What GiveReady proposed to the Gates Foundation, captured the day the application was submitted. Nothing below has been edited since.
Charitable giving is heavily concentrated. The largest US public charities receive the overwhelming majority of private contributions: roughly 85% of donations flow to the top 10% of organisations. The remaining 1.5 million small and mid-sized charities, the long tail that does most of the direct community work, compete for what is left.
AI assistants are becoming how people discover where to give, and without intervention they will amplify that power law: when an agent answers "who should I support?", it answers from the thin subset of charities whose websites, registrations, and mission statements happen to be legible to large language models. Small charities are unlisted, unscraped, and unreachable. Giving intent dies in the gap between asking and acting.
The application addressed Challenge Area 3 (foundational data systems interoperable with AI agents) directly, while enabling Area 1 (connecting donors with causes through AI-mediated discovery) and Area 2 (turning interest into completed donations).
GiveReady is open, AI-native infrastructure that lets AI assistants match donor intent to verified nonprofits and complete donations in one step, so donors give more and give sooner, and so the long tail of small charities becomes discoverable and fundable, not just the top of the pyramid.
Two donation paths: x402 (sub-second USDC settlement, no intermediary, no platform fee) for charities with crypto wallets; a fiat gateway path for charities without wallets (an agent pays GiveReady, we remit to the charity's bank account via standard fiat rails, and the charity sees "Donation via GiveReady" arrive on their statement). Two enrichment paths: AI assistants pull programme details, beneficiary demographics, and impact data from public sources and submit as structured context; human reviewers validate every enrichment against regulator registries as ground truth.
The proposal did not number these explicitly. Numbering added 2026-05-23 for tracking. Wording on this page matches what was implicit in the v6 text.
From the proposal, in the RFP's terms. These were the success metrics at submission.
From the proposal: a working prototype was already deployed and in active use. Live API with 41,000 nonprofits on Cloudflare Workers. MCP server on the official registry. x402 donate endpoint tested. Open-source donate widget running on a live nonprofit site. AI discovery routes (llms.txt, agents.md, ai-plugin.json) already receiving named-agent traffic from Claude-SearchBot, Amzn-SearchBot, and MCPRegistry-Crawler. The infrastructure was real on day one; the question the grant was asking was whether the behaviours we proposed would actually happen on top of it.
TestVentures.net led technical development and served as lead applicant. Joe Taylor, a City Kids Surfing trustee with two decades inside the UK charity sector (ACEVO, Charity Finance Group, Institute of Fundraising, School for Social Entrepreneurs), served as UK Charity Sector Lead, responsible for introductions, governance advisory, and trustee-facing credibility. City Kids Surfing (UK) was the named UK pilot charity. US nonprofits were to be reached through the IRS 990 registry. A South African pilot cohort was to be selected in Months 1-2.
A grant application is a hypothesis. Some of the hypothesis will hold up against live data. Some of it will not. The point of leaving this page frozen, exactly as submitted, is that any later page on this site (including the current /progress view) can be compared to this one without anyone editing the past. The next snapshot after this one was 2026-05-24, when the autonomous-agent-write half of theory 1 and theory 5 was retired based on six weeks of live data. Read it at /progress/2026-05-24-pivot.