Loading
Loading
ScholarAI evaluates 25+ signals — academics, need, achievements, research — to match students with the right scholarships and give institutions a fair, auditable ranking of every applicant.
Scholarships awarded through the platform
Student profiles evaluated by the AI engine
Match accuracy validated against committee outcomes
Average time to a full explainable ranking
No black boxes. Every score traces back to a published weight and a verifiable input.
Academics, income, achievements, research, documents — 25+ signals, entered once, verified by the OCR pipeline, reused for every application.
A transparent weighted model scores each area — Academic 22%, Financial Need 18%, Achievements 12% — nothing hidden, every weight published.
Each scholarship gets a match score, a winning probability, and a plain-language explanation of why it fits — or exactly what's missing.
Committees see the same explainable ranking, fraud signals, and verification status — decisions become auditable instead of arbitrary.
Students get matched. Institutions get fairness. Everyone gets receipts.
A weighted model across 25+ signals. Every recommendation ships with its full score breakdown.
Eligibility prediction, match scores, and win probability across 12 scholarship categories.
Income certificates, marksheets, and IDs verified with cross-document consistency checks.
Statistical anomaly signals — mismatched claims get flagged before committees see them.
ATS scoring, skill extraction, and targeted suggestions tuned for scholarship review.
Quarter-by-quarter improvement plan with projected score impact for each action.
Ranking heatmaps, demographics, geographic distribution, and exportable reports.
Ask why you matched, why you didn't, and what single change raises your odds the most.
We replaced a three-week manual shortlisting cycle with a two-hour review of ScholarAI's ranked list. The explainability panel is what won over our committee — every score has a paper trail.
I had no idea I qualified for a research grant. ScholarAI flagged it, told me exactly which two documents I was missing, and I was funded within a month.
The fraud signals alone paid for the platform. We caught four inconsistent income declarations in the first cycle — cases we would previously never have noticed.
A published weighted model: Academic Performance 22%, Financial Need 18%, Achievements 12%, Research 10%, Leadership 8%, Projects & Skills 8%, SOP 8%, Community Service 7%, Recommendations 4%, Behaviour 3%. Every sub-score shows the exact inputs used — you can audit your own number.
Matching should never cost the people who need funding.
For universities and foundations running selection at scale.
National-scale programs with compliance requirements.