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.
Rightsizing cloud spend explained: the concepts behind matching resources to real usage, the benefits of doing it, and the trade-offs against reliability headroom.
A practical roadmap to cloud security posture: gain visibility, prioritize by risk, remediate, and prevent with guardrails, so posture improves continuously instead of via one-time scans.
The common AI model risk management pitfalls: policy without controls, pre-deployment-only validation, no intervention path, and uniform governance, and how to avoid each.
How Logiciel delivers Kubernetes at scale for real estate: the engagement, the foundations, and what you get when many services run reliably on orchestration.
A framework for Kubernetes cost control in mid-market and enterprise teams: make spend visible, right-size, and assign accountability, in that order.
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.
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.
