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I built a free global charity database with 7.9M+ records from 100+ countries
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Hi EA Forum. I have been building GiveRadar (https://giveradar.com), a free platform that lets you look up nonprofits across borders. It currently covers 7.9M+ records from 100+ countries, sourced from official government registries and other datasets.
The project started because I was fundraising for a charity that builds schools in Africa and SEA, and I realized there is no global equivalent of GuideStar. To build the database, I contacted charity registries in over 100 countries.
I thought this community would find it useful. Happy to answer questions about the data, methodology, or what I learned about global charity transparency infrastructure.
Would also love to hear any feedback or ideas!
Cheers.
Matt
Update (October 2026): counts and sources corrected. GiveRadar now covers 7.9M+ nonprofits in 100+ countries from official registers and tax authorities. Since this post, we've added an integrity assessment, an API, and an MCP server for AI agents, and published two research notes: the Global Charity Register Transparency Index 2026 (https://giveradar.com/research/charity-register-transparency-index-2026/) and Charity Finances 2021 to 2024 (https://giveradar.com/research/charity-finances-2021-2024/).
A few things I'd love input on from this community:
What data points matter most to you when evaluating a charity? Right now GiveRadar shows registration status, financial filings, officer data, and more. But I'm curious what the EA community would actually want to see when looking up an organization. Program spend ratios? Staff size relative to budget? Something else entirely?
Also, if anyone has experience with charity data in countries I haven't covered yet, I'd love to hear about it. Especially interested in leads for Sub-Saharan Africa and Southeast Asia where registry data is hardest to find.
And for those who work with IATI, Open Sanctions, or similar open data sources, I'm curious if you've found ways to link organization identifiers across datasets reliably. Deduplication across 25+ country registries has been one of the trickiest technical challenges so far.
Happy to share more about the methodology, data sources, or architecture if anyone is interested.