Education
AI-powered learning and operations that improve outcomes, retention, and institutional efficiency
You get AI agents for adaptive learning, student risk analytics, assessment automation, and administrative workflow optimisation. They run on your cloud, connect to your existing SIS and LMS, and produce insights your academic leaders can trust.
Built on your AWS, Azure or GCP tenant. FERPA and GDPR compliant. Regulator-ready.
Proof of concept
To measurable ROI
AWS, Azure or GCP
Student expectations have changed. Your operating model has not. AI closes the gap.
Students expect personalised, responsive learning experiences. Regulators demand detailed outcome reporting. Budgets are flat or shrinking. Your faculty spend hours on administrative tasks that AI can handle in seconds. Meanwhile, retention rates remain the single largest financial risk for most institutions.
We build AI agents that personalise learning, predict at-risk students, automate assessment, and streamline operations with governance that satisfies your data protection obligations and accreditation requirements.
improvement in student retention rates with AI-powered early intervention (McKinsey, 2024)
global higher education AI market projected by 2027 (BCG X, 2024)
reduction in administrative workload achievable through AI automation (Deloitte Insights)
of students prefer AI-personalised learning paths over one-size-fits-all curricula (McKinsey QuantumBlack)
From student data to institutional action, in one workflow
We connect your student information system, learning management platform, assessment tools, and administrative systems to AI agents that personalise learning, predict retention risk, automate grading, and generate the compliance reporting your accreditors require.
SIS, LMS, assessment and admissions data
- Approval gates
- Human-in-loop
- Audit logging
AWS, Azure or GCP
- Student insights
- Compliance reports
- Operational dashboards
What we deliver for education institutions
From adaptive learning to institutional analytics, governed, auditable, enterprise-ready
Adaptive learning and personalised pathways
Adjust content and pace to each student's mastery level.
ROI
Process
Assess prior knowledge
Diagnostic assessments and LMS interaction data used to map each student's starting competencies and learning gaps
Personalise pathways
AI generates individualised learning sequences, recommending content, activities, and assessments matched to student level
Adapt continuously
Models update recommendations in real time based on assessment performance and engagement signals
Sources
Based on published findings: McKinsey, 'How AI Can Accelerate Students' Holistic Development', 2024; BCG X, 'AI in Education' — reproduced for illustrative purposes
Student retention and risk prediction
Identify at-risk students weeks before traditional signals.
ROI
Process
Aggregate engagement data
LMS logins, assignment submissions, attendance, financial aid status, and academic performance consolidated from all systems
Score retention risk
ML models assign risk scores to every enrolled student, updated weekly, with factor attribution
Trigger interventions
At-risk alerts routed to advisors, tutors, and student support teams with recommended actions and context
Sources
Based on published findings: McKinsey QuantumBlack, 'Predictive Analytics in Higher Education', 2024; Deloitte Insights, 'AI and Student Success' — reproduced for illustrative purposes
AI-powered assessment and feedback
Grade and return detailed feedback in minutes, not weeks.
ROI
Process
Receive submissions
Student work submitted through LMS, automatically routed to AI assessment pipelines by assignment type
Grade and evaluate
NLP and ML models score work against rubrics, checking structure, argumentation, accuracy, and originality
Deliver feedback
Personalised, constructive feedback returned to students with specific improvement suggestions and rubric mapping
Sources
Based on published findings: BCG X, 'AI Assessment in Education', 2024; McKinsey, 'Technology-Enhanced Learning' — reproduced for illustrative purposes
Admissions and enrolment optimisation
Predict yield and optimise financial aid allocation.
ROI
Process
Analyse applicant data
Academic records, demographic data, engagement signals, and historical enrolment patterns aggregated from admissions systems
Predict yield and success
ML models forecast which admitted students will enrol and which will persist through graduation
Optimise offers
Financial aid and scholarship allocation recommendations that maximise enrolment quality within budget constraints
Sources
Based on published findings: Deloitte Insights, 'AI in Higher Education Admissions', 2024; McKinsey, 'Data-Driven Enrolment Management' — reproduced for illustrative purposes
Curriculum analytics and programme design
Find which programmes deliver value and which need redesign.
ROI
Process
Collect outcome data
Course grades, completion rates, student evaluations, and graduate employment data consolidated across programmes
Analyse effectiveness
AI identifies which courses and sequences predict strong outcomes and which correlate with attrition or poor results
Recommend changes
Programme redesign recommendations with projected outcome impact, supported by evidence from your own institutional data
Sources
Based on published findings: McKinsey, 'Skills-Based Higher Education', 2024; BCG X, 'Future of University Education' — reproduced for illustrative purposes
Administrative workflow automation
Automate scheduling, timetabling, and procurement tasks.
ROI
Process
Map administrative processes
Existing workflows for scheduling, procurement, HR, and reporting analysed for automation potential
Deploy AI agents
Automated agents handle timetable generation, room optimisation, purchase approvals, and regulatory reporting
Monitor and improve
Continuous performance tracking with exception handling routed to appropriate staff
Sources
Based on published findings: Deloitte Insights, 'Automation in Higher Education', 2024; McKinsey, 'Operational Excellence in Education' — reproduced for illustrative purposes
Compliance and accreditation reporting
Generate accreditation and regulatory returns automatically.
ROI
Process
Map reporting requirements
OfS, QAA, HESA, and accreditation body data requirements mapped to your institutional data sources
Extract and validate
Student, financial, and outcome data extracted, cross-referenced, and validated against regulatory definitions
Produce submissions
Regulatory returns and accreditation reports generated in required formats with full audit trails
Sources
Based on published findings: BCG X, 'Higher Education Operating Models', 2024; Deloitte Insights, 'Regulatory Technology in Education' — reproduced for illustrative purposes
VP Open Learning, MIT
Sanjay Sarma
"We are using machine learning on AWS to analyse how students engage with online course content at a scale that would be impossible for any human teaching team. The models identify which students are likely to fall behind weeks before traditional assessments would flag a problem, and we are able to intervene with personalised support."
Originally published: AWS Customer Stories — MIT — reproduced for illustrative purposes
How we work in education environments
Education AI is only valuable if it respects student privacy, integrates with your existing platforms, and produces outputs that academic leaders and accreditors trust. We start by mapping your SIS, LMS, and administrative systems, then identify the one use case where AI will create the most immediate, measurable impact on student outcomes or institutional efficiency.
Everything we build runs on your cloud tenant. Your student data, your assessment records, your regulatory submissions. None of it leaves your environment. Your DPO, accreditation body, and governing board can audit the system directly.
01
Data and use case mapping
We assess your SIS, LMS, and administrative systems, identify the highest-value AI use case, and map it to your data protection and accreditation obligations.
02
30-day proof of concept
A working AI agent on your cloud, connected to your institutional data, with demonstrable output in 30 days.
03
Governance by design
FERPA and GDPR compliance, audit trails, and explainable outputs built in from the start. Ready for DPO review and accreditation inspection.
04
Scale in 12-18 weeks
From one validated workflow to measurable improvements in student outcomes, retention, or operational efficiency across your institution.
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Frequently Asked Questions
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