Diagnostic language engine · grade by grade
Vaaani builds a living picture of every child — what they know, what they’re ready for, what’s slipping away — and picks each lesson from it, with a reason it can show you. Built out from the child’s home language. Not a chatbot. A glass box.
Two things she already knows, but hasn’t joined up yet. Fixing this one link also makes photo, graph, laugh easier.
Just the right challenge — about 68% likely to get it, with room to learn.
Why schools choose us
Most classroom AI is a large language model in a wrapper — helpful, but you cannot say why it taught what it taught. Vaaani was built the other way round.
Inside the glass box
No large language model anywhere in the teaching decision. Just an inspectable, estimated state and explicit reasoning — the kind a linguist, not a chatbot, would use.
A living picture of what your child knows — it updates with every answer, and fades gently over time, the way real memory does. Every lesson is picked for your child alone, so no two children follow the same path.
Words are learned in a web, not one at a time. Master one link and the words next to it get easier — learn the /f/ in “phone” and photo, graph, laugh come along for the ride.
When your child gets something wrong, it works out why — the spelling isn’t linked to the sound yet, not enough practice, or a habit carried over from the home language — then fixes that exact thing.
Before each lesson it predicts how your child will do, then checks itself against what actually happened. A teacher honest enough to grade its own guesses.
Knowing a rule and using it in the moment are not the same thing. This watches how the answer arrives — fast and sure, or slow and second-guessed — and tells effortful recall apart from real command. A correct answer that took eight seconds and two tries isn’t mastered yet, and it says so, so a child is never marked “done” on a skill they can only manage on paper.
The unfair advantage
An Indian child learning English is not a blank slate — they already own an isomorphic web in Hindi, Bangla, Tamil. Vaaani grafts that head start onto the English graph, and reads L1 into why a sound is hard.
A Bengali speaker saying “jero” for “zero” isn’t making a mistake — Bengali has no /z/. The engine knows that, and teaches from it.
Run a pilot
We start small and honest — a single cohort, real children, measured results you can read line by line.
One class or centre. Set the home languages. No installation — it runs in the browser, on a phone or a lab machine.
Each child works through short lessons. The picture of what they know fills in; every choice records why it was made and how likely it was to land.
You see what each child knows, why they slipped, and how well the engine’s own guesses held up — not a mystery score.
Where we honestly stand: a first small pilot (7 children) is complete; the engine’s starting numbers come from published research and get sharper as real pupils use it. We show what we’ve actually measured — and flag anything that’s still an estimate.
Give one cohort an engine that knows each child — and can prove it. We’ll set up your pilot personally.