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 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.
The state of LLM evaluation and testing in healthcare for 2026: from ad hoc spot checks to rigorous, clinical-grade evaluation before and after LLMs touch care.
Semantic layer design explained for enterprise leaders: why your reports disagree, how a semantic layer fixes it, and why the hard part is agreement, not technology.
Choosing a semantic layer design partner: the questions a Director of Analytics should ask to get consistent, governed metric definitions, not just another modeling tool.
How to approach AI governance in energy and utilities: make it risk-based and operational, concentrated on grid-affecting AI, so it enables adoption instead of stalling it.
How Logiciel delivers data contracts for real estate: the engagement, the work, and what you get when data producers and consumers agree on enforced expectations.
High-availability systems explained for healthcare leaders: what HA is, why it matters when systems affect care, and what to know without being an engineer.
How Logiciel delivers infrastructure as code for the enterprise: the engagement, the work, and what you get when infrastructure becomes versioned, reviewed, and reproducible.
Choosing a DevSecOps partner: the questions a VP of Engineering should ask to find one who embeds security into delivery without slowing it to a crawl.
A practical roadmap to monitoring LLMs in production: track output quality, not just latency and cost, because an LLM fails by being wrong while looking healthy.
A decision framework for vector databases in mid-market and enterprise teams: when you need a dedicated one, when an extension suffices, and how to choose.
A Director of Analytics checklist for vector databases: choose and operate one based on recall, latency, and cost at your scale, not benchmark hype.
A CDO's checklist for change data capture: stream source changes reliably without overloading systems or losing events, so downstream data stays fresh and correct.
A practical roadmap to RAG architecture: build retrieval quality first, prove grounding on a real corpus, then scale, because retrieval is the ceiling, not the model.
What is DataOps? A data platform lead's guide: applying DevOps discipline to data pipelines, automated testing, CI/CD, and monitoring, so data ships reliably.
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.
