Before the First Prompt
AI changes how we produce code, but reliable software still begins with source control, automated testing, environment discipline, and CI/CD.
Read article →Services
Practical technology leadership for organizations that need structure, security, and direction without unnecessary complexity.
AI Knowledge Architecture
Design the knowledge structures, metadata, retrieval boundaries, governance, and decision flows that make enterprise AI useful, trustworthy, and secure.
Private & Sovereign AI
Control where AI runs, what knowledge it can access, what may leave the organization, and when private or frontier models are appropriate.
Fractional CIO & AI Strategy
Ongoing executive technology leadership for strategy, governance, vendors, architecture, investment priorities, and practical AI adoption.
Knowledge Operations
Keep institutional knowledge current, governed, documented, and usable through ownership, lifecycle management, runbooks, and AI-supported workflows.
Articles
Practical guidance on technology leadership, infrastructure, security, governance, and the operating decisions behind durable systems.
How We Got Here
Decades of software engineering practice provide the architecture, shared logic, and persistent project instructions needed to guide AI coding agents.
Read article →The Work Order Is the New Unit of Software Development
Translate business requirements into bounded, testable Work Orders that let AI agents implement software within an established engineering framework.
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Knowledge Architecture Begins Where Your Organization Ends
AI becomes valuable when it moves the right knowledge to the right person at the right time, especially when the customer's objective extends beyond the organization's internal responsibilities.
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Content Types Were Proto-AI Knowledge Objects
Long before vector databases and semantic retrieval pipelines, enterprise architects were already attempting to solve the problem of computational meaning at organizational scale.
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We Have Been Here Before
Artificial Intelligence is forcing organizations to confront a problem that enterprise architects, search engineers, and information architects have wrestled with for decades: how to structure knowledge in ways that preserve meaning, authority, and institutional memory at scale.
Read article →Notes
Recent observations on AI, knowledge architecture, organizational systems, technology, and the practical work of making them useful.
The Foundation of Intelligence
We assume that a sufficiently intelligent AGI will decide humanity is a threat. But cooperation under pressure is not simply morality. It may be one of the foundations of intelligence itself.
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You Don't Need AGI to Build a Dystopia
You do not need AGI to build a dystopia. Specialized AI and surveillance infrastructure are already enough to create systems that see too much, infer too much, and act too easily.
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Did AI Really Do It?
When an AI system behaves badly, we increasingly describe the AI as the actor. But the models, objectives, tools, permissions, compute, and environments were all supplied by humans.
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You Need a Career Autobiography
Resumes compress careers until important experiences disappear. A career autobiography preserves the projects, failures, detours, technologies, and relationships that explain how you became who you are.
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Not Everything Belongs in the RAG
Enterprise AI projects often assume that more data creates a better system. Sometimes the right architecture depends on knowing what should not be indexed, embedded, retrieved, or exposed.
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