Philanthropic Scout Network

Inspiration

Grantmakers want to support effective community programs, but evidence about charitable organizations is often scattered across reports, websites, spreadsheets, and conversations.

I built The Philanthropic Scout Network around a simple question:

How can organizations operating on shoestring budgets improve their chances of being discovered for funding by leveraging AI?

The idea was to explore a model where grantmakers can proactively scout organizations instead of relying entirely on inbound applications.

What it does

The Philanthropic Scout Network is an AI-assisted discovery and evidence-review prototype.

For charities, it explores whether progress and evidence can be reused across reporting, discovery, and future grant applications.

For grantmakers, it allows them to set funding priorities and explore organizations through a provincial map using information such as program focus, documented milestones, and supporting evidence.

Each organization has a structured profile for deeper review, including unanswered questions and evidence gaps.

An ElevenLabs-powered voice analyst allows grantmakers to ask questions about the selected organization.

After human review and authorization, the prototype also demonstrates a test funding transaction using Solana devnet.

The prototype uses fictional organizations and demo data.

How we built it

I built the prototype using JavaScript, HTML, CSS, Node.js, Docker, and Vultr.

Auth0 handles authentication, ElevenLabs powers the voice analyst, and Solana devnet demonstrates a test transaction and receipt.

The experience also includes an interactive Canadian map and a versioned evidence workflow.

Challenges we ran into

The biggest challenge was combining discovery, evidence review, conversational AI, authentication, and payments without overwhelming the user.

I also had to keep the AI grounded in the selected organization’s records and separate analytical support from actual funding authority.

The final workflow became:

discovery → evidence review → human decision → demonstrated transaction

Accomplishments that we're proud of

I’m proud to have built an end-to-end prototype that tests whether grantmakers can proactively discover grassroots organizations based on their priorities.

For charities, the prototype also explores whether reusable evidence can reduce some of the administrative burden associated with reporting and proposal writing.

What we learned

I learned that philanthropic discovery and accountability have to work together.

AI can help surface organizations, summarize records, and identify evidence gaps, but funding decisions should remain with people.

What's next

The organizations in this prototype are fictional. The next step is to learn how charities and grantmakers in Canada and the U.S. currently approach discovery, reporting, and funding decisions, and test whether this model can improve that process.

There is also more to explore around data quality, verification, consent, and ensuring smaller organizations are not disadvantaged by weaker reporting infrastructure.

Built With

• css
• html
• javascript
• typescript