FundFlow AI — Scholarship Funding Intelligence

Students searching for scholarships and grants often face hundreds of opportunities without knowing which ones they actually qualify for. Eligibility requirements such as nationality, GPA, study level, field of study, and required documents are frequently buried in lengthy application pages, forcing students to manually evaluate opportunities one at a time. FundFlow AI solves this problem by turning funding discovery into an eligibility intelligence workflow. A student creates a funding profile, and FundFlow AI uses Bright Data to retrieve live scholarship and funding pages from the web. AI/ML API then extracts structured eligibility requirements from the retrieved content and evaluates them against the student's profile. Instead of presenting a black-box recommendation, FundFlow produces an explainable GO, MAYBE, or NO-GO decision. GO indicates that no known mandatory requirement has been failed, MAYBE identifies missing or uncertain information, and NO-GO identifies clear eligibility failures. The platform also explains why a student qualifies, highlights potential issues, identifies required documents, and provides a plain-language recommendation. The MVP was built end-to-end with native.builder, using Supabase for the database and Edge Functions powering the discovery, extraction, and matching pipeline. Bright Data provides live web retrieval, while AI/ML API powers structured extraction and eligibility reasoning. FundFlow AI is positioned Africa-first, beginning with students such as our Nigerian undergraduate demo profile, while the architecture supports students searching for funding opportunities globally.