Student applications, assessments, and mentor matching automated in one semester
A leading Hungarian university wanted to attract high-potential students before they applied. In roughly one semester, Rollout IT delivered a mentoring platform that connects assessments, personalized development paths, mentor assignment, and administrator reporting in one system, live on September 15, 2024.
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Snapshot
| Field | Detail |
|---|---|
| Client | One of Hungary's top universities (anonymized) |
| Industry | Higher education / EdTech |
| Region | Hungary |
| Company stage | Established university recruitment and talent program |
| What we built | Student mentoring, assessment, and reporting platform |
| Tech stack | NestJS, TypeORM, Orval, React, TypeScript, Tailwind, PostgreSQL, Apache Superset |
| Time-to-value | Development from June 2024, first phase live September 15, 2024 |
| Headline KPIs | 1-month design phase – first release in ~3.5 months – automated assessments and mentor matching |
The challenge
The university wanted to become more attractive to top high-school students by giving them a clearer path into academic life. The platform had to do more than collect applications: it needed to assess personality, interests, and academic direction, then connect students with mentors who could guide them toward relevant development paths. Administrators also needed reporting, because a mentoring program becomes impossible to scale when progress, test results, and mentor activity are scattered across manual tools.
Why the university chose Rollout IT
The project called for product design, full-stack development, QA, and data visualization in a tight timeframe. Rollout IT brought a multidisciplinary team that could turn an academic framework into usable workflows while staying flexible as the testing logic and reporting requirements evolved mid-project.
The solution
We designed and built a platform that guides students from application through assessment and mentor assignment. Students complete personality and career-interest tests, then receive personalized development paths aligned with areas such as entrepreneurship or engineering, and mentors track progress and give tailored recommendations. For administrators, Apache Superset provides reporting over student results, progress, and program metrics.
The system uses a React and TypeScript frontend with Tailwind, a NestJS backend, TypeORM, Orval, and PostgreSQL for structured relational data. Weekly client meetings kept academic stakeholders close to the product, and mid-project changes, including test-evaluation logic and internal Superset hosting, were handled collaboratively without slipping the September 15 release. Delivering to a fixed academic calendar is exactly where a partner earns trust: the date could not move, and it did not.
Results
- Student applications, assessments, and mentor matching moved into one automated workflow.
- Administrators gained a visual reporting layer for monitoring student data and test results.
- The university reported positive feedback from students and administrative users on ease of use.
| Metric | Before | After | Timeframe |
|---|---|---|---|
| Program workflow | Manual, fragmented student tracking | Automated applications, assessments, mentor matching | June-Sept 2024 |
| Design readiness | Concept and requirements | Design phase completed | 1 month |
| Reporting | Limited visibility into progress data | Apache Superset dashboards for administrators | First release |
| Release schedule | Development start June 2024 | Live on September 15, 2024 | ~3.5 months |
The AI angle
No AI capability is claimed for the current phase. The strongest angle is data-driven mentoring: structured assessments and reporting help the university guide students earlier and more personally.
What's next
The second phase should expand data extraction and deepen recommendation logic, making mentorship paths more precise and helping the university spot high-potential students earlier. Rollout IT is set up to take that next step.
FAQ
How fast was the first version delivered? Development started in June 2024 and the first phase went live on September 15, 2024, roughly 3.5 months, with a one-month design phase.
What does the platform automate? Applications, personality and career-interest assessments, mentor matching, personalized development paths, and administrator reporting.
How do administrators see progress? Through Apache Superset dashboards covering student results, progress, and program metrics.
What was it built with? NestJS, TypeORM, Orval, React, TypeScript, Tailwind, and PostgreSQL, with Apache Superset for reporting.
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