Research & practice
Practical writing on building, securing, governing, and operating AI systems.
The EU's AI transparency rules now apply. Here is what product and engineering teams need to change in interfaces, content pipelines, provenance, and operational evidence.
As AI agents gain access to tools and data, identity and authorization become the real security boundary. A practical architecture for least privilege, delegation, approvals, and audit.
Long-running AI agents change the unit of knowledge work from a conversation to a delegated outcome. That changes planning, review, management, and the metrics that matter.
Cheap tokens do not guarantee cheap work. Measure AI by the full cost of an accepted outcome, then route each task to the least expensive system that can complete it reliably.
Government, legal, medical, and industrial clients require AI on their own infrastructure. Here's what on-premises AI deployment actually involves.
AI-specific vulnerabilities like prompt injection, data poisoning, and model extraction — what they look like in practice and how to defend against them.
Every AI project budget underestimates the real costs. Here's where the money actually goes — and how to build a budget that holds.
Complex AI workflows need multiple agents with defined roles, handoffs, and failure modes. Here are the multi-agent architecture patterns that work in production.
Before investing in AI, know where you stand. Here's the framework for assessing AI readiness and building a realistic implementation roadmap.
Search engines are no longer the only path to discovery. Here's how to optimize your online presence for ChatGPT, Perplexity, Claude, and Gemini.
Cloud inference is expensive, slow, and dependent on connectivity. Here's how we deploy computer vision models on edge devices for real-world applications.
What a fractional CTO does in an AI startup's first year — the decisions that matter, the mistakes to avoid, and milestones that unlock investment.
Prompt engineering gets the press, but structured generation is what makes AI reliable in production. Here's when to use each approach.
90% of AI proof-of-concepts never reach production. Here are the real reasons why — and what to do about them before you start building.
Most RAG demos look great until they hit real data. Here's what actually breaks in production and how to engineer around it.