Overview

Daffodil’s results-based impact enablement model is the structure for how we apply a lens of accountability to the activities of participating nonprofits. It’s this lens that we use to distribute from the Daffodil Discovery Fund. Impact data produced by the model can also be provided to interested partners in ancillary verticals.

Daffodil consumes nonprofit generated content and data in 3 levels per organization:

• Level 1: we meet the orgs where they are and consume their already publicly available data, from social media, newsletters, 990 forms, website, etc.
• Level 2: nonprofits opt-in to Daffodil surveying, which is designed to get a deeper level of understanding of the org’s general capacity, health, and performance across known and unknown programs.
• Level 3: nonprofits push internal data directly to Daffodil, such as financials, KPIs, CRM info, etc.

Nonprofit Incentives

The Daffodil Discovery Fund exists as a pooled fund that gets distributed to nonprofits that are actively participating on the Daffodil network, contributing data at any of the levels above, and contributing data of high quality that allows us to build a story of their impact.

Organizations receive monthly distributions from the fund. These financial contributions, which are unrestricted donations, serves as a powerful incentive to continue participation and improve the level and quality of their contributions over time, for mutual benefit.

Discovery Fund

Donors and DAF holders on Daffodil’s giving platform allocate a portion of their funds towards the Daffodil Discovery Fund. This is the donors’ mechanism of discovery, powered by matchmaking and a monthly commitment of granted dollars. This gives them feedback on organizations they would otherwise not have known about. Distributions are algorithmically allocated based on the donors’ set preferences, and can be adjusted at any time.

Data Extractions

From any and all data collected, Daffodil extracts the following using a proprietary AI/LLM data processing pipeline:

Structured extractions:

• key metrics
• beneficiaries
• timeframes
• program names

Unstructured extractions:

• supporting stories