Public Record Intelligence
Building the canonical structured understanding of US public records.
The United States generates an enormous volume of financially valuable public records every day. Insurance rate filings that predict earnings. Probate inventories that reveal asset transfers. Mineral deeds that identify tradeable royalty positions. Municipal check registers that signal government contractor revenue. Assessor records that map infrastructure lease holders. All of it is public by law. None of it is accessible at scale.
How It WorksWhat Exists Today
Many Verticals, One Intelligence Layer
Every vertical is an application built on the same underlying data asset, not a separate business. Every new jurisdiction added to the pipeline activates data for every vertical simultaneously.
The Problem
The data is scattered across 19,500 municipalities, 3,100 counties, 50 state agencies, and thousands of special-purpose courts and commissions. Each jurisdiction publishes in its own format, on its own website, behind its own filing system. No one aggregates it because the acquisition cost has always been too high -- too many jurisdictions, too many formats, too much human labor.
The big data vendors cover New York and Chicago. Nobody is systematically reading Gorham, Maine or Franklin, New Hampshire. But small and mid-sized municipalities issue the same building permits, the same check registers, the same assessor records as large cities -- just on worse websites, with less standardization, behind more obscure FOIA statutes. That's where the gap is widest and the signal is least picked-over.
AI agents change the math. What used to require a team of 50 people filing FOIA requests and parsing PDFs can now be done by one person with an automated pipeline. We built that pipeline. It runs daily, autonomously. The same architecture extends to every public record type in the country.
Leading Indicators, Not Lagging Reports
Municipal check registers, vendor payments, and budget approvals are the source documents that bond ratings eventually reflect. We structure them daily. When a town's spending pattern shifts, that's the signal, months before rating agencies act.
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We work with hedge funds, infrastructure companies, data platforms, and aggregators who need structured government data.
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