Research
The knowledge infrastructure and evaluation work measuring how well AI systems serve and represent rural communities.
Why this research matters
AI models see, experience and learn about rural America like tourists, not locals.
Artificial intelligence systems learn from what has been written down, digitized, and published — and rural America is underrepresented at every stage of that record. Rural communities are less completely documented online, local news coverage is thinning, and much of what rural people know still lives in archives, organizations, and lived experience rather than in formats AI training pipelines can absorb. The result is measurable: the first systematic audit of place-based knowledge in large language models found that models answer questions about urban counties with significantly more accuracy and depth than rural ones, with the gap concentrated in exactly the knowledge that defines a community — its events, institutions, and social fabric. As AI moves into health care, public benefits, lending, and the software that runs everyday business, systems built on this incomplete record risk missing rural realities in decisions that carry real economic weight. Our research exists to make that gap visible, measurable, and fixable before rural knowledge becomes an afterthought in the systems shaping the next economy.
BRAIN
MVP · Aug 2026Bedrock for Rural AI kNowledge
BRAIN is a memory system for everything rural AI, a centralized knowledge base that connects to all of our agents and workflows. Built to plug in as an MCP connector, it lets any tool we use draw on the same shared, vetted, purpose-built memory rather than searching scattered files one query at a time.
Everything we rely on and everything we will lives in BRAIN. Centralizing that knowledge makes it far more efficient and lets it work at a larger scale, and more accurately, than a language model reaching into a document folder.
It allows any entity to scale a database of authoritative, cited, referenced, knowledge across their organization and then make it usable for others: a municipality, a tribal nation, and trade association.
What Lives in BRAIN
Benchmark
In DevelopmentHow well do today's AI models understand and represent rural information?
Benchmark tests how well today's leading AI models—Claude, Gemini, GPT, and others—actually perform for rural users and rural contexts. Currently, these models tend to default to urban, data-rich environments. This project turns that gap into a public, comparable score.
It won't fix the problem on its own but by making the metro–rural tech-equity gap visible, it starts the conversation and spreads awareness, rather than asking people to just take our word for it. The project is in active development.
How It Will Work
Pose a set of 50+ rural-specific questions to each model and its sub-models.
Grade answers against a rubric: accurate terminology, avoids urban defaults, reflects rural reality.
Post the results publicly so anyone can see where the gaps are.
Re-run on a regular cadence to track how models change over time.
Grounded in Real Rural Expertise
Subject-matter experts help build the initial questions and grading rubric and validate the answers, so scoring reflects real rural experience. From there, we'll build an agentic grading system so the benchmark can run without relying on SMEs every cycle. Any rural or tribal dataset the Benchmark draws on or publishes routes through our Tribal & Community Data Sovereignty Review Process.
Why It Matters
When foundation models underrepresent rural contexts, they inherit blind spots that affect millions of Americans. Public, repeatable results give rural institutions evidence for which tools to trust and give model developers a concrete target for doing better by rural communities.