Artificial Intelligence

AI implementations are won or lost before anything gets built. The two-week current state assessment: silos, interviews, pain math, and the blueprint.

A smaller model won't give the identical answer a frontier model would — but for most business work both answers are acceptable, and one costs a fraction. Here's the engineering that makes that true, with the research behind it.

Everyone asks which AI tool to buy. Almost nobody asks whether their information is organised enough to use one. Here's why organising is engineering work — and the four rules I apply before any AI touches a knowledge base.

They said prompt engineering would be dead by now. Instead it became the core of context engineering, and both live inside orchestration. Here are the three nested layers, and how to tell which one your agent is failing at.

Gartner: 60% of AI projects without AI-ready data get abandoned. Why the data foundation — not the model — decides whether your AI enablement survives.

You cannot fit a database in a prompt — so how does ask-anything AI work? The retrieval architecture that answers from millions of rows without reading them all.

Default AI claps for everything you do. Here is the exact agent setup I use to engineer an AI with a spine — a critic, adversarial agents, evals, and a kill switch.

88% of AI agent pilots never ship. Here is the architect playbook that puts SMBs in the 12% — a data foundation, one governed agent, and MCP.

How I would architect Agentforce 360 + Data 360 to resolve service cases autonomously — grounded in live data, escalating only the hard ones. A use case.