Personalized AI matching that connects everyday givers to the right maternal health partner — based on their values, geography, and giving preferences — across a coalition of 50+ frontline organizations.
Maternal health is complex — spanning prenatal, delivery, and postpartum care across dozens of countries. Donors arrive motivated but face barriers that suppress giving: the cause feels abstract, the choices are overwhelming, and the path from intent to impact is unclear.
Maternal health spans before, during, and after birth. With 50+ partners across 15 countries, donors can't see where their money goes — the cause feels like an "abstract term" rather than a concrete action.
Of donors who reach a donation page, nearly 4 in 5 leave without giving. The intent is there — the path is unclear.
Of those who abandon, most say they'll come back. The vast majority never do. Every moment of friction costs lives.
Sources: Fundraise Up, Donation Page Friction Study (2024); M+R Benchmarks; Snowball Fundraising
Most proposals address 1-2 challenge areas. This addresses all three as interdependent layers — each strengthens the others.
Helps donors navigate a complex, multi-layered cause. AI identifies donor preferences — geography, intervention type, faith-based giving — and matches them to 1-3 partners whose work resonates. Turns "I want to help mothers" into "Here's the organization doing exactly what you care about."
Establishes a clear, simple pathway from motivation to meaningful contribution. AI-driven personalization across channels — seasonal campaigns, donor re-engagement, targeted outreach — makes the act of giving easier for everyday donors across different countries and communities.
Partners report diverse data — healthcare workers trained, delivery kits distributed, C-sections performed — in different formats via Google Drive. This pillar builds a dashboard-based reporting system with AI that translates diverse partner data into a uniform, compelling donor story.
Motivated to support maternal health — but unsure where to give among 50+ organizations
Conversational matching recommends 1-3 partners based on donor values, geography, and preferences
Builds understanding of the donor — geography, intervention interest, giving capacity, faith-based motivation — to create personalized matches and segment into actionable personas
WHO/UNICEF data grounds each partner's work in real maternal health gaps — not abstract appeals
Higher conviction, better match, to the organization whose work resonates most
Donor sees their $10 become $30 through the Pregnancy Boost Fund, funding 10 months of antenatal care. Personalized impact stories — not receipts — drive repeat giving.
Where it's most needed and most aligned with donor values
AI prioritizes under-funded organizations when donors are undecided — leveling the playing field within the coalition and directing funds where they're needed most
Open data standards, matching patterns, and findings shared for replication
When donors arrive at Every Pregnancy, they're motivated — but two distinct barriers stand between intent and donation. Each requires a different AI intervention.
These are different donors at different stages. Problem 1 is an entry point problem — "help me decide." Problem 2 is a last mile problem — "help me commit." Each gets its own AI tool below.
The Navigator starts by asking the most important question: do you already know which partner you want to support? Donors who arrive with a specific partner in mind go straight to that partner — no alternatives shown, no split suggested, no diversion of funds. Donors who are undecided get the guided flow: 2–3 questions on motivation, region, and budget, then the AI narrows 50+ partners down to the best matches. Only after a donation is complete do we invite the donor to make an additional gift to a similar partner — protecting each partner's relationship with their loyal donor base.
Demo 1 solves the "where should I give?" problem well. But not every donor arrives undecided. Many land directly on a partner page — from a social media post, a friend's link, a Google search, or a Ramadan campaign. They already know who. They need to know why this one and why now.
A chatbot doesn't serve this donor — it adds a new interaction on top of the page they're already reading. What's needed is contextual AI that works within the partner page experience: surfacing evidence, answering hesitations, and reducing the distance between "I'm interested" and "I'm donating."
This is for donors who are already on a partner page with intent. The AI widget doesn't summarize what's already visible — it adds what's missing: WHO health data for the partner's country, how donations are used, Zakat eligibility, and impact at specific giving levels. It meets the donor at the point of highest intent and removes the last reasons not to give.
WHO/UNICEF data grounds each partner's work in real maternal health gaps. AI generates the connecting narrative — never fabricates claims.
"In Bangladesh, where only 59% of births are attended by skilled health workers, Mercy Without Limits provides prenatal checkups, safe delivery kits, and emergency obstetric referrals in rural Sylhet — one of the country's most underserved regions."
Health data: WHO/UNICEF (attributed, dated). Partner description: partner-approved. AI generates the connecting narrative only.
Before AI can match donors to partners, it needs to understand who's giving and why. We're building a donor intelligence layer from partner interviews, behavioral data, and persona-driven A/B testing.
Interview partners to understand their existing donor base — age, gender, geography, giving patterns. These insights seed the initial persona models and reveal what motivates real donors.
Build actionable donor personas that guide segmentation, communication, and AI matching. Each persona represents a distinct motivation and giving pattern — from first-time Zakat givers to recurring monthly donors.
Test persona-driven experiences against controls. Iterate based on what segmentation works. Build toward automated lead scoring that improves matching accuracy over time.
The Grand Challenge awards up to $150K per project for 1 year. Co-funding is welcomed. We're matching the grant with $150K in EP resources — demonstrating commitment and extending reach.
Grant allocation is EP's proposed budget. Co-funding per team discussion (Apr 10, 2026). Grand Challenge: co-funding welcomed but not required (FAQs).
No existing AI tool solves the coalition navigation problem — routing donors across a curated network of organizations in a shared cause area.
| Platform | Approach | EP's Differentiation |
|---|---|---|
| Charity Navigator | AI ratings across 1.5M+ charities | EP goes deep within one cause across a curated coalition |
| Daffy | Conversational AI for DAF holders | EP serves mass donors, especially Ramadan/Zakat givers |
| Fundraise Up | AI-optimized single-org donation forms | EP innovates on which org — the routing layer |
| DonorSearch | AI tools for fundraising teams (B2B) | EP's Navigator is donor-facing — donors are the users |
| Muslim Charity UK | AI-personalized Ramadan emails | EP applies AI across 50+ orgs with Zakat routing |
No AI tool tackles coalition-level donor routing — a structural gap in philanthropic technology.
Source: Competitive analysis — Charity Navigator, Daffy, Fundraise Up, DonorSearch serve different niches (see Competitive Landscape above)
EP originated from a Gates Foundation planning initiative on maternal health and the Muslim community in 2023. This proposal extends a relationship the Foundation already invested in.
Source: everypregnancy.org; Devex profile for Marleen Vellekoop; Gates Foundation grant records
Platform, partners, donors, and data already exist. The grant funds the AI layer — not building from scratch.
Source: EP production platform (everypregnancy.org), 50+ active partner integrations, Stripe payment infrastructure
Ramadan provides a 30-day controlled window with 3 years of campaign baseline data ($13M in 2024, $21M in 2025, $91M in 2026). Plus $150K in co-funding — $300K total commitment.
Source: EP Ramadan campaign reports (2024-2026); co-funding per team discussion Apr 10, 2026; Grand Challenge FAQs confirm co-funding is welcomed
Matching, engagement, and data infrastructure as interdependent layers — not stapled together.
Source: Grand Challenge criteria — connect, convert, infrastructure (gcgh.grandchallenges.org)
Ramadan/Zakat AI is under-researched. Muslim Charity UK achieved 4.1x increase in donations per email using AI-personalized Ramadan outreach — for a single org. EP applies this at coalition scale.
Source: Muslim Charity UK, Insight in Fundraising Conference 2024 award; JCASC AI-Enabled Islamic Philanthropy Framework (2024)
Open findings, open schema, replicable patterns. $150K produces knowledge, not just features.
Source: Grand Challenge values "approaches that generate insights" and learning (FAQs); SSIR framework on AI for nonprofits
AI matching can prioritize smaller, under-funded partner organizations when donors are undecided — ensuring the coalition's smallest members benefit from the platform, not just the largest.
Source: Team ideation meeting (Apr 10, 2026) — addressing equity within the coalition
$91M raised in Ramadan 2026 from 192K+ donors. The intent exists at massive scale — the gap is between intent and informed action.
Source: EP Ramadan 2026 campaign results (everypregnancy.org)