Vision
For the first time, scale and personalization can happen together.
Scaling education used to mean standardizing it. AI breaks that trade-off — and that reorganizes not a software category, but how every student learns.
i · Who decides where learning goes
Bus
Traditional school
Fixed route, time and group pace. Fall off the group’s speed and you’re left behind.
Carpool
Human tutor
More flexible, but bound by price, time and teacher supply — never everyone’s infrastructure.
Private car
Personal AI tutor
The learner sets the destination and leaves anytime; the system adapts the route to them.
ii · How much the system drives — our roadmap
Phase 1 · Manual
Today’s general AI
Powerful — but you judge what you don’t know and what to do next.
Phase 2 · Automatic
Decides within a frame
The system chooses inside a human-designed teaching framework.
Phase 3 · Self-driving
Long-term betFinds new methods
Our end-state bet: the system discovers teaching combinations no human wrote down. Phases 1–2 are what we’re building now.
iii · Why equity is built into the cost structure
Not a slogan — it comes from the cost structure. Lower marginal cost turns people who could never afford a tutor into users, so equity makes the market bigger.
Cost falls
More students reachable
More learning evidence
Better adaptation
iv · The market, bottom-up
Mission
The help a student gets shouldn’t depend on income, postcode, or luck with a teacher.
We don’t promise to erase every gap — only to lower how much good teaching depends on family income, region and teacher supply.