The Shared Pattern
Both companies start from the same insight: the data exists, it's public, but nobody has assembled it. Parcel records across 3,000 counties. Hospital cost reports across 6,000 facilities. The value isn't in any single dataset — it's in the cross-reference. And now AI can make it conversational.
Who they serve
Landowners & capital allocators navigating data center, solar, wind, and conservation deals
Hospital CFOs & health system leadership navigating $50M–$5B capital projects
The information gap
$300B in institutional land assets. Owners don't know what their land is worth for emerging uses.
$150B/yr in healthcare capital spending. CFOs rely on $200K–$600K consultant engagements for data they could have instantly.
Data moat
Parcel boundaries, grid infrastructure, solar/wind potential, soil, ecology, zoning, sale comps — from county, federal, and state sources
10.5M records: hospital financials, discharge data, cost reports, demand forecasts, bond data, construction projects, consultant RFPs — from CMS, EMMA, state FOIA, Census
The killer query
"I own 500 acres in West Texas. Solar farm, wind farm, data center, or keep farming?"
"I'm hiring a consultant for a market study. What should I pay, and what does Eagle already cover for free?"
What AI enables
Plain-English queries across geospatial layers. "Find me 40 acres near a substation, outside the floodplain, fast-permitting county."
Conversational intelligence across the full capital planning chain. Demand → sizing → cost → finance → build → operate → prove.
Where they are
$4M seed, 5–7 person team, beta platform, building data layer
Solo founder, live product with real users, 260+ database tables, 67 AI tools deployed, $0 raised
Cost to build the data layer
Mostly free — federal downloads, county scraping, satellite imagery APIs
Under $3K/year — federal downloads, FOIA, EMMA bond docs, state portals
Eagle — What's Already Built
Total database records10.5M
Healthcare facilities tracked13,509
Health systems with financials224
Capital projects on file4,992
Discharge market share records706K
Demand forecast data points2.7M
Consultant RFPs with scope data102
AI tools in production67
Data acquisition cost (Year 1)<$3K
VC funding raised$0
The thesis is the same: assemble the data nobody else has, make it queryable, and arm the decision-maker. Aarden does it for land. Eagle does it for healthcare capital. The pattern works because the data is public, the assembly is hard, and AI finally makes it useful.
Enjoyed the conversation, Len. Happy to go deeper anytime.