Nonprofit
An AI workflow resolved about half of applications in testing and assembled evidence for cases requiring expert review.
A non-profit organization needed to assess applicants for a high-profile international forum. Reviewers had to establish identities, verify affiliations, investigate gaps, research background and social profiles, and decide which cases required closer attention. Routine research consumed time experts could spend on difficult cases.
The Experiment
ArcticBlue translated the organization’s criteria into an AI workflow that researched each applicant, checked identity and affiliation evidence, and routed uncertain cases to people.
Cases sent to experts included supporting links, relevant evidence, and unresolved concerns. Reviewers could inspect the reasoning and investigate specific issues without restarting the research.
Results
About half of applications resolved automatically during workflow testing.
Zero false approvals in the final validation batch of 100 applicants.
Evidence and outstanding questions assembled for applications requiring expert review.
63% reduction in time spent overall.
Why it mattered
The workflow handled routine checks while giving experts a better starting point for complex decisions. Automation reduced the research burden without hiding uncertainty or removing escalation. The validation results established a concrete basis for assessing the workflow’s usefulness before broader use.
