs by hand every morning. It works until the pipeline gets valuable enough that a dropped
Personalization products stand or fall on a number most demos never mention: the milliseconds you have to decide before the page renders. A recommendation that arrives after the hero has painted is a
Attribution is the part of a marketing-AI product where the engineering and the honesty have to arrive together. Shipping a dashboard that assigns tidy credit to every channel is easy. Shipping one wh
Most AI marketing initiatives fail at the plumbing, not the model. A team wires an LLM into a content workflow, bolts a recommendation engine onto the storefront, and stands up an attribution dashboar
The gap between "we hooked up an LLM to generate blog posts" and a content engine an editorial team runs in production is enormous, and most of it is engineering. A single generation call is a toy. A
Every article on this blog is written by someone who built the thing; not a content marketer reading docs. Real lessons from production, written down while they are still fresh.
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