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.
How to build anomaly detection that doesn't cry wolf: tuning for signal over noise, so alerts are trusted and acted on instead of ignored.
Why idempotency is essential in event-driven systems and how to design idempotent APIs: idempotency keys, deduplication, and the controls that make retries safe.
Why AI adoption fails on the human side, not the technology, and how change management, trust, training, and workflow fit, drives the adoption that delivers value.
The query patterns that quietly drain data warehouse budgets, and how to find and fix them with attribution, monitoring, and the controls cost discipline needs.
Which developer experience metrics actually predict delivery speed, and which are vanity, so you measure what improves throughput instead of activity.
How enterprise AI model cards document what matters, intended use, limits, data, and performance, so models are used appropriately and governed, not shelfware.
How to architect for data residency by design: keeping data in required jurisdictions from the start, so global compliance is built in rather than retrofitted.
A pragmatic path to real-time analytics: confirming you actually need it, choosing the right latency tier, and the controls a production real-time system needs.
How to manage embeddings at scale: storage, refresh when models or data change, and versioning, so vector-based features stay consistent and current in production.
How to build the business case for reliability by quantifying the true cost of downtime, direct, indirect, and compounding, so reliability investment is justified.
How to validate streaming data quality in flight: schema and semantic checks on events as they arrive, with the controls that stop bad data before it propagates.
How to do capacity planning for AI inference fleets: modeling demand, latency, and cost together, and the controls a production inference fleet needs.
How to build a compliance-ready audit trail for AI decisions: capturing inputs, model version, and rationale so every decision can be explained and reconstructed.
How to take data science from notebooks to production: reproducibility, pipelines, deployment, and the engineering discipline that turns experiments into systems.
The tenant isolation patterns for multi-tenant SaaS, shared, siloed, and hybrid, their tradeoffs in cost, security, and operations, and how to choose for your product.
How to design AI-powered products that degrade gracefully when the model fails: fallbacks, confidence thresholds, and the controls a resilient product needs.
How to build carbon accounting pipelines for Scope 1, 2, and 3 emissions at scale: data ingestion, methodology, auditability, and the controls reporting needs.
What a DERMS data platform requires to orchestrate distributed energy resources: real-time data, state, and control, with the reliability a production system needs.
How meter-to-cash analytics find revenue leakage in utilities: tracing the process end to end, detecting anomalies, and the controls a production system needs.
How to optimize battery storage dispatch under market and physical constraints: balancing revenue, degradation, and limits, with the controls a production system needs.
How utilities use AI on satellite and LiDAR data for vegetation management: the data pipeline, prioritization, and the controls a production deployment needs.
How to build utility demand forecasting that integrates weather, load, and price signals reliably, with the validation and controls a production forecast needs.
How to bridge SCADA and OT systems to the cloud safely: one-way data flow, segmentation, and the controls that get cloud analytics without exposing operational systems.
Why grid forecasting is moving to the edge, close to the meter, the latency and connectivity drivers, and the controls a production edge-forecasting deployment needs.
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.
