Engineering notes from building AI-native software.
What actually works in production, what breaks after the demo, and how we build, written by the engineers doing the work, not a marketing team.
E-Commerce and AI: The Catalogue Is the Product, and It Is Usually the Bottleneck
Storefronts are a solved problem. What still consumes a team is everything behind it: product data that arrives inconsistent, listings duplicated across marketplaces, and an operations layer that grows faster than the catalogue does.
FemTech Product Engineering: Building Where the Data Is the Risk
Cycle, fertility and maternal health products hold some of the most sensitive data any consumer app collects. That single fact should drive the architecture (retention, inference, export and deletion) long before it drives the privacy policy.
Healthcare Software with AI in It: Designing for the Audit You Will Eventually Face
Clinical software is not judged on average performance. It is judged one case at a time, in retrospect, by someone asking why the system did what it did. That reframes every architectural decision, starting with what you log.
Logistics Software and the Exception Problem: Why AI Belongs at the Edges, Not the Middle
Warehouse and transport systems already handle the happy path well. The cost sits in exceptions: the short shipment, the mislabelled pallet, the delivery window that moved. That is where AI earns its place, and it changes what you should build.
AI in Workforce Management: Where Automation Helps, and Where It Quietly Breaks
Scheduling, job orders and shift coverage look like obvious automation targets. They are also the operations where a wrong decision has a person standing in the wrong place at 6am. A mechanism-level look at what to automate, what to keep human, and how to tell the difference.
How to Build an AI-Native Engineering Team (Roles, Workflow, Hiring)
Giving your engineers Copilot licenses doesn't make the team AI-native. Here's what actually has to change in roles, workflow, and hiring, and where teams get it wrong.
Written by the engineers doing the work.
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