Funding Follows Networks, Not Keywords
Most teams read the funding landscape from press releases, mission statements, and keyword searches. We built SciRise on a stronger signal: the giving behavior recorded in millions of IRS filings. It shows who actually funds what, where capital concentrates, and where money is moving, for the impact investors, consultants, nonprofits, and foundations working across the mission-driven economy.
12.8x
stronger signal than keyword matching
4.8M+
grants in the dataset
16,000+
curated foundations
300,000+
nonprofit recipients
Most teams lack a systematic way to read the capital flowing around a mission, an organization, or a field. Who funds work like this? Where is money concentrating, and where is it pulling back? Which funders are expanding into new areas?
SciRise is that intelligence layer.
What SciRise Does
Network Matching
Ranks funders by how closely their actual giving behavior aligns with a given organization or field. Not keyword overlap. Not geography. Revealed funding patterns across the full grant record.
Landscape Analysis
Map how capital concentrates around a cause or field: who the major funders are, how a portfolio compares to its peers, and where the gaps and white space sit.
Funder & Recipient Intelligence
Trace any funder or recipient through the network: grant history, typical grant size, co-funders, board and staff connections, and how money moves between them.
The Pattern
The network signal appears consistently across the dataset. Here is one example of how it surfaces matches that other methods miss.
Case Study
Lincoln Financial Foundation is based in Pennsylvania. Boys & Girls Clubs of Northeast Indiana is in Fort Wayne. Different states. Zero keyword overlap.
Our network model ranked Lincoln Financial #1 out of 69 candidate funders for Boys & Girls Clubs. They've since made a $50,000 first-time grant.
Geography matters in philanthropy. But network signal is stronger.
Why This Matters
Keyword and geographic prospecting surfaces foundations that look similar on paper. Network analysis surfaces foundations that behave similarly: their actual grant patterns reveal alignment that profiles and keywords miss.
In validation testing on held-out historical grants, network-matched foundations were 12x more likely to have actual funding relationships than AI-matched results.
See our validation methodology →Why Trust the Data
IRS source filings
All foundation and grant data comes directly from IRS 990 and 990-PF filings. No self-reported directories. No third-party aggregators.
Explore our data →Curated, not scraped
16,000+ foundations vetted for active grantmaking of $1M+ annually. DAFs, captive entities, flow-throughs, and closed foundations are removed so every result is actionable.
Validated on real outcomes
The 12.8x signal strength is measured on held-out historical grant data: real foundations, real recipients, real dollars. Not synthetic benchmarks.
See validation results →Why This Exists
Michael Fern, PhD
Founder & CEO, SciRise
Michael is Chief Administrative Officer for Population Health Sciences at Duke University School of Medicine and holds a PhD in Strategic Management from UNC Kenan-Flagler Business School. He built SciRise after seeing how little systematic intelligence existed around how mission capital moves, even inside major institutions, and recognizing that the data to close that gap already sat in public IRS filings.
SciRise is built on a simple conviction: the information needed to understand the mission-driven economy, who funds what, where capital concentrates, and how organizations are financed, already exists in public data. It just hasn't been structured, connected, or made usable. We believe impact investors, consultants, nonprofits, and foundations should all be able to see the capital landscape clearly, not just the parts they already know.
See the capital landscape clearly.
Request a demo to see how SciRise maps funders, flows, and relationships across the mission-driven economy.