Math Solver AI Tutor is an intelligent tutor for university mathematics. It guides students with a Socratic method that asks questions and gives hints instead of final answers. The product ships with a modern Next.js TypeScript front end, an Express API, PostgreSQL via Prisma, Redis for cache and sessions, KaTeX for LaTeX rendering, and Algebrite plus Math.js for symbolic verification. Status is production ready with a complete MVP, optimizations, and comprehensive testing.

Help students truly learn mathematics by thinking step by step. Replace answer dumps with guided reasoning that adapts to the learner, verifies calculations, and builds durable problem solving skills.

Balancing guidance with progress so students do not feel blocked. Ensuring mathematical correctness at each step. Keeping responses fast under load. Parsing LaTeX and free text reliably. Managing cost and rate limits for AI calls. Presenting clear steps on mobile.

Socratic tutoring with GPT configured to ask targeted questions and never reveal full solutions. Symbolic verification using Math.js and Algebrite before responses are shown. KaTeX based rendering for clean equations. JWT auth with secure sessions, input validation, CORS, and rate limits. Redis caching to cut latency and cost. Responsive UI with Tailwind and Framer Motion. Step tracking and adaptive hints for engagement. Measured 70 to 80 percent performance gains after caching and bundle trims. Full test pass across pages, math operations, build, and security checks.

Product is production ready with a complete schema, stable API, and green test suite. Real time chat interface and visual step breakdown validated in internal testing.

The Socratic contract must be explicit or the model will leak answers. Verification at each step reduces hallucinations and builds trust. LaTeX first input lowers friction for STEM users. Caching common hints and verified steps reduces both latency and spend. Clear progress visuals improve session length.

Ship OCR for photo to LaTeX intake. Add progress analytics and a learning dashboard. Introduce light gamification with streaks and badges. Add multilingual UI. Enable WebSocket transport for streaming tokens. Finalize success metrics tracking for response time, accuracy, and retention.

Frontend: Next.js 15, TypeScript, Tailwind, Shadcn UI, Framer Motion, KaTeX. Backend: Express with TypeScript, Prisma on PostgreSQL, Redis with ioredis, JWT plus bcrypt. AI: GPT models via OpenAI with tool use for math verification. Project includes seeds, environment templates, Prisma migrations, and scripts for dev and production. Targets response time under 2 seconds, accuracy above 95 percent, perfect Lighthouse on core pages, and zero critical vulnerabilities.