Research organizations sit on something valuable - deep researcher profiles, domain expertise, and a membership that trusts them. What most of them lack is a scalable way to connect each researcher to the funding they're actually eligible for.
Manual grant matching doesn't scale. Off-the-shelf AI tools can't touch the proprietary data. And the researchers who need funding most are the ones spending the most time searching for it.
MisaLabs built a recommendation engine that continuously pulls grant opportunities from across the web, matches them against researcher profiles held inside the organization's own environment, and delivers personalized funding matches automatically. Their data never leaves their systems. The engine runs and updates without staff intervention.
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 funding opportunities from 28+ sources like grants.gov and the Simons Foundation, structuring them into one clean, current feed.
Scores every opportunity against a researcher's profile, then surfaces the strongest fits, so researchers see only the grants worth their time.
Screens out grants a researcher doesn't qualify for before anything reaches their inbox, so every recommendation is one they can actually pursue.
Redacts researcher profile data before any AI model sees it, and logs every action end to end. Profiles stay inside the customer's environment, always.
Data source
Grants are pulled from external sources and stored internally. Researcher profiles never leave the environment - there is no outbound profile data, ever.
Infrastructure
Grant store
Deployment
Profiles
