A membership organization with thousands of researchers wanted to help them find funding, at a point when research money was getting harder to secure. Members had been asking for a better way to find grants for a long time, as it is time consuming and takes time away from doing actual research, and the organization already had the relationships and subject expertise to do it well.
The system continuously collects grant opportunities published across the web, builds a profile for each researcher from the organization’s existing records, and emails every researcher a monthly set of matches that fit their field, career stage and eligibility. The researcher decides what to apply for. Everything runs inside the organization’s own environment, so member data never leaves it.
How it works
The Grant Recommendation Engine runs as a pipeline of four MisaCores - modular building blocks from our platform. Each one owns a single stage, from collection to delivery, and the whole pipeline runs inside the customer's own environment.
Pulls and deduplicates grants continuously from 28+ public and licensed sources, including grants.gov, Simons Foundation, Scientify, and more.
Scores every grant against a researcher’s profile using semantic understanding, surfacing and ranking the strongest fits.
Screens out grants the researcher doesn’t qualify for before anything reaches them.
Researcher profile data is redacted before any AI model sees it. Every action is logged.
