How Boom AI Works

How Boom AI Works

The AI-native retention operating system that detects at-risk students, sends human-feeling interventions, and briefs navigators — before students disappear.

2
live campuses
~30,000
students in the pipeline
7
Claude agents running asynchronously, every day

A 7-agent pipeline, not a dashboard

Most retention software is a dashboard with sentiment analysis bolted on. Boom AI is something different: seven Claude managed agents that run a full pipeline across every student, every day. Each agent has one job, hands off to the next, and gets out of the way when a human takes over.

The pipeline runs asynchronously. By the time an navigator opens their inbox, the work is already done.

The science behind the pipeline

Boom AI is built on Vincent Tinto's Theory of Student Departure, the most empirically validated framework in higher-ed retention research. Tinto's central insight: students don't leave because they're failing. They leave because they haven't integrated. Academically, socially, or both.

Every agent in the Boom pipeline maps to that framework. The Tinto Classifier doesn't just flag that a student is at risk — it identifies what kind of risk, so the intervention matches the real cause. A student who's academically struggling needs a different conversation than a student who's academically fine but socially isolated.

Most retention tools treat "at-risk" as a single flag. Tinto told us forty years ago why that's wrong. Boom AI is the operationalization of that insight at scale.

AcademicIntegrationBehavioral SignalRisk DetectionInterventionSocialIntegrationSocial-EmotionalLife EventsInstitutionalCommitmentTinto ClassifierNavigator CopilotPersonalizedInterventionEach dimension maps to specific agentsthat read its signals and act accordingly.

Early outcome data

Boom AI is currently part of a quasi-experimental study running through summer 2026 across our partner campuses. The data below is preliminary, drawn from the in-progress cohort.

41%
reduction in summer melt
vs. control cohort
Preliminary results — study in progress. Final analysis will be published when the cohort closes in summer 2026.

Methodology: Quasi-experimental design across Community College pilots, summer 2026 cohort. Control = students on the standard summer outreach pathway; treatment = students in the Boom AI pipeline. Outcome measured: summer-to-fall persistence. Final results, including significance testing, will be published when the cohort closes.

MW
Matthew West, MA, MS.

Built by a school psychologist,
not a software engineer

Boom AI was founded by Matthew West, MA, MS., a licensed school psychologist with 14 years of clinical experience across LA campuses.

The agent prompts, the Tinto classifier categories, the language each intervention message uses — none of it came from a generic LLM playbook. It came from a decade and a half of sitting across from kids in crisis and figuring out what actually moves them.

Most edtech is built by engineers who consult clinicians. Boom AI is built by a clinician who learned to ship software.

  • MA, MS., School Psychology — Pepperdine University
  • Licensed School Psychologist — State of California
  • 14 years clinical experience across LA campuses

Want to see Boom AI on your campus?

Book a demo →
Or email matthew.west@boomaiapp.com directly.