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Case studyEdTech

Rank universities, degree programmes and funded research by how well they fit the applicant.

Year
2026
Vertical
EdTech

The challenge

Choosing where to apply abroad means comparing one profile against thousands of programmes, scholarships and research groups spread across separate university websites. Applicants do that comparison by hand, one tab at a time, and still miss the funding they qualify for.

What we built

01AI matching engine that turns an applicant profile into a searchable vector
02Compatibility ranking across universities, degree programmes and funded research groups
03Scholarship and funding database of 4,000+ records
04Document vault with AI-assisted feedback on each upload
05Per-application tracking and automated email outreach across eight destination countries

Outcomes

4,000+
Scholarship and funding records
8
Destination countries covered
Vector search
Profile to programme matching

How we shipped it

01

Profiles as vectors

The matching engine converts an applicant's academic history, research interests and constraints into a vector, then ranks programmes and research groups by compatibility rather than by keyword. It means a candidate surfaces funded groups they would never have searched for by name.

02

Funding is the hard part

A programme match is useless if it is unaffordable, so funding sits in the same index as the programmes — a 4,000-record scholarship and funding database that ranks alongside the academic results instead of living in a separate list.

03

After the shortlist

The platform carries the applicant past the decision: a document vault that gives AI-assisted feedback on each upload, per-application status tracking, and automated email outreach across the eight destination countries the product covers.

Stack

Python
FastAPI
Next.js
PostgreSQL
Redis
Live · accepting briefs/lat 25.20 lon 55.27
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