The Next Economy Runs on Agents. Your Site Is the Infrastructure.
Minnesota.AI is a field lab for AI discoverability engineering. We run controlled experiments on how AI systems find, parse, cite, and route users to websites — and publish the data openly.
Read All Articles View Live DataOriginal Research With Live Data
Every claim is backed by a controlled experiment. These are documented findings from live sites with real traffic and measured outcomes.
Live AI Data Feed
Rolling 24-hour window of AI crawler activity across sites we operate and experiment on. Updated hourly from Cloudflare analytics.
What can AI access? — LLM Structure. The semantic markup, entity clarity, and machine-readable patterns that make a site legible to language models.
What does AI understand? — LLM Visibility. The measurable signals that determine whether a model correctly represents what you do.
What does AI choose? — LLM Discoverability. How organizations get cited, recommended, and routed to in AI-assisted search and agent workflows.
The semantic markup, entity clarity, and machine-readable content patterns that determine whether AI systems can parse a site accurately. We study what makes content legible to language models.
The measurable signals that determine whether a language model understands what an organization does. We research how AI models interpret and represent entities — and what structural patterns drive accurate representation.
Visibility is being readable. Discoverability is being chosen. We study how organizations appear in AI-assisted search, answer engines, and agent-driven workflows — and what moves the needle.
How lightweight, production-ready systems combine user input, AI inference, decision logic, and measurable outputs. We study what makes these systems work and publish the frameworks behind them.
When language models understand you, they recommend you.
Search behavior is changing. Users are increasingly asking AI systems to compare options, recommend vendors, and guide decisions. Organizations that structure their content for LLMs gain visibility. Those that make their information clear and retrievable gain discoverability.
This is what we study — and everything we find, we publish.
David Valencia
Minnesota.AI is where I publish what I find about how AI systems discover, interpret, and recommend websites.
Pilots + Partnerships
If you are running a site where AI discoverability matters and want to collaborate on a controlled pilot, we would welcome the conversation.
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