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.
Taking a warehouse migration from strategy to production with an engineering partner: the gap between a migration plan and a migrated warehouse nobody's reports broke on.
Why deployment automation matters for scaling energy and utilities teams: manual deployment becomes a bottleneck and a risk as systems multiply near the grid.
How to approach change data capture in real estate: prioritize reliable, log-based capture of the data that matters, with deletes and schema changes handled, not just streaming.
How to measure and prove modern data architecture ROI: quantify the cost of the legacy foundation and the value of what a modern one enables, against the rebuild cost.
The common platform engineering pitfalls: building what nobody adopts, gold-plating, mandating instead of earning adoption, and no product mindset, and how to avoid each.
Hallucination mitigation explained: the concepts behind reducing and containing LLM hallucination, the benefits of doing it, and the trade-offs against cost and latency.
How to measure and prove buy-vs-build AI ROI: compare the fully-loaded cost of building against buying, per capability, including speed and the value of differentiation.
An SRE lead's introduction to CI/CD pipeline design: build the pipeline as a reliability tool, with the gates, safety, and rollback that protect production.
A CTO's checklist for multi-agent orchestration: coordinate AI agents reliably with clear control, bounded autonomy, and observability, before scaling complexity.
Taking production-grade AI from strategy to production with an engineering partner: building the reliability, monitoring, and safe-failure a working model lacks.
Invest in formal incident management or keep your current approach? A CTO's decision guide on when the practice pays off and when ad hoc response is still enough.
Taking developer experience from strategy to production with an engineering partner: the gap between a DevEx vision and friction actually reduced for developers.
How Logiciel delivers LLM evaluation and testing for real estate: the engagement that verifies LLMs are accurate and safe before they touch listings, valuations, and tenants.
MLOps for enterprise explained: the concepts behind operationalizing ML at scale, the benefits of reliable model delivery, and the trade-offs in cost and complexity.
How Logiciel delivers designing for scale for the enterprise: the engagement that finds the bottlenecks, proves scaling under load, and builds the operational maturity.
How Logiciel delivers cloud cost optimization for the enterprise: the engagement that makes spend visible and owned, fixes structural drivers, and sustains the savings.
Taking pipeline monitoring from strategy to production with an engineering partner: the gap between "we'll monitor our pipelines" and catching silent data failures.
Best practices for medallion architecture at scale: enforce layer contracts, govern transformations, and manage cost, so bronze-silver-gold stays trustworthy as it grows.
Choosing a platform engineering partner: the questions a Head of Platforms should ask to find one who builds a platform developers adopt, not shelfware nobody uses.
The common infrastructure as code pitfalls: drift from manual changes, no state discipline, copy-paste sprawl, and no testing, and how to avoid each.
Invest in CI/CD pipeline design or keep your current deployment process? A VP Engineering decision guide on when the investment pays off and when it is premature.
How energy and utilities teams add pipeline monitoring without disruption: instrument running data pipelines incrementally, starting where failures hit operations.
How to approach semantic layer design in real estate: get the organization to agree on one definition per metric (NOI, occupancy, cap rate) and govern it, then build.
The 2026 trends shaping change data capture in healthcare: real-time, reliable sync of clinical data with the PHI handling and auditability healthcare demands.
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.
