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.
The data architecture behind virtual power plants: ingesting and coordinating distributed energy resources in real time, with the controls reliable aggregation needs.
How to automate mortgage underwriting with AI while staying within fair lending limits: explainability, disparate-impact testing, and the controls a compliant model needs.
How to turn commercial building IoT from sensor sprawl into operational insight: data integration, normalization, and the controls a building data platform needs.
How to design rent optimization that improves revenue without regulatory and antitrust risk: independent inputs, transparency, and the controls a defensible system needs.
What computer vision for property inspections can reliably do in production today, what it cannot, and how to deploy it with human oversight where it matters.
How to move real estate portfolio analytics from spreadsheets to a platform: a single source of truth, automated data flow, and the controls portfolio decisions need.
How to build energy benchmarking data pipelines for buildings and ESG reporting: ingesting meter and utility data, normalization, and the controls reporting needs.
How document intelligence handles real estate contracts, leases, and titles reliably: extraction, validation, and the controls a production document workflow needs.
How to tame PropTech data integration across MLS, CRM, and ERP: a canonical model, reconciliation, and the controls a reliable property data platform needs.
How AI lease abstraction turns lease PDFs into structured data reliably: extraction, validation, human review, and the controls a production workflow needs.
Where automated valuation models still fall short in 2026, the data, edge cases, and confidence gaps, and how to deploy AVMs with the guardrails they need.
Guardrail patterns for healthcare chatbots that avoid liability: scope limits, escalation, disclaimers done right, and the controls a safe patient-facing bot needs.
What it takes to ship AI as Software as a Medical Device under FDA oversight: classification, validation, change control, and the controls a regulated AI product needs.
How to architect payer-provider data exchange for the new interoperability rules: APIs, standards, and the controls a compliant, scalable exchange needs.
How to build medical imaging pipelines that handle storage, AI inference, and compliance at scale: DICOM, model integration, and the controls a clinical deployment needs.
How to design clinical decision support that avoids alert fatigue: relevance, specificity, and the controls that keep alerts trusted and acted on, not dismissed.
Techniques for de-identifying healthcare data at scale for analytics and AI: methods, re-identification risk, and the controls a compliant de-identification pipeline needs.
How to manage patient consent for healthcare AI: capturing, enforcing, and honoring consent across data uses, with the patterns and pitfalls a compliant system needs.
Where AI delivers real ROI in healthcare revenue cycle management, coding, denials, prior auth, and where it does not, with the controls a production deployment needs.
What FHIR-native architecture means and how it delivers real healthcare interoperability: using FHIR as the model, not a translation layer, plus the controls it needs.
How to build a healthcare data lake that governs PHI at petabyte scale: access control, lineage, de-identification, and the controls a compliant platform needs.
What ambient clinical intelligence actually requires in production beyond AI scribe demos: accuracy, clinician workflow, compliance, and the controls that make it safe.
How to build an AWS backup and disaster recovery plan that actually works: RPO/RTO targets, tested restores, and the controls that turn backups into real recovery.
How AWS PrivateLink enables private B2B SaaS integrations without exposing services to the internet: architecture, patterns, and the controls a production setup 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.
