01
Child-safe accounts
Parent registration with consent, child profiles, scoped learner tokens and a parent PIN — no child email or phone number is collected.
Curriculum-grounded AI teacher for school students
Voice-First AI Tutor
A personal AI teacher that speaks and listens, teaches from the student’s own textbooks, draws on a whiteboard and tracks what they have mastered.
AT A GLANCEEDUCATION
- Apps
- Student PWA, parent dashboard, admin console
- Teaching
- Live bilingual voice, or streamed text
- Grounding
- Hybrid vector and full-text retrieval with citations
- Built with
- Next.js, Fastify, PostgreSQL + pgvector
11 MODULESBUILT AND SHIPPED
The problem
What it replaces
General-purpose chatbots hand children answers, invent facts that are not in their syllabus and give parents no idea what was learned. Schools and tutoring businesses want AI help that stays inside the curriculum, teaches rather than tells, and is safe for children.
This tutor ingests and publishes curriculum through a reviewed pipeline, retrieves from it with citations, and teaches in live voice lessons or streamed text — Socratically, with schema-checked visuals, mastery tracking and a parent dashboard. The model only requests actions; the server decides what happens.
Who uses it
Students
One-tap voice or text lessons on a phone or tablet, handed over by a parent with an optional PIN.
Parents
Register with consent, create child profiles and follow progress on a dashboard.
Curriculum admins
Upload or fetch textbooks, review extracted structure, and publish.
Features
What the system does
02
Curriculum as data
Board, class, subject and chapter are records, not code, with admin authoring and an audit log.
03
Reviewed ingestion
Upload or fetch from an allow-list, extract page by page, detect structure, then edit, approve and publish behind a gate.
04
Grounded retrieval
Structure-aware chunks, vector and full-text search fused and reranked by curriculum proximity, with citations and a grounded flag on every answer.
05
Live voice lessons
Real-time voice with barge-in and reconnection, falling back to speech-to-text, model and text-to-speech when needed.
06
Teaches, doesn’t tell
Layered prompts drive a Socratic teaching style; the model asks for actions and the server carries them out.
07
Safe visuals
Thirteen schema-validated visual types, such as step-by-step equations, rendered by the app — the model never supplies HTML or SVG.
08
Animated teacher
An animated mascot with expressive states and lip-sync to the voice.
09
Mastery tracking
Knowledge tracing with retention decay and misconception clustering, plus hints, generated questions and practice sets.
10
Parent dashboard
Progress written constructively, a rule the test suite enforces.
11
Security by design
A route-by-role authorisation matrix, prompt-injection tests and a data-retention job.
Architecture
How the pieces fit together
A monorepo that keeps the real-time voice gateway separate from the web app, and keeps the AI model on the server side of a strict tool boundary.
Apps
- Student PWANext.js · audio worklets
- Parent dashboardNext.js
- Curriculum consoleNext.js
Platform
- Tutor APIFastify
- Voice gatewayWebSocket
- Teaching enginetool boundary
- Retrievalhybrid + rerank
- Ingestion workerjob queue
Data
- PostgreSQL 16pgvector + full-text
- Job queuein Postgres
Connected services
- AI modelsteaching, live voice, speech, embeddings
- PublishTextbooks are ingested, reviewed and published as structured curriculum.
- AskA student speaks or types; the server retrieves the relevant pages.
- TeachThe tutor guides them with questions and visuals, citing the book.
- TrackMastery updates per concept and the parent sees progress.
Built with
- TypeScript
- Next.js
- Fastify
- PostgreSQL
- pgvector
- Drizzle
- Zod
- Vitest
Built before, fitted to you
Start from an architecture that has already shipped
We have delivered this kind of system, so yours starts from a design that is known to work, not a blank page. Your workflow, your terminology and your approval chain shape what we build on top of it.
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