Long-form essays from the engineers shipping AI inside payers, hospitals, energy operators and proptech platforms. Written for technology leaders who care more about what runs in production than what trended last week.
SaaS scalability is not one thing. It is load, data, tenants, team, and cost scaling on different curves. Chasing scale as a single goal is how teams over-build one and get blindsided by another.
Bolting AI onto a normal SDLC produces retail demos that die in production, recommendations, search, and support that fail on real catalogs and shoppers. The AI process rethinks discovery,…
Bolting AI onto a traditional SDLC produces demos that die in production. The AI product development process rethinks discovery, evaluation, and iteration around models that behave probabilistically.
Most hospitality analytics is a pile of events that cannot explain why bookings fall through. A real implementation starts from the booking questions and a tracking plan. How to instrument…
Most retail analytics is a pile of events that cannot explain why carts get abandoned. A real implementation starts from the commerce questions and a tracking plan. How to instrument analytics…
Most SaaS analytics is a pile of events nobody trusts, tracked inconsistently and answering nothing. A real implementation starts from the questions and a tracking plan. How to instrument…
Property listings heavy with photos and maps get slow, and a slow search loses buyers to a faster portal. Treating frontend performance as budgets, monitoring, and culture keeps listings fast.
For healthcare, WCAG accessibility is a patient-access and legal obligation, patients who cannot use a portal cannot get care. Treating it as built-in engineering, not a one-time audit, keeps care…
For a hospitality booking site, WCAG accessibility is legal exposure and lost bookings from guests who cannot reserve. Treating it as built-in engineering, not a one-time audit, keeps booking…
For a retail storefront, WCAG accessibility is both a legal exposure and lost revenue from shoppers who cannot check out. Treating it as built-in engineering, not a one-time audit, keeps the store…
A slow booking site loses reservations, especially on mobile during a trip search. Treating frontend performance as budgets, monitoring, and culture, not one-off fixes, keeps the booking flow fast.
In retail, a slow storefront is lost revenue, every hundred milliseconds costs conversions. Treating frontend performance as budgets, monitoring, and culture, not one-off fixes, keeps the store fast.
As a SaaS product and its teams grow, a design system keeps the UI consistent and fast to build. Treated as product infrastructure, not a component library, it scales across teams and surfaces.
Across booking sites, guest apps, and multiple hotel brands, a design system keeps experiences consistent and fast to build. Treated as product infrastructure, not a component library, it scales.
Across many storefronts, brands, and channels, a design system keeps retail experiences consistent and fast to build. Treated as product infrastructure, not a component library, it scales. How to…
Hospitality teams build guest and operations features from assumptions about what guests and staff want, then see thin adoption. Product discovery gathers evidence first. How to run it in hospitality.
Most SaaS teams skip discovery and build straight from the roadmap, then ship features nobody uses. Product discovery gathers evidence before the roadmap. What it is and how technical teams run it.
In energy, an architecture that cannot handle fleet-scale telemetry, grid failures, or regulatory data is a costly mistake to discover in production. What a serious review pressure-tests before…
Most architecture reviews are a rubber-stamp meeting or a fight. A serious review pressure-tests scale, failure, data, and cost before you build. What it actually covers and how to run one.
In healthcare, AI code that looks right but mishandles a dose, a unit, or PHI is a patient-safety and privacy risk. Know how AI code fails so you review for clinical and compliance harm.
In fintech, AI code that looks right but mishandles money, precision, or a control is a reportable incident, not a bug. Know how AI code fails so you review for money and compliance risk.
AI-generated code fails in patterns, plausible but wrong, insecure defaults, subtle duplication. Knowing its quality profile lets your SaaS team review for what AI actually gets wrong.
When AI generates much of the code, output-based productivity metrics like lines and commits stop meaning anything. What to measure instead: outcomes, flow, and the quality of what ships.
In fintech, shipping fast means little if a change breaks payments or fails an audit. DORA metrics reveal delivery health, but stability and change control carry extra weight. What they predict…
One long-form essay every other Wednesday. Written by the engineers shipping production AI for our clients, not by a content team. No promotional emails. Unsubscribe in one click.
