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Why This Exists

The encoding problem: Professional information is currently structured for human readers—2-page CVs, bullet points, compression for scanability. But professional information is increasingly consumed by AI: recruiter screening tools, agentic browsers, personal assistants.

The encoding no longer matches the decoder.

      This profile is an experiment in encoding professional identity for the actual receiver: AI agents that can fetch, parse, and reason over structured data.
    

The Architecture

The key decision: users interact with my data using their own agents, not mine.

This avoids the bias problem inherent in platforms where the platform's agent interprets profiles for paying customers. LinkedIn's AI describes you to recruiters who pay LinkedIn. Recruitment AI optimises for the employer's goals, not yours.

Here, the profile provides data. Your Claude, ChatGPT, or browser agent reasons over it. You control the interpretation.

What's Different

No 2-page compression—full context for every claim
Evidence linked to skills, not just listed
Verification metadata on every assertion
Structured for queries, not scanning
Progressive enhancement: JSON-LD, llms.txt, MCP (coming)

What This Is

A practical career asset (richer than any traditional CV)
A demonstration of understanding where the agentic web is heading
A potential product prototype
A discovery mechanism for people who think similarly

Try It

Paste alexrevill.com into your AI assistant and ask it questions about my background. The agent will fetch the structured data and answer based on what it finds—not what I've pre-written for it to say.