Most of your revenue
comes from the students you already have.
In a private university, dropout is concentrated in the first year and at re-enrollment. Orakia anticipates which students are at risk, in the first weeks, by combining the data you already have. So you retain them before they leave, not after.
There are as many journeys as students. And you can't operate them all.
You recruit, enroll, welcome. But after orientation, communication splits across degrees and faculties, and you lose the thread with the student. By the time you see the dropout, they stopped logging into the campus weeks ago.
- Dropout clusters at two points. The first year (frustration with the platform, failing to adapt, low performance) and re-enrollment for the next term.
- Students don't read your emails. Email reminders and the anonymous survey arrive late and reach few. By the time they answer, they've already decided.
- Recruitment runs on volume, not on signals. Thousands of leads and few advisors. Without knowing which student is a good fit and which family recommends you, you convert blind.
The dropout signals are already in your data: learning platform, assignments, payments, student services. What's missing is reading them in time.
Centralize. Understand. Get ahead.
Orakia doesn't replace your campus platform or your registrar. It connects the data you already have on each student and tells you who is at risk, while you can still support them.
Connect what you already collect on each student
Learning platform (logins, assignments, grades), registrar and academic CRM, payment status, student services, surveys. All in a single record per student and cohort. We start with whatever you have, even if your sources are disconnected.
It tells you which students will drop out and why
It doesn't leave you with a map of a thousand journeys. It groups students into risk archetypes and tells you what weighs on each one (adapting to the platform, performance, payment, support) and what to do. Every prediction comes explained, so you can defend it in front of the board.
Act in the window that matters
Alerts in the first weeks and before re-enrollment: the student who doesn't log into the campus, the one who doesn't submit work, the one who hasn't completed payment. In time for a call, a tutoring session or a financing option, not three days too late.
First results in 2-4 weeks. If you want to see where you stand today, book a 30-minute assessment.
We start with whatever you have.
We don't need to standardize everything to get started. We work with what's there and give you the risk map by student and cohort.
Orakia classifies them in minutes: what students are saying, by faculty and by year, without reading them one by one.
Every prediction with its variables. It isn't a black box: it's a model leadership can understand and defend.
Put a number on last year's dropout.
Multiply the students who didn't re-enroll by the average value of the years they had left. That number is the cost of not getting ahead. Orakia helps you see it in the first weeks, not at the end.
Frequently asked questions
How does Orakia anticipate that a student is going to drop out?
Orakia combines the signals you already have: learning platform activity, assignments, grades, attendance, payment status, student services and surveys. It flags at-risk students in the first weeks (not logging into the platform, not submitting work, not enrolling for the second term) so you can step in early.
We have as many journeys as students. How do you manage that?
Not with one journey per student: with a few actionable risk archetypes. Orakia groups students by behavior pattern and tells you what to do with each group, instead of leaving you with a map nobody can operate.
Is it a black box? The board will want to know why it predicts what it predicts.
No. Every prediction comes with the variables that explain it: what drives each student's risk (adapting to the platform, performance, payment, support). The model can be understood and defended in front of the board.
What about student data protection?
We work within the GDPR framework with the data under the university's control, respecting consent and the authorized channel.