Enrolment data arrives too late to act on
Applications, deposits, aid and registration live in separate systems. The full picture assembles after the point where intervention was possible.
Public sector
The number of school leavers has peaked. From here it falls until 2041.
Enrolment is holding up today. The intake pool is not. Institutions that cannot see enrolment, retention and cost in one place will decide blind.
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3.4M
projected US high school graduates in 2041, down from a record 3.9 million in 2025.
What is happening
Numbers are falling, and the figures that would explain it sit in four systems.
Retention signals exist but are not connected
Attendance, LMS activity, financial holds and advising notes each predict risk. Separately, none of them trigger anything.
Budget pressure is immediate and durable
Districts and institutions face inflation, a shrinking intake pool and unpredictable funding, while obligations in special education and support services grow.
Administrative load lands on teaching staff
Which is how tighter budgets become teacher burnout, and burnout becomes another cost.
Intelligence, plumbed in
An adviser's note predicts a withdrawal. Nothing was reading it.
We build the retrieval and the semantic layer over four student systems, and we score it against last year's real outcomes.
How we make AI survive real data-
Connectors -
A semantic layer -
Evals you can check
What changes
Judge us on this, not on what we built.
Earlier Enrolment funnel visible while it can still be influenced
Applications through deposit, aid and registration in one view, by segment and by source.
Retention At-risk students identified from signals you already collect
Attendance, engagement, financial holds and advising history combined, with a route to action rather than a report.
Hours back Administrative load off teaching staff
Reporting, compliance returns and data entry automated where the data already exists elsewhere.
Research
We wrote a full page on each of these, with the sources.
Each one carries its own figures and the citations behind them. Start with whichever sounds most like your week.
However hard, whatever it is
Enrolment is one example. Bring whatever the board is asking about.
We wrote this about seeing the intake pool. If yours is timetabling, estates or a returns deadline nobody owns, the approach is identical.
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01 We sit with you
Days where the work happens, not a workshop in a meeting room. We watch the job get done and write down the shortcuts nobody wrote down.
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02 We read everything
Your data, your rules, your vendors and their documentation, and the published research on your sector. We report what is actually in there.
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03 We break it to first principles
Not which tool fixes this. What is actually causing it, taken apart until we reach the piece that cannot be divided further.
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04 Then we build
Weeks, not quarters. By this point we are not guessing what to build, and guessing is the thing that makes projects long.
Where we sit
Your student system stays. We build the view that shows who is about to leave.
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What you get
- Earlier Enrolment funnel visible while it can still be influenced
- Retention At-risk students identified from signals you already collect
- Hours back Administrative load off teaching staff
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Built new for you — none of this exists in your stack today
- An early-warning list a tutor gets on Monday morning
- One form that replaces five for the same student
- A screen for the department office showing the week ahead
2
The student-signal layer Reads your student systems. Writes nothing into a student record.
Connectors, one agreed meaning per field, and a model reading what no
field holds. Accuracy measured on your own records.
1
- Banner
- PeopleSoft
- Workday Student
- PowerSchool
- Canvas
What you already run — unchanged, and still yours
If the student system holds it, we can reach it. A nightly extract, a form the registry emails, a student-system screen nobody is allowed to change. All of it can be used.
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No API -
No documentation -
A terminal from 1994 -
It arrives as paper -
The vendor said no -
It reports nothing
Not a list of limits. Name yours on the call.
And once we can reach it, a model can read it. Most of the value here is in the sources nobody ever structured — the note, the letter, the screen.
Who this is for
The people who feel this first
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VP of Enrolment Management -
Chief Information Officer -
Registrar -
Director of Institutional Research -
District Superintendent
Straight answers
The questions you would ask on the call
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We have a student information system. Why is that not enough?
An SIS records status. To act on enrolment and retention you need admissions, financial aid, the LMS and advising in one view. Today they sit in four systems, with different IDs and different update times.
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Is student data safe?
Handled under FERPA constraints with role-scoped access and per-record logging. Predictive signals are designed to route to a human who can act, never to make a decision about a student automatically.
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Can you work with a small district or institution?
Yes. That is why we start small. A short paid look at your data lets a smaller district find out if this is worth doing before committing to anything bigger.
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Can AI help without touching student records?
It reads, it never writes. The early-warning list comes from notes and activity you already hold, scored against last year's real outcomes.
Also in public sector
Next step
Start with your own numbers.
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We talk
20 minutes. Free.
You tell us what is not working. We ask how the work really gets done.
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We look at your data
A few weeks.
We read your systems, including the notes and letters no field holds. You get what is really in there, what it costs, and the accuracy we can hit.
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We build
A few months.
Only if step 2 says it is worth it. Fixed price, agreed before we start.