Applied AI that catches the student before the semester does.

• 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 options left 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.

• 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 an advisor.

  SIS → registrar → LMS

• Grading and feedback at scale

  Grades against the rubric, writes feedback specific to what the student 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

• Agentic workflows

  Enrollment reconciled across SIS, LMS, and registrar every term without the spreadsheet.

  Multi-step processes run end to end. People approve the exceptions instead of doing every step.

• Reasoning systems

  Rubrics and prerequisite rules applied consistently, with the reasoning shown to the instructor.

  Models work through policy, rules, and edge cases, and show how they reached the answer.

• Multimodal AI

  Essays, worksheets, and scanned work read and graded against what you actually teach.

  Documents, images, and forms become structured data without anyone keying them in.

• MCP & agent interoperability

  Scoped access to student records that keeps FERPA boundaries in the architecture.

  Your software gains a second kind of user: agents, with scoped and auditable access to act.

1. 01Advise

   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.

2. 02Engineer

   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, grounded in your courseware, rubrics, and prerequisite rules.

3. 03Operate

   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.

Other industries

• Healthcare
• Financial Services
• Field Service
• Private Equity