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.
Data lakehouse architecture explained for enterprise leaders: what it is, why it combines the lake and the warehouse, and what to know before adopting it.
A Head of Platforms checklist for implementing multi-cloud: pursue it for a real reason, standardize across clouds, and contain the complexity, or consolidate.
Taking an observability strategy from strategy to production with an engineering partner: the gap between an observability vision and answering "what broke" affordably.
Why model latency optimization matters for scaling healthcare teams: slow AI inference becomes a clinical workflow bottleneck and a cost driver as usage grows.
A framework for data contracts in mid-market and enterprise teams: where to apply contracts, what to put in them, and how to enforce them without slowing everyone.
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.
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.
