Thinking out loud about AI, architecture, and building things that work
Research, case studies, and notes from the work.
MCP Universe and AI limitations
On LinkedIn I wrote a short piece on how the limitations of AI impact the design and architecture of AIUX, the process of designing interfaces for AI. We will need to pay close attention to how this develops.
Making Excel AI Friendly
MDN (Markdown Notation) project defining a simple, AI-readable spec for representing tabular and contextual data for AI systems
Strategic decision feedback
An application that uses AI to provide critical feedback in go-to-market decisions. Outcome: Startup concept, effective technology but could not resolve on GTM. Retired.
Don't Build AI Tools. Build With AI.
The real opportunity in the AI era isn't building AI tools, but creating AI-native companies that fundamentally rethink how value gets created and delivered.
AI in Regulated Contexts
Exploring how compliance, audit, and defensibility change when AI becomes part of the workflow. Outcome: Developed AI tool to write compliance training scripts and documents. Client Project
The Future of Proofreading: AI Consensus Systems
How combining multiple AI proofreaders with consensus-based decision making can achieve better results than either humans or AI alone. Client Project
The Three Layers of AI Data
Understanding the three critical data layers that form the backbone of reliable AI decision-making: Training Data, Proprietary Data, and External Data.
Streamlining External Data with Preprocessed Repositories
How preprocessed data repositories can streamline external data integration for corporate AI systems, reducing complexity and accelerating time-to-value.
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