Let teachers spend their time teaching, not marking.
MNT is not an “AI question-bank vendor”.
We build a three-layer AI teaching system for Hong Kong tutorial institutions and independent tutors: from diagnosing what a student actually gets wrong, to delivering it to every student at low cost, to breaking the improvement plan into tasks they can finish daily. Delivered as progressive service packages — start with a small pilot, upgrade tier by tier as results prove out.
Three problems
If you run a tutorial business or teach independently, these should look familiar.
Inefficient paper drilling
A student finishes a full past paper. Whether the errors are vocabulary, comprehension or answering strategy still comes down to a tutor judging each script by eye. It does not scale.
Teaching capacity ceiling
There is a hard limit to how many students one tutor can follow closely. Growth means more headcount and more cost, with margin thinning every time.
Plans that never execute
Once weaknesses are diagnosed, students have packed schedules and fragmented time. The improvement plan stays on paper, with nobody following through.
Three layers that interlock into a teaching loop that keeps running.
Each layer answers one of the problems above. Any single layer helps; the three together become a system.
MNT Standard · Subject Knowledge Engineering
Turns raw exam papers into knowledge assets, so you can pinpoint what a student cannot do and why.
WhatsApp AI Teaching Assistant
Delivers practice, feedback and progress through the WhatsApp people in Hong Kong already use daily. Zero install.
SLOT Learning Execution System
Breaks the improvement plan into 5-to-45-minute tasks that fit a student’s fragmented time, so it actually gets done.
Three rules
These are not marketing lines. They are hard rules already written into the code, and institutions can read them as procurement terms.
The same script, marked a hundred times, gives the same result.
Model answers come from official marking schemes — never generated on the spot.
When it cannot mark with confidence, it returns “undetermined” — never charged to the student.
Anchored to: EDB curriculum guides · HKEAA marking schemes. Every reference traceable to the official source.
Service tiers
From HK$38,000, upgrading tier by tier. We understand how educational institutions buy: prove it works, then commit more. So the system breaks into five independently purchasable packages — start with low-barrier AI teaching diagnostics, validate over a 6–8 week pilot, then step up along the loop. No six-figure commitment up front.
Reviews your current teaching workflow and identifies which parts suit AI, and what the expected return is.
Workflow design, question delivery and answer collection, teacher console, plus a 6–8 week pilot and results report.
Turns your strongest subject’s papers and teaching experience into a proprietary knowledge asset.
Question bank + diagnosis + WhatsApp + SLOT in one: the full loop running end-to-end on a single subject.
Multi-subject deployment, teacher back office, operational support and full implementation for chains and groups.
Why us
cannot build a formal marking engine.
do not understand Hong Kong’s curriculum standards and marking schemes.
This work needs both capabilities at once — and that intersection is exactly where twenty years of cross-market practice lands.
A glimpse of MNT Lab
Bring a past paper. We’ll show you on the spot.
Bring one of your past papers or a real class situation. We will demonstrate live how it becomes a diagnosable knowledge asset, and map an entry tier for you.