New thinking on enterprise AI is live → Read The Deployment Layer
Home Solutions Advisory Our Impact Resources AI Ethics About 📰 Newsletter Contact us
Frameworks · Thinking · Tools

Practical thinking on
enterprise AI
that actually ships.

No hype. No vendor pitches. Just honest frameworks, hard-won lessons, and weekly thinking from practitioners who build AI systems for a living.

Featured resource

The Deployment Layer

The gap between AI experimentation and production is where most teams fail. Weekly thinking on enterprise AI architecture, agent systems, and responsible deployment — for the people who have to make it work.

Subscribe free
Weekly
Publishing cadence — every week, no exceptions
100%
Practitioner-written — no AI-generated filler
Free
Always free to subscribe — no paywall
Topics we cover

What you'll learn

Every issue covers one topic in depth — from architecture decisions to governance frameworks to hands-on implementation.

Enterprise AI architecture
Scalable RAG pipelines, agentic workflows, real-time inference systems, and the reference architectures that survive contact with production.
MLOps & GenAI ops
Model governance, monitoring, drift detection, versioning, and cost controls. The operational backbone AI needs to stay reliable after launch.
Responsible AI & governance
Fairness, transparency, auditability, and regulatory compliance. How to build AI systems your stakeholders can actually trust.
LLM evaluation & selection
How to evaluate LLM providers, run rigorous PoCs, and choose vector databases and AI platforms based on evidence — not marketing.
Agentic AI systems
Designing, building, and governing autonomous AI agents — including the failure modes most teams don't anticipate until it's too late.
Team & org design for AI
How to structure AI teams, build internal capability, and create Centers of Excellence that accelerate delivery instead of slowing it down.
Frameworks & guides

Free resources from the field

Practical frameworks developed through real engagements — free to use, built from experience, not theory.

The aiApas Production Framework
Methodology
Our proprietary 4-phase framework: Assess → Architect → Deploy → Govern. The system we use for every production AI engagement.
Read the framework →
AI Model Governance Checklist
Checklist
40-point assessment covering data governance, explainability, bias testing, model documentation, and monitoring — aligned to SR 11-7 and NIST RMF.
Get the checklist →
Regulatory Compliance Matrix
Reference
Full mapping of AI governance requirements across GDPR, SOX, FCRA, BSA, GLBA, CCPA, OCC/SR, and NIST FISMA — with implementation guidance.
Download matrix →
Bias Detection Methodology Guide
Guide
Step-by-step bias detection methodology — with metric definitions and reporting templates for production AI systems.
Read the guide →

Want the thinking behind the frameworks?

Subscribe to The Deployment Layer — weekly enterprise AI architecture thinking from the practitioners who build these systems.

Subscribe free