How Brian Impact Foundation Uses Claude to Review 100x More Fellowship Candidates

Brian Impact Foundation was created to find and fund the next generation of social innovators—people using technology, and increasingly AI, to solve global problems. Founded after Kakao’s Brian Beom-su Kim pledged more than half his net worth to social causes, the organization manages millions in grants with a team of just 13 people. Its fellowship program identifies and supports individual innovators, but identifying the right candidates from a global pool while operating lean was a persistent constraint.

The core problem was volume and equity. Without AI, the team relied on its expert network to surface candidates, leaving a vast field of potentially deserving innovators unreviewed. Reviewers had to conduct laborious manual searches to compile candidate profiles, and with limited staff hours, the fellowship effectively had a ceiling on how many people it could fairly consider.

The team built BEES—Benevolence Enhancing Expert System—on Claude. BEES gathers diverse data related to social problems and innovation from the open web, then sends the raw data to Claude to extract candidate information, summarize it, and analyze data quality and structure. Claude then organizes the information according to the foundation’s evaluation criteria and surfaces it in a custom web app, where reviewers can access a comprehensive view of each prospective candidate. Claude’s long context window was a decisive factor, given the volume of news articles and unstructured source material processed for each candidate.

The outcome was a step change in scale. In the most recent fellowship cycle, the foundation reviewed over 20,000 candidates—100 times more than in the prior year. Claude also tripled the number of reviews per candidate, enabling the team to apply more thorough evaluation to every applicant rather than skimming the surface of a smaller pool. The process became more equitable: the foundation could support innovators it would never have reached through its prior network-dependent approach.

For Brian Impact Foundation, AI is not just an operational lever—it’s central to the mission. By automating the data-gathering and summarization work that consumed reviewer time, Claude freed the team to focus on judgment and impact. The foundation can now pursue a larger, more diverse candidate pool while maintaining its strict commitment to evaluating every application thoroughly.