MamaMatch AI removes the friction between donor intent and maternal health impact — connecting everyday givers to the right partner faster, and bringing them back with personalized context that makes every next gift count.
Most proposals address 1-2 challenge areas. This addresses all three as interdependent layers — each strengthens the others.
The gap: Donors want to give but feel paralyzed by 50+ organizations — most leave without giving.Helps donors navigate a complex, multi-layered cause. AI identifies donor preferences — country, type of support, and urgency — 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."
The gap: Donors on a partner page have intent but not enough conviction — last-mile hesitation is where donations die.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.
The gap: Philanthropic data is siloed — AI cannot translate a gift into meaningful impact without a structured public health data layer.The IHME/LiST public health data pipeline makes philanthropic data visible to AI — connecting country-level mortality data to partner program outputs to produce credible, evidence-based impact estimates for donors.
Both moments are shown below.
MamaMatch AI guides donors from intent to giving in three steps: select a cause, choose a region and urgency, then commit to the right partner — or a portfolio. Donors who already know their partner go straight there. Every path is designed so partner relationships are protected and donor intent is honored.
The matching engine is deterministic — rule-based filters, mortality ranking, Zakat eligibility flags. The AI has one job: when a donor types instead of clicking. That first free-text message is where intent is fragile, phrasing is unpredictable, and keyword routers break. The AI extracts what it can, asks only for what's missing, and hands off to the rule-based engine. These examples show the full range of what it handles.
When a donor types instead of clicking, the AI is what prevents the "I don’t understand" dead end. It reads intent, fills in gaps, answers questions — then hands the rest to the rule-based engine. The structured flow handles the journey. The AI handles the door.
The donor journey doesn't end at checkout. On a donor's return visit (7+ days later), MamaMatch AI recognizes them via client-side localStorage and opens with a personalized follow-up — limited to the same device and browser without cleared history. Two post-donation features close the loop from barrier "Understanding impact" and "Knowing what to do next."
Stored under mamamatch.donations — partner slug, amount, timestamp, impact string. No email, name, or device fingerprint.
Email content is generated at checkout from three signals: (1) donation data (amount, partner, timestamp from Stripe); (2) conversation summary — the AI compresses the chat session into topic tags at session end; (3) country context from the partner selected, not IP geolocation. No name, device fingerprint, or browsing history is used. The personalized email is composed once and queued via Stripe webhook — the donor's email address comes from the checkout form they complete themselves.
Longitudinal partner outcome reporting — where partners submit updated field data months after a gift — is out of scope and requires complementary funding. Year 1 delivers three things: the immediate post-donation message in chat, the Ramadan return recognition via device memory, and the 3-month follow-up email — all generated from data captured at checkout, with no partner data submission required.
Three months after the gift, donors receive a follow-up grounded in donation-time data: the partner they supported, the country context, and the impact estimate generated at checkout. No new partner data is required.
The email is scheduled at the moment of donation and sent automatically 90 days later. All content — the impact estimate, country context, and partner summary — is drawn from data captured at checkout. No partner data submission is required for this email to send.
When a partner's reported output maps to a validated effectiveness coefficient, MamaMatch AI generates an evidence-based impact estimate using country-specific baselines from IHME Global Burden of Disease. When it doesn't, the partner's activity is shown as reported. Every modelled estimate passes a three-condition validity gate and is clearly labeled as a projection, not a measured outcome.
Uncontrolled bleeding after childbirth causes ~27% of all maternal deaths. The E-MOTIVE bundle (oxytocin + tranexamic acid + guided response) cuts severe PPH by 60%. Partners report: bundles delivered + country. Everything else is public data.
IHME Global Burden of Disease is Gates Foundation-funded. LiST (Lives Saved Tool) from Johns Hopkins is the standard used by UNICEF and WHO. When a Gates reviewer reads "we apply LiST methodology with IHME country data," they recognize it — it is the same standard their program officers use internally. This makes estimates auditable, not proprietary.
If any condition fails, show "Impact data not yet available for this partner" — never a fabricated number.
| Intervention | Evidence | Effectiveness | Primary Source |
|---|---|---|---|
| PPH Bundle (E-MOTIVE) | RCT | 60% reduction in severe PPH | Lancet 2023, E-MOTIVE trial (WHO-led, 10 hospitals) |
| Antenatal Corticosteroids | Cochrane | 30% reduction in neonatal deaths | Cochrane 2020, Oladapo et al. — 30 RCTs, 7,774 women |
| Kangaroo Mother Care | Meta-analysis | 20–41% reduction in neonatal deaths | Cochrane 2023, Conde-Agudelo & Díaz-Rossello |
| Modern Contraception | DCP3 Model | 38/100K maternal deaths averted per 1% coverage gain | DCP3 2016, Bhutta et al.; IHME GBD cause fractions |
| Skilled Birth Attendant | Multi-country | 21% reduction in neonatal deaths | Lancet Global Health 2019, Semrau et al. |
| Obstetric Fistula Repair | Clinical series | 65% quality-of-life improvement post-repair | WHO 2018 clinical guideline; Arrowsmith et al. fistula outcomes |
All effectiveness coefficients are from peer-reviewed published sources. Applied via LiST methodology with IHME GBD country-specific baselines. Estimates are labeled as modeled projections, not clinical claims.
Six workstreams that turn a working prototype into a production AI system — from infrastructure to measurement. Each phase builds on the last; the knowledge base and partner data pipeline feed every downstream component.
We use a stepped-wedge trial (Hemming et al., BMJ 2015) — MamaMatch AI rolls out sequentially to donor cohorts during Ramadan 2027. Each cohort serves as its own control before activation. Control: donors reaching Every Pregnancy partner pages directly (pre-launch Ramadan 2026 baseline from existing payment and analytics data). Treatment: Navigator-assisted donors during Ramadan 2027.
The "test" in the Gates requirement refers to measuring real donor behavior — specifically whether AI-assisted matching causes measurable change in giving rate and gift size compared to the pre-launch baseline. This is the stepped-wedge trial described below, not software QA.