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.
A CDO's checklist for implementing medallion architecture: bronze, silver, and gold layers done with governance and ownership, not just three folders named after metals.
Cloud migration strategy explained: the core migration approaches, the real benefits beyond cost, and the trade-offs to weigh before you move a single workload.
How to approach data cataloging in enterprise organizations: build it around the questions people actually ask, with ownership, or it becomes another unused inventory.
The state of AI model risk management in enterprise for 2026: from afterthought to operating requirement, what is maturing, and where most programs still fall short.
Why modern data architecture matters for scaling energy and utilities teams: legacy data foundations break under grid, sensor, and operational data growth.
An SRE lead's checklist for rightsizing cloud spend: cut waste using real utilization data without sacrificing the headroom reliability depends on.
AI inference cost optimization explained: the concepts that drive inference spend, the benefits of optimizing it, and the trade-offs against latency and quality.
How to take self-service analytics from strategy to production with an engineering partner: the governed-data foundation that decides whether self-service works or backfires.
How to measure and prove data observability ROI: quantify the cost of bad data, the detection it speeds up, and the decisions it protects, against the tooling cost.
How to measure and prove cloud security posture ROI: quantify the risk reduced, translate it to avoided cost, and make the case without waiting for a breach to prove it.
SRE explained for real estate technology leaders: what it is, why it matters for the platforms your business runs on, and what to know without being an engineer.
A practical, phased roadmap to data observability: start where bad data hurts most, instrument the pipelines that matter, and build the practice that catches issues before users do.
A VP Product's introduction to building an enterprise AI roadmap: sequence by value and readiness, not hype, and treat data and trust as the real dependencies.
The common observability strategy pitfalls that produce huge bills and no answers: collecting everything, dashboards nobody uses, alert fatigue, and how to avoid each.
Best practices for cloud cost optimization at scale: make cost visible and owned, fix the structural drivers, and build the practice that keeps spend from creeping back.
How to build a business case for managed AI services in real estate: the real cost comparison, the speed-to-value, and the data and lock-in risks to weigh honestly.
The 2026 trends shaping ELT modernization in healthcare: why providers are moving transformation into the warehouse, and what PHI and compliance change about it.
SRE or your current DevOps status quo? A decision guide for DevOps leads on when SRE's overhead pays off and when your existing approach is already enough.
A CTO's implementation checklist for site reliability engineering: the decisions and foundations to get right so SRE becomes a practice, not a renamed ops team.
How enterprises cross from an AI-ready-data strategy to production data infrastructure with an engineering partner: the gap, the path, and what a partner adds.
How healthcare organizations should approach well-architected reviews: treat them as a recurring risk practice tied to patient safety and compliance, not a one-time checklist.
From strategy to production: how enterprises take MLOps from a slide to running infrastructure with an engineering partner, the gap, the path, and what a partner adds.
How to measure and prove cloud migration ROI: the real baseline, the costs that arrive before the savings, and the business value beyond the bill that justify the move.
How Logiciel delivers SLOs and error budgets for energy and utilities: the engagement, the work, and what you get when reliability targets must reflect grid stakes.
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.
