For US Universities & K-12 Schools

Enterprise AI that turns student data into action.

US universities and school districts have been collecting student data for decades. Vast majority still can't act on it automatically — because the AI layer between their systems was never built. Justo builds it.

Education Banner
Education Intro 1

Built by enterprise AI engineers

Education Intro 2

Works alongside your existing SIS & LMS

Education Intro 3

FERPA Compliant

Education Intro 4

24/7 Global Support

Education Intro 1

Built by enterprise AI engineers

Education Intro 3

FERPA Compliant

Education Intro 2

Works alongside your existing SIS & LMS

Education Intro 4

24/7 Global Support

The AI Reality Gap

Your institution collected the data. Your systems never learned from it. 

US universities and school districts have spent years collecting student data across disconnected systems. The gap between data collected and intelligence acted upon remains the defining failure of institutional technology.

78% Have an AI strategy. Almost none are executing it.

Most institutions completed AI roadmaps in 2023–24. In 2026, the vast majority have not shipped a single AI tool to students or faculty.

EDUCAUSE 2025 Higher Education Technology Report

18 Separate platforms per institution on average

SIS, LMS, advising tools, early-alert systems — all disconnected. AI can’t reason across data it cannot see.

Inside Higher Ed IT Benchmark Survey 2025

89% Say their current vendor cannot deliver real AI

Only GPT wrappers bolted onto existing interfaces. Real institutional intelligence requires training on your data — your students’ actual patterns.

Gartner Higher Education IT Survey 2025

$2.3M Lost annually to preventable student attrition

Average annual tuition revenue lost to preventable dropout at a 12,000-student institution. The interventions exist. The AI to trigger them doesn’t.

NACUBO 2025 Retention Benchmarks

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The Challenge Universities and schools Face

Students are already using AI. Institution isn't keeping up.

Students use AI weekly

Students use AI weekly

Only 11% of those tools are approved by their institution

Seperate platforms

Students use AI weekly

separate platforms per institution on average

Background-University

Wasted siloed SIS + LMS

Students use AI weekly

That can't feed an AI — so the data sits, useless

Universities have no AI strategy

Students use AI weekly

Despite mandating AI literacy in 2026 curriculam

How Justo Works

Your data never leaves your infrastructure · 100% open source · FERPA · COPPA

Your LMS

Courses & activity data

Academic records

Grades & student history

Your infrastructure · your governance

Intelligent SIS

Custom-built for you

LMS × SIS Bridge

Real-time intelligence

AI Learning Assistant

Trained on your content

Institutional Data Platform

Foundation layer · all data governed here · models run inside your environment

What your people receive

Students

24/7 AI answers

Advisors

Early alerts & briefs

Faculty

AI teaching tools

Meet yourAI agents

They don't wait to be asked.

Currently done manually: Advisor pulls a weekly SIS report, sorts by GPA, cross-checks LMS logs — 45 minutes every Monday. For every student.

Watches LMS engagement, SIS grades, and attendance simultaneously. When risk signals align, it generates the advisor's brief and flags the student — automatically, every day.

Universities · K-12

Currently done manually: Advisor checks each student's transcript against degree requirements before registration. At 380 advisees, most get checked once a year — if they ask.

Audits every student's credit completion against graduation requirements every week. Flags shortfalls the semester they can still be fixed — not after the window has closed.

Universities · K-12

Currently done manually: Counselor reviews daily attendance sheets, identifies students near the 10-day threshold, then calls home — often too late for intervention to change the outcome.

Detects developing absenteeism at Day 3, not Day 10. Sends the counselor a specific alert with the student's full context — not a list of 47 absent students with no priority.

K-12 Schools

Currently done manually: FA staff export a list of incomplete applications weekly, draft individual follow-up emails, and manually track who responded — repeated across hundreds of students each cycle.

Monitors every student's FA application status in real time. Sends personalised deadline reminders and flags missing documents. Staff only get involved when a decision needs judgment.

Universities

Currently done manually: Special education coordinator emails each teacher each semester to confirm accommodations are in place — and hopes everyone replies before the audit.

Continuously monitors whether IEP and 504 accommodations are being delivered across every class. Flags missed implementations to the coordinator before they become compliance violations.

K-12 Schools

Currently done manually: Admissions staff open each application, check document completeness, note what's missing, and send individual follow-up emails — for every applicant, every cycle.

Verifies every submitted document the moment it arrives. Sends personalised follow-up requests for missing items automatically. Staff review only the escalations requiring judgment.

Universities

Your Transformation Journey: Transparent & Proven

curve curve curve curve

Data Audit & SIS Mapping

Catalogue all data sources, assess quality & completeness

Knowledge Architecture Design

RAG indexes, embedding strategy, context windows

Model Training & Fine-tuning

No data leaves your walls — on-prem or private cloud

Agent Configuration (MCPs)

Define what your AI can DO, not just answer

Deploy, Monitor, Iterate

Continuous retraining as your institution evolves

FAQ

How do you ensure FERPA compliance?

FERPA compliance is built into our data architecture from day one — not added as a layer after the fact. Student records are stored in your infrastructure, not ours. Our AI models are trained exclusively on anonymised institutional patterns and non-PII content: course catalogs, policy documents, curriculum structure, and aggregate academic patterns. Student PII never enters model training under any circumstance. We operate as a service provider under your institution's existing FERPA data governance agreements. For K-12 institutions with students under 13, we additionally build COPPA-compliant data collection and parental consent workflows into every system we deploy.

What is your approach to legacy system integration?

We don't require you to replace anything on a timeline that doesn't suit you. Our AI intelligence layer connects to your existing systems via API — adding intelligence without touching your operational databases. Where you want to augment your existing SIS, we build the connectors and the AI layer on top. Where you eventually want to replace it with our custom-built Intelligent SIS, we handle the full data migration with zero record loss. We've built integrations for every major student information system and learning management platform used across US higher education and K-12. If your specific system isn't on our standard list, we build a custom connector in Phase 2.

How long does a typical implementation take?

You see a working prototype running on your own institutional data within 6 weeks of kickoff — before any full deployment contract is signed. Full production deployment runs 4–6 months for universities and 3–4 months for K-12 districts. We phase the rollout by department, starting with the highest-ROI use case first. K-12 district deployments run faster because there are generally fewer stakeholder approval layers in the procurement process.

Do you offer ongoing support and maintenance?

Yes. Your AI models improve automatically every semester as new institutional data is ingested. We run quarterly model tuning reviews and provide monthly performance reports. All model maintenance, security updates, and capability upgrades are handled by Justo. Your team's post-deployment commitment is one 2-hour quarterly review meeting. We maintain 24/7 monitoring with US-timezone engineers — not an offshore ticket queue.

What if we have limited IT staff?

We designed our engagement model for institutions with lean IT teams — including single-person IT departments in smaller school districts. Phase 1 requires 2–3 hours per week from one IT Director and one data access approval. We don't require you to hire AI engineers or data scientists at any point. Justo supplies the full technical capability. Post-deployment, your ongoing commitment is one quarterly review meeting.

How do you handle data migration?

We run a full data audit in Phase 1, mapping every format, schema, and historical record. We build migration pipelines that run parallel to your live systems — operations continue without interruption. Every migrated record is validated against the original source before the old system is decommissioned. Student historical data going back as far as your records exist is preserved and queryable in the new system.

Can you work with our existing infrastructure and cloud provider?

Yes. Justo is infrastructure-agnostic. We deploy on your existing environment — your own data centre, your government cloud account, or a private cloud you control. Our open-source foundation means no proprietary dependencies that lock you to any specific infrastructure. If your institution has a pre-negotiated enterprise cloud agreement, we deploy within that agreement.

What makes your AI different from what our current vendor offers?

Three specific differences. First: our AI is trained on your institution's data — not generic benchmarks. A risk model trained on your dropout history is more accurate for your students than one trained on aggregated national data. Second: we deploy agentic AI — not just chatbots. Our agents observe, decide, and act across systems without waiting for a human to initiate. Third: you own everything. The models, the infrastructure, the source code — all open-source, running in your environment. When the engagement ends, nothing is decommissioned.

How do you handle COPPA compliance for K-12 students under 13?

COPPA compliance is built into our K-12 architecture at the data collection layer. We implement strict data minimisation — collecting only what is required for educational purposes. Parental consent workflows are built into student onboarding for any data involving students under 13. No student under 13 data is used in model training. We maintain data retention schedules and deletion workflows compliant with both COPPA and applicable state student privacy laws.

How does pricing work for K-12 districts versus universities?

University engagements are project-based — typically $350,000–$550,000 depending on scope. K-12 district engagements are scoped separately and priced proportionally — a single-school deployment is a meaningfully different investment from a district-wide rollout. We don't use per-seat billing in either market. Founding partner pricing is locked for 36 months from contract signing, written as a contract clause. We provide scope-specific estimates free of charge as part of the AI Readiness Assessment.

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We respond within 2 business days — a working session, not a sales call.

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