For English-medium schools · Class II–V
Vaaani finds the exact sound, spelling, or grammar link each child is missing — and gives your teacher the evidence to fix it. Runs on the computers you already have. No names, no accounts, and nothing a child does is ever sent to an outside AI.
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.
Every line the engine shows is computed, not generated. The numbers are its own estimates, drawn from what the child actually did — no ChatGPT, no large language model, anywhere in the teaching decision.
What lands on your desk
Not a score out of ten. For each child: the exact link that is missing, why it slipped past a good class teacher, and how the engine saw it anyway. This is the sheet your teacher works from on Monday morning.
| Child | What is missing | Why the teacher never saw it | How Vaaani sees it | What we do about it |
|---|---|---|---|---|
| Shuili | Spelling and pronunciation have come apartsound–spelling link not joined up | Corrected word by word, so it never showed up as a pattern | phonogram map — one sound, five spellings | Irregular-word set: photo · graph · laugh |
| Soumya | Past-tense endings “-ed”rule known, not yet automatic | The ending is subtle and the sentence still reads right | morphology — walked · played · wanted are three endings, one spelling | Contrast drills on the three endings |
| Siddhartha | “th” lands as a dental stop — “three” → “tree”home language · Bengali has no /θ/ | Inconsistent, and the meaning survives | L1 contrast edge — predicted before it appears | Minimal pairs: three/tree · path/pat |
| Md. Akram | Knows the rule, cannot reach it in timecorrect on paper — 7.2s, two tries | Read as hesitation, or as nerves | answer latency and retries, not just right or wrong | Short timed sets until it is automatic |
| Anirban | /z/ comes out as /dʒ/ — “zero” → “jero”home language · Bengali has no /z/ | Accepted as accent, so nobody teaches it | L1 contrast edge · zoo · busy · rose all inherit it | The /z/ set, then the words it unlocks |
| Saroj | Errors appear only when the task gets heavierholds up alone, breaks under load | Looked random, so it was put down to carelessness | self-repair and retry pattern across a session | Rebuild the weak link before adding load |
| Sinjini | Plural “-es” after /s, z, ʃ/has not met it often enough | Reading is fluent, so the gap stays hidden | rule-level evidence, not word-level | boxes · buses · dishes as one rule |
In any teaching decision. Every line above is computed from what the child did — no ChatGPT, no chatbot, no child’s work sent to an outside AI.
We test our own reasoning before we ask a school to trust it: does tracing the links between sounds, spellings and words actually predict what a learner gets right next? Run on the shipped engine, repeatable, with the results as they came out. Read the method →
Where the evidence honestly stands: the engine, its reasoning and its self-checks are built and reproducible — but Vaaani has not yet run a full classroom pilot. There is no school logo to show you and we are not going to invent one. Your school can be the first, on 30 students, free — and we will publish what the numbers say whichever way they fall.
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, free. 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: no classroom pilot has run yet — the first one is the offer on this page. 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 is still an estimate.
Thirty students, free, and a diagnostic sheet you can hand to a class teacher. We set it up personally.