Notes on building an AI-ready company
Field notes, frameworks, and templates from the engagements we run with SaaS, consulting, and service teams.
Private AI Infrastructure, Built Four Ways
Four private AI deployment patterns — local workstation, owned server, rented regional GPU, and managed open-model endpoint — followed by a practical architecture guide covering control, context, inference, operations, model sizing, and cost.
The AI Boomerang: Why Cheaper Cognition Brings Work Back to the Business
AI lowers the minimum viable unit of work, bringing neglected customers, cases, reviews, and ideas within economic reach. The management job is turning new capacity into useful activity — not just fewer people.
AI Can't Use Knowledge Your Company Can't Find
Scattered access points give AI too many doors and no reliable route. A practical case for the knowledge vault — the governed context layer that makes company knowledge usable by copilots, search, and agents.
How to Choose an AI Model Stack Without Creating a Dependency Trap
Closed, enterprise, sanitized, private, or no-AI — the model stack is a control spectrum. A practical framework for routing workloads by sensitivity, criticality, cost, and reversibility.
The AI Cost-Cutting Trap: Why Cheaper Work Often Gets More Expensive
Unlimited AI access without workflow design turns cost reduction into cost multiplication. Here's how to attach AI to real economic outcomes.
The Integration Ceiling: Why SMEs Believe in AI But Still Cannot Use It
Tool access is rising faster than operational change. The adoption problem has moved from persuasion to integration — and pilots hide the real cost.