Start with the organisation
Matching can use mission, organisation type, geography, objectives, focus areas, project criteria and other available profile information. Better profile context leads to better-informed recommendations.
Grant matching
How FundRobin Matches Grants to Your Organisation
Smart Matching is deliberately different from keyword search: FundRobin uses organisation context and eligibility conditions before deeper fit analysis, then gives users reasoning they can challenge.
Eligibility first
Explicit eligibility conditions are considered before deeper fit analysis. This is a screening process, not an eligibility certification.
Strategic fit second
AI can assess how the available opportunity information aligns with organisation context and produce a rationale. A score helps prioritise research; it is not a win probability.
A recommendation you can interrogate
The rationale is intended to show why FundRobin considered an opportunity relevant, including the evidence and context available at matching time.
Matching that can learn explicit constraints
Recommendation → user marks not relevant and explains why → FundRobin assesses whether the reason is reusable → an actionable preference can be added to organisation matching context → future matching can be refreshed.
- Actionable feedback can cover focus, geography, funder preferences, funding size and eligibility concerns.
- Vague feedback may not produce a reusable preference.
- Positive feedback is recorded but is not currently converted into a new matching preference.