Notes from the frontier.
What we learn getting AI into production across whole businesses: what works, what stalls, and what it takes to have an impact.
AI-native operations
Building the AI-native enterprise: the principles Native works by
An AI-native enterprise has people making the calls, agents doing the work and every department rebuilt in turn. The principles Native builds toward.
AI engineering
Building an AI-native SDLC: the four foundations
An AI-native SDLC stands on four foundations: evals, software fundamentals, coding agents and shaping the build. Here is each one in production.
AI adoption
Why most AI pilots never reach production, and what the ones that ship do differently
AI pilots stall after the demo for five reasons: no owner, no system-of-record integration, no evals, no written authority and no change plan.
MCP and agents
The Model Context Protocol, explained for business and IT leaders
MCP is an open standard for connecting AI agents to the systems a business runs. What it does, when it helps, the security questions, and how to start.
AI adoption
An AI adoption playbook: from the first workflow to the next department
A practical AI adoption playbook: map the department, pick the first workflow, write the authority, gate on evals, train champions, measure impact.
Case study
AI in production in under six months
150+ AI deployments across 8 departments, all in production in under six months.
Put AI to work across your whole business.
Bring the hardest problem on your list. In 30 minutes we'll show you where AI will have the most impact first, and what it takes to get there.