Blog

Thinking about intelligence infrastructure.

Insights on building, evaluating, and operating AI systems at enterprise scale.

Why Your AI Agent Needs an Engineer's Playbook, Not a Bigger Context Window

The most effective AI agents don't succeed because they're smarter. They succeed because someone gave them the same working habits that make human engineers reliable.

8 minMar 2026

Engineering9 min

Diffusion LLMs: A New Architecture for Language Generation

A new class of language models generates text the way image models generate pixels — by refining noise into clarity. Here's what it means for enterprise AI.

Strategy8 min

Why Most Enterprise AI Projects Fail Before They Start

70-85% of AI projects never meet their objectives. The reason isn't model quality — it's everything underneath.

Engineering10 min

Building Ground Truth: The Hardest Problem Nobody Talks About

Ground truth isn't a dataset. It's an engineering discipline. Here's how to build it so your AI systems tell the truth.

Evaluation9 min

How to Evaluate AI Systems That Don't Have Right Answers

Traditional testing has assertions. AI doesn't. Here's how to build evaluation frameworks for non-deterministic systems.

Engineering7 min

Semantic Drift: The Silent Killer of Production AI

Your AI worked perfectly at launch. Six weeks later, it's hallucinating. Here's what's happening and how to prevent it.

Industry7 min

AI in Retail: Beyond Chatbots and Recommendations

The real AI opportunity in retail isn't customer-facing. It's in the data layer that makes everything else work.

Security8 min

AI Governance Without the Bureaucracy

Enterprise AI governance doesn't need 200-page PDFs. It needs engineering. Here's a practical guide to SOC 2, GDPR, and the EU AI Act.

Strategy6 min

The Case for Boring AI Infrastructure

The AI industry celebrates breakthroughs. Nobody celebrates the teams who make AI actually work. That's a mistake.