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.
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.
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.
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.
