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Predict Absence. Automate Cover. Keep Schools Moving.

EduNoma is an AI-powered predictive staffing platform that analyses historical absence patterns to forecast future teacher absence and supply-cover demand.

From predicting staffing needs and preparing available supply teachers to AI-powered matching, automated candidate outreach and booking, EduNoma delivers an end-to-end automated supply-cover process, with the school retaining final approval.

Predict.Prepare.Match.Cover.
For SchoolsRegister your Interest For TeachersRegister as Teacher

Reporting that shows how cover is performing

EduNoma turns every request into data, so school leaders can see response times, forecast accuracy and demand trends in one place.

Average time to fill Minutes, last 10 weeks
Week 1Week 5Week 10
Time to fill
Forecast accuracy Last 8 weeks
94%Within forecast range
±2Average error, requests a week
7 of 8Weeks inside range
ImprovingTrend vs last quarter

Charts show sample data for illustration.

A first look at the screens

Early previews of three core screens. Layouts and data shown here are samples and will change before launch.

Absence reports by weekday Sample data
FewerMore

See when absences are reported through the week, so teams can plan for the busiest mornings.

Candidate for approval Awaiting you
JT
Jordan TaylorMathematics, KS3-4, 6 miles
Available
AM
Match score 91Subject fit, distance, past placements
ApproveDecline

One confirmed teacher at a time. The school always makes the final decision.

My availability

Repeats every week

Teachers update their week from a phone in seconds.

Built to keep schools moving

Predict absence early

Historical absence patterns are analysed to forecast teacher absence and supply-cover demand before it happens.

Match and contact automatically

AI-powered matching finds suitable supply teachers, and automated outreach does the chasing for you.

Schools keep final approval

Every booking is reviewed and approved by the school before it is confirmed.

From absence to booking

Four simple stages take cover from forecast to confirmed booking, and the school keeps final approval.

01

Predict

Forecast teacher absence and cover demand from historical patterns.

02

Prepare

Ready available supply teachers ahead of predicted peaks.

03

Match

AI matching and automated outreach find the right teacher.

04

Cover

The school approves and the booking is confirmed.