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
Remote patient monitoring looks simple on a slide: a patient takes a reading at home, a clinician sees it, and someone intervenes before a problem becomes a hospitalization. The value is obvious and t
Ask a general-purpose LLM a clinical question and it will answer with confidence: sometimes correct, sometimes a plausible fabrication, and rarely with a way to tell the difference. In most domains a
Most software fails gracefully. A dropped request retries, a broken page shows a fallback, a bad recommendation wastes a few minutes. AI patient triage does not get that luxury. When a symptom checker
AI healthtech products rarely fail because the model was wrong. They fail because the architecture around the model treated Protected Health Information (PHI) as an afterthought: logged where it shoul
The pitch for an AI clinical documentation product is easy to make and hard to build. A clinician talks to a patient, the software listens, and a finished, coded, EHR-ready note appears. The demo land
The best AI agency for a healthtech product is the one that talks about controls before features: PHI data flows, BAAs, audit trails, and what happens when the model is wrong about a patient. This gui
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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