GiveAI — World's Largest Vetted Nonprofit Database

The world's largest vetted nonprofit dataset — 8 million+ organizations across 80+ countries, combining structured financial data with vector embeddings so AI applications can search, verify, and reason about any nonprofit in the world.

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What's in this dataset

This dataset has two components that work together:

all_ngos/ — Master Nonprofit Records (~8M records)

One document per nonprofit, containing the full organizational profile:

• Identity — legal name, EIN/registration number, country, formation year
• Location — full address, formatted address, country code
• Contact — phone, email, website
• Mission — stated mission, beneficiaries served, program details, geographic reach
• Financials — up to 3 years of revenue, expenses, assets, liabilities, donations received/made, salaries, government grants, investment income
• Aggregated stats — max/min/avg across years for all financial fields
• Tax & compliance — tax exempt status, report type (990/990PF/990EZ), political affiliation, donation permissions (domestic and foreign)
• References — source URLs from official registries
• Vector embedding — 256-dimensional semantic embedding of the organization

vectors_optimized/ — Attribute-Level Embeddings (~14M records)

Where all_ngos has one embedding per organization, this dataset has a separate embedding for each individual attribute — mission, revenue, volunteer count, program details, and more. This enables fine-grained semantic search:

• Find nonprofits whose mission matches a query
• Find nonprofits with a specific financial profile
• Find nonprofits by cause area or geography independently

Each record links back to all_ngos via ngo_id and includes the organization's full financial summary alongside the targeted embedding.

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Coverage

Data sourced from IRS 990 filings, national charity registers, and proprietary data pipelines across 80+ countries. All organizations are verified against official government records.

Region	Organizations
United States	~1.6 million
Canada	~86K
United Kingdom	~171K
Rest of world	~6.2 million
Total	8M+

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Embeddings

All vector fields are generated using OpenAI text-embedding-3-large with dimensions=256.

To perform semantic search against this dataset, embed your query with the same model:

from openai import OpenAI
client = OpenAI()

response = client.embeddings.create(
    model="text-embedding-3-large",
    input="your search query here",
    dimensions=256
)
query_vector = response.data[0].embedding  # 256-dim list of floats

Then run nearest-neighbour search against the vector column using your vector DB of choice (Qdrant, Weaviate, pgvector, FAISS, etc.).

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Why structured + vectors?

Most nonprofit databases are either purely structured (good for filters, bad for semantic search) or purely text (good for search, bad for analytics). GiveAI combines both:

• Structured fields → filter by country, revenue range, tax status, cause area
• Vector embeddings → semantic search in natural language
• Together → hybrid queries like "climate nonprofits in Southeast Asia with assets over $1M"

This makes it natively compatible with RAG pipelines and conversational AI — an AI assistant can look up, verify, and reason about any nonprofit in the world using this dataset as its knowledge base.