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Enterprise AI for Education and Universities

Enterprise AI for Education and Universities

Universities run on manual processes designed for a fraction of today's application volumes and student populations. We build AI systems that handle admissions processing, student inquiries, and administrative work so your staff can spend time on the decisions that actually shape outcomes.

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Up to 67%
of student inquiries resolved without staff
Up to 55%
faster admissions review per application
3-6 wks
from kickoff to production pilot

What We See in Enterprise AI for Education and Universities

1

Admissions offices at R1 universities review 50,000 to 120,000 applications per cycle through Slate or PeopleSoft Admissions, with reviewer teams that didn't grow proportionally, so evaluations get compressed and scoring consistency across reviewers deteriorates measurably by March.

2

Student support centers handle 65 to 80% of ticket volume on questions about registration, financial aid deadlines, degree audits, and course requirements that have clear answers in Banner, Workday Student, or PeopleSoft that no student reads a PDF to find.

3

Faculty spend 8 to 14 hours a week on administrative tasks (grading rubric-based assignments, attendance tracking in Canvas or Blackboard, answering course-logistics email) that AI handles well, time that should be going to research, office hours, or curriculum work.

4

Institutional research teams manually compile data from the SIS, LMS, HR, and finance systems to produce reports that take weeks, arrive outdated, and still don't answer the question the provost actually asked, because joins across systems keep getting redone by hand.

How We Help

Admissions Processing AI

The agent reads applications in Slate or PeopleSoft Admissions, extracts structured data from transcripts and essays, checks eligibility criteria, and generates preliminary scores based on your rubric. Admissions officers review AI-scored applications with highlighted strengths, concerns, and flagged inconsistencies rather than reading 120,000 files cold. Scoring consistency across reviewers improves and files move through the pipeline at pace.

✓ 55% faster review per file and measurably higher inter-rater consistency

Student Services Intake and Support

AI answers student questions about financial aid, registration, degree requirements, campus services, and academic policies through chat, email, SMS, and voice. It pulls answers directly from your institutional knowledge base and live Banner or Workday Student data, so responses reflect the student's actual record. Complex or sensitive questions route to an advisor with full conversation context attached.

✓ 52% drop in routine support tickets and first-response time from 48 hours to under 4 minutes

Financial Aid Automation

The agent handles routine financial aid workflows (verification document intake, SAP appeal triage, award explanations, FAFSA correction walkthroughs), pulls the student's live record, and produces a response or a prepared case file for the aid officer. Officers work complex appeals and counseling rather than first-pass document review. Peak-season backlog collapses because the first pass gets done in minutes rather than days.

✓ Peak-season response times from 14 days to under 48 hours

Program and Curriculum Analysis

AI analyzes enrollment trends, course completion rates, labor market data, and student outcomes to surface insights about program health. Department chairs and the provost's office get quarterly reports showing growing programs, at-risk programs, curriculum gaps relative to employer demand, and specific course-sequence bottlenecks that data the IR team's manual reports never surfaced because assembling them by hand was too slow.

✓ Program review from annual to quarterly and at-risk programs identified 6-12 months earlier

Research Literature and Grant Assistant

AI agents ingest research papers, grant proposals, and institutional publications to help faculty find relevant prior work, identify funding opportunities matching their work, and draft literature review sections. The system searches the institutional repository plus PubMed, Web of Science, and Google Scholar simultaneously and returns structured summaries with citations.

✓ 42% less time on literature review and 2.5x more funding matches per quarter

Our Services for This Industry

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Engagement shape

Timeline

A typical higher-education engagement runs five to eight weeks to first production. Weeks one and two are discovery: sponsor alignment (provost's office, student services VP, or admissions dean), interviews with IT, FERPA compliance, and general counsel, plus a written integration pattern for the SIS, LMS, CRM, and any specialized systems in scope. We build an eval set in week two from 2,000 to 8,000 historical tickets, applications, or cases labeled by senior staff.

Weeks three and four are build. The agent runs daily against the eval set and we share a Friday scorecard. Weeks five and six cover shadow mode against a paired staff queue on live tickets or applications, plus FERPA and general counsel review sign-off. Weeks seven and eight are production cutover on one office or one student population with hypercare for 30 days. Expansion to additional offices follows the same pattern in parallel waves timed to academic calendar windows.

Cost model

Most education engagements fall between $80k and $200k for the first production use case. The main drivers are SIS integration depth (Banner, PeopleSoft, Workday Student each carry different integration timelines), number of offices or student populations in scope, and whether peak-season operations are in the pilot scope. A single-office student services pilot sits near the bottom of the range. A multi-office rollout across admissions, student services, and financial aid with full SIS write-back lands at the top. Ongoing platform and inference costs typically run $5k to $20k per month in production.

Frequently Asked Questions

How do you handle FERPA and state student privacy requirements?+
FERPA compliance is built into the system architecture from kickoff. Student data stays within your institution's infrastructure or approved cloud environment. We implement role-based access controls tied to your SSO, audit logging for every AI interaction with student records, and strict data minimization so the AI only accesses fields required for the specific task. For states with stricter requirements (California, Illinois), we layer in the additional controls. We produce data-flow documentation for your FERPA compliance officer and general counsel before any agent goes to production against real student data.
Can AI evaluate admissions essays fairly?+
The AI scores essays against your rubric criteria and flags specific areas of strength or concern with citations to the essay text. It does not make admit or deny decisions. Admissions officers make every final decision with the AI analysis as one input. We test for disparate impact across demographic groups on your historical data before deployment and monitor continuously in production. For institutions bound by specific state or federal guidance on AI in admissions, we produce the documentation required. Your admissions dean and general counsel sign off on the rubric and monitoring approach before the agent scores a single essay.
How does the student support AI stay current with policy changes?+
The agent connects to your official policy documents, course catalogs, and academic calendars as the source of truth. When your office updates a document, the knowledge base updates automatically. For sensitive topics like financial aid eligibility, Title IX, or academic probation, we build review workflows so your staff approve AI-drafted answers before they go live to students. The system maintains a version history so if a policy changes mid-semester you can see exactly what students were told under the old policy and who needs follow-up.
What integrations do you support with Banner, PeopleSoft, Workday Student, Canvas, and Slate?+
We integrate with Banner via Ethos and the Banner Web Services API, PeopleSoft via Integration Broker, and Workday Student via the Workday REST API and reports-as-a-service. For LMS we connect to Canvas via LTI and the Canvas API and Blackboard via their REST surface. For admissions we integrate with Slate (Technolutions) through the Slate API. Integration scope is defined during discovery. We've worked inside institutions at every combination of these systems and we tell you during scoping where your specific versions or configuration create constraints.
What does a pilot cost and how long does it take?+
A focused pilot on one use case (student services for one unit, admissions scoring for one program, or financial aid intake for peak season) runs 5 to 8 weeks from kickoff to production. Pricing typically lands between $80k and $180k depending on SIS integration depth, number of offices in scope, and whether the pilot covers peak-season operations. A full rollout across admissions, student support, financial aid, and faculty tools runs 4 to 7 months in parallel waves. We quote a fixed SOW before kickoff so the CIO, provost's office, and CFO all see the same number.
What data stays on our infrastructure vs. with the AI vendor?+
Student records, application content, essay text, financial aid data, and research data stay inside your institution's tenant. We deploy the application layer in your Azure, AWS, or on-prem environment and run inference against models hosted in your own account with zero retention and zero training on your prompts. No student PII, application content, or aid data transits a public AI API. For public research literature and general knowledge the agent uses ordinary academic APIs under your existing subscriptions. Full egress map goes to your CIO and CISO before go-live.
Who's accountable when the AI scores an application wrong or gives a student the wrong policy answer?+
The admissions officer, student services advisor, or financial aid counselor remains accountable for the decision. Our agents surface recommendations and first-pass responses with the supporting data and citations, and route low-confidence or sensitive cases to a human with full context attached. For admissions, officers make every admit/deny call. For financial aid, counselors approve every final determination. For student support, the system never commits policy changes unilaterally. The MSA spells out liability allocation and we carry appropriate E&O coverage for higher-education deployments.
How is this different from Ellucian AI, Salesforce Education Cloud AI, or a big consulting firm, and how do we measure ROI?+
Platform vendors ship general-purpose product features. Big consulting firms deliver a 12-month transformation roadmap. We tune agents to your specific academic policies, aid packaging rules, admissions rubrics, and SIS configuration, and we deliver running code in weeks. ROI is measured against a baseline captured in discovery: application review time, student response time, aid processing backlog, staff hours per request, student satisfaction metrics. Most institutional deployments see payback inside one academic year on loaded staff cost, with separate impact on enrollment yield, retention, and student experience metrics the strategic plan actually cares about.

Let's build your AI system.

Production-grade AI for Enterprise AI for Education and Universities. We deploy in weeks, not quarters.

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