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.
AI reliability engineering explained: the concepts behind keeping AI correct in production, the benefits of treating reliability as engineering, and the trade-offs.
A practical roadmap for the buy-vs-build AI decision: decide per capability on differentiation, data, and cost, not as one company-wide choice.
A practical, phased approach to building an enterprise AI roadmap: sequence by value and readiness, lead with foundation-building wins, and keep it living.
The 2026 trends shaping AI observability in energy and utilities: monitoring AI behavior, not just infrastructure, where grid-affecting models must be watched in production.
How to approach agentic AI workflows in real estate: start with bounded, low-stakes tasks and human oversight, not autonomous agents loose on transactions and tenant data.
What is data lineage? A data platform lead's guide: how it traces data from source to consumption, why it underpins trust and debugging, and how to make it real.
A Head of Platforms checklist for well-architected reviews: run them as a recurring practice across the platform with owned findings, not a one-time per-team audit.
Taking cloud-architecture-for-scale from strategy to production with an engineering partner: the gap between a scalable design and a system that actually scales.
The common ETL to ELT migration pitfalls: lifting-and-shifting old transformations, ungoverned warehouse logic, and runaway compute cost, and how to avoid each.
How to approach self-service analytics in real estate: build a governed semantic layer first so brokers and analysts get consistent answers, not ten versions of NOI.
A DevOps lead's checklist for high-availability systems: remove single points of failure and prove recovery, instead of buying redundancy you have never tested.
A Head of AI's introduction to GPU cost optimization: where GPU spend hides, the levers that cut it, and how to lower cost without starving training or inference.
Automated model deployment pipelines or your current manual process? A VP Product's decision guide on when the investment pays off and when it is premature.
Why SLOs and error budgets matter for scaling enterprise teams: they replace endless reliability-versus-velocity arguments with a shared rule that scales across teams.
How to build a business case for a feature store in healthcare: justify it on consistency, reuse, and governance of clinical features, not just ML convenience.
How real estate teams unify customer data across systems without disrupting operations: incremental integration on a unified layer, not a risky big-bang consolidation.
Why observability strategy matters for scaling energy and utilities teams: without it, scaling multiplies telemetry cost and noise while you still cannot answer what broke.
A practical roadmap to agent guardrails: constrain what AI agents can do, validate before they act, and keep humans in the loop, layered by the stakes of each action.
Best practices for legacy system modernization at scale: modernize incrementally with the strangler pattern, preserve domain knowledge, and anchor to business value.
Taking LLM monitoring from strategy to production with an engineering partner: the gap between "we'll monitor the LLM" and a system that catches confident wrong answers.
A decision framework for data mesh in mid-market and enterprise teams: which principles to adopt, when full data mesh fits, and when it is organizational overkill.
The 2026 trends shaping production-grade AI in energy and utilities: from pilots to reliable, monitored, governed systems where AI touches the grid.
Choosing a hallucination mitigation partner: the questions a CTO should ask to find one who reduces and contains hallucination realistically, not one promising to eliminate it.
AI governance frameworks explained for energy and utilities leaders: what they are, why grid-affecting AI raises the stakes, and what to know to govern AI without stalling it.
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.
