AI agents that catch the student before the semester does
By the time a grade report shows a student failing, the intervention is late and expensive. We build agents that read the signals as they happen — attendance, submissions, LMS activity — adjust the path, alert the advisor, and draft the plan. Plus the enrollment and grading work nobody should be doing by hand.
roster.syncSIS1,840 students across 3 SIS feedseligibility.checkregistrarcredits, prereqs, holdsseat.assignLMS212 placements written backexception.routeregistrar6 conflicts sent to an advisor
Trusted by teams building and running mission-critical platforms
Why the term gets away from you
Education runs on a calendar, and that’s the problem. Almost everything expensive is a signal somebody saw too late.
Intervention arrives after the grade
The signals that predict a withdrawal appear in week three. The report that surfaces them appears in week nine, when the only remaining options are the costly ones.
Personalization is capped by headcount
An instructor can adapt for a handful of students. Nobody adapts for four hundred, so the median student gets the median course and the tails get nothing.
Enrollment runs on spreadsheets
Rosters, prereqs, holds, and seat assignments get reconciled by hand across the SIS, the LMS, and the registrar every term. It breaks every term, in the same places.
Procurement wants evidence, not features
Districts and institutions ask for outcome data, accessibility conformance, and a straight FERPA answer. An AI feature that can’t produce all three never clears review.
Design, build, and run — in your stack
We build to the academic calendar, so changes land between terms instead of halfway through one. One senior squad from the first workshop to something advisors open in week three.
Follow the signal, not the roadmap
We trace what your data already knows and when it knows it — which signals predict withdrawal, how early they appear, and who could have acted. That gap is the opportunity, and we size it.
Into the SIS and the LMS
We integrate through LTI, OneRoster, and the SIS feeds you already sync, and write back where staff already look. Agents surface the pattern and draft the intervention; a named human sends it.
Grounded in the course, not the internet
Your courseware, rubrics, prerequisite rules, and institutional policy become what the agent reasons over, so feedback matches what you actually teach and FERPA boundaries hold.
Measured on outcomes you report
We track intervention rate, time-to-contact, and the outcome metrics you already report upward. Those numbers are what your next procurement cycle will ask for.
Systems we work inside
Agents act in your systems of record. No parallel tool for your team to maintain.
- Canvas, Blackboard, and Moodle
- SIS platforms and registrar systems
- LTI 1.3 and OneRoster integrations
- Assessment and grading engines
- Content and courseware repositories
- xAPI and learning telemetry
What we ship in education
The agent does the reading and the drafting. A person still sends the message and gives the grade.
At-risk detection and intervention
Watches attendance, submissions, and LMS activity, flags the pattern in week three, drafts an intervention plan, and routes it to the advisor who owns that student.
LMS → SIS → advisor queue
Enrollment and eligibility verification
Syncs rosters across SIS feeds, checks credits, prereqs, and holds, writes seat assignments back, and sends only the genuine conflicts to a human advisor.
SIS → registrar → LMS
Grading and feedback at scale
Grades against the rubric, writes feedback specific to what the student actually submitted, and routes anything near a boundary to the instructor with its reasoning shown.
LMS → rubric → gradebook
Adaptive path adjustment
Reorders the next unit based on what a student has demonstrably mastered, not on where the cohort is, and tells the instructor what changed and why.
Assessment data → courseware → LMS
District and accreditation reporting
Assembles the term report from source systems, reconciles the discrepancies it finds, and cites the source of each figure so the submission survives review.
SIS → warehouse → report draft
We’ve done this already
Numbers from delivered engagements, not a marketing deck.
AI assistant deployed across every company in a PE-backed education roll-up
of employees reached
Consolidated from scattered systems across multiple acquisitions
knowledge layer
New companies onboard into the same layer instead of starting over
rebuilds per acquisition
Bring us one term of data
Whichever signal you wish you had acted on sooner — attendance, submissions, a stalled enrollment queue. In 45 minutes we’ll tell you how early an agent could have caught it and what it takes to ship before the next term.