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.
RAG architecture explained: the concepts behind retrieval-augmented generation, the benefits when it fits, and the trade-offs to weigh before building on it.
The state of managed AI services in enterprise for 2026: what enterprises are buying as managed, where they keep control in-house, and how the buy-vs-build line has settled.
How to approach developer experience in enterprise organizations: treat DevEx as a measurable outcome with an owner, not a perk, and improve the friction that slows delivery.
A practical roadmap to GitOps: the phased path from ad hoc operations to a reviewed, self-correcting, auditable system, with what to do at each stage and what to avoid.
How Logiciel delivers securing AI systems for real estate: the engagement, the work, and what gets delivered when AI handles listings, valuations, and tenant data.
The common application modernization pitfalls that stall or sink projects, big-bang rewrites, no definition of done, lost domain knowledge, and how to avoid each.
Embedding AI into existing products explained: the concepts, the benefits when it works, and the trade-offs to weigh before adding AI to a product already in production.
How to measure and prove AI reliability engineering ROI: the cost of unreliable AI, the improvement, and the business value that justify the investment with a number.
Incident management explained for energy and utilities leaders: what it is, why it matters when systems affect the grid, and what to know to make incidents recoverable.
GitOps explained for real estate technology leaders: what it is, why it matters for reliable software delivery, and what to know before adopting it.
How to measure and prove internal developer platform ROI: the developer-productivity baseline, the improvement, and the business value that justify the investment.
A VP Engineering's introduction to building AI-ready data: what makes data ready for AI, why it is usually the bottleneck, and where to start making data AI-ready.
A practical roadmap to designing for scale: the stages from identifying breaking points to scaling deliberately, so systems grow without hitting a wall or over-building.
How healthcare organizations should approach AI observability: what to monitor beyond uptime, the clinical and compliance stakes, and how to start without boiling the ocean.
How to measure and prove distributed tracing ROI: the diagnosis-time baseline, the improvement, and the business value that justify the investment with a number.
The common pitfalls of data lakehouse architecture, and how to avoid them: governance, table formats, small files, and the discipline that keeps a lakehouse reliable.
The questions a CTO should ask when choosing an ELT modernization partner: about approach, data, cost, governance, and handoff, so the engagement delivers and lasts.
Why the buy-vs-build AI decision becomes decisive as real estate teams scale, and how getting it right concentrates scarce engineering on what differentiates.
How to build the business case for distributed tracing in healthcare: the value, the cost, and the patient-care and compliance stakes that justify the investment.
The common pitfalls of re-platforming monoliths, and how to avoid them: scope, data, dependencies, and the disciplined approach that keeps the move from going sideways.
The state of cloud security posture in enterprise for 2026: where posture management stands, the pressures shaping it, and what enterprises should prioritize.
A decision guide for VP Engineering weighing modern data architecture against the status quo: when to modernize, when to wait, and how to decide on evidence not hype.
How to measure and prove the ROI of CI/CD pipeline design: the metrics, the baseline, and the business case that turn pipeline investment into a defensible return.
How Logiciel delivers incident management for healthcare organizations: the practice, the controls, and the operating model that make incidents recoverable without risking patient safety.
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.
