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 DevOps lead's checklist for implementing container orchestration: get the foundations (networking, storage, security, observability) right, not just a running cluster.
How Logiciel delivers data lineage for the enterprise: the engagement, the work, and what you get when data flow becomes automatically captured, current, and navigable.
How to measure and prove real-time data ingestion ROI: quantify the value of fresh data for decisions, against the higher cost and complexity versus batch.
How Logiciel delivers feature stores for real estate: the engagement, the work, and what you get when ML features become consistent, reusable, and governed.
How to build a business case for high-availability systems in healthcare: justify the cost on the clinical and operational impact of downtime, not just uptime numbers.
Best practices for MLOps at enterprise scale: standardize the model lifecycle, automate the path to production, and monitor models, so many models ship reliably.
Choosing an AIaaS adoption partner: the questions a VP of Engineering should ask to find one who helps draw the consume-versus-build line, not just wire up APIs.
Taking AI model risk management from strategy to production with an engineering partner: the gap between a risk policy and operational controls on real models.
Container orchestration or your current deployment approach? A VP Engineering decision guide on when orchestration's complexity pays off and when it is premature.
How to measure and prove data unification ROI: quantify the cost of fragmented data, the value of a unified view, and weigh it against the integration effort.
A practical roadmap to multi-cloud strategy: adopt multi-cloud for a real reason, deliberately, because accidental multi-cloud multiplies cost and complexity for nothing.
A decision framework for internal developer platforms in mid-market and enterprise teams: when an IDP is worth building, what to build first, and when it is premature.
How to build a business case for LLM evaluation and testing in energy and utilities: justify it on the operational risk of deploying an unevaluated LLM near the grid.
Self-service analytics explained: the concepts behind letting people answer their own data questions, the benefits when governed, and the trade-offs when it is not.
A practical roadmap to production-grade AI systems: turn a working model into one that handles real inputs, stays reliable, is monitored, governed, and fails safely.
Kubernetes cost control explained for enterprise leaders: why Kubernetes bills balloon from over-provisioning, and what to know to control cost without hurting reliability.
Choosing a Snowflake vs. Databricks partner: the questions a VP of Engineering should ask to get a fit-based recommendation, not a partner selling their preferred platform.
How to build a business case for hallucination mitigation in energy and utilities: justify it on the operational cost of a confident wrong AI output near the grid.
How to build a business case for Kubernetes at scale in real estate: justify it on operational consistency and scaling, not hype, and weigh the real complexity cost.
The state of re-platforming healthcare monoliths in 2026: incremental, strangler-based modernization that protects clinical continuity, not risky big-bang rewrites.
Choosing a chaos engineering partner: the questions an SRE lead should ask to find one who runs disciplined, safe experiments that build resilience, not reckless breakage.
The 2026 trends shaping AI-as-a-service adoption in enterprise: where enterprises consume AI as a service, where they keep control, and how the line is settling.
Data contracts explained for energy and utilities leaders: what they are, why they prevent silent data breakage in grid and operational systems, and what to know.
How to approach data unification across systems in real estate: build a unified layer over running systems and resolve identity well, rather than consolidating everything.
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.
