Funding Follows Networks, Not Keywords
Most organizations prospect for foundation funding using keyword searches, program descriptions, and geographic filters. We tested those methods against co-funding network analysis across the full IRS dataset and found a fundamentally stronger signal.
12.8x
stronger signal than keyword matching
1M+
grants in the dataset
16,000+
curated foundations
300,000+
nonprofit recipients
Most organizations lack a systematic way to understand the foundation landscape around them. Who funds organizations like theirs? Where are the gaps? Which foundations are expanding into new areas?
SciRise is that intelligence layer.
What SciRise Does
Network Matching
Ranks foundations by how closely their giving patterns align with organizations like yours. Not keyword overlap. Not geography. Actual funding behavior across the full grant record.
Landscape Analysis
See where your foundation funding stands relative to similar organizations. Identify concentration risk, peer benchmarks, and untapped segments of the foundation landscape.
Foundation Screening
For each matched foundation, AI screens the application process, typical grant size, deadlines, and board and staff connections to your organization.
The Pattern
The co-funding 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: they fund the same types of organizations through the same co-funding clusters.
In validation testing on held-out historical grants, network-matched foundations were 12.8x more likely to have actual funding relationships than keyword-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 firsthand how little systematic intelligence exists around foundation funding, even at major research universities, and recognizing that the data to close that gap already existed in IRS filings.
SciRise is built on a simple conviction: the information nonprofits and universities need to make better funding decisions already exists in public data. It just hasn't been structured, connected, or made accessible. We believe every organization should be able to see its full foundation landscape clearly, not just the foundations it already knows about.
Explore your foundation landscape.
Create a free account to see your top matched foundations, peer benchmarks, and landscape analysis.