Utility LMSHybrid data architectureGujarati-ready AI

AI-first learning platform for utility training teams.

A production-oriented LMS plan that combines structured SaaS records in Postgres with flexible AI, RAG, lesson, quiz, and analytics workflows in MongoDB.

Open dashboardReview architecture
Platform blueprint
Current implementation surface plus the next production domains to connect.
3
AI providers
8
Mongo models
4
Route handlers
6
Data domains
Request flow
1. Next.js UI
2. Route handlers
3. AI router
4. Data stores
Architecture

Use each database for the job it handles best.

Postgres owns consistency-sensitive SaaS records. MongoDB owns flexible learning content, activity telemetry, AI memory, and RAG data that will evolve quickly.

PostgreSQL on Neon
Systems of record
  • Better Auth users and roles
  • Courses, enrollments, and certificates
  • Stripe payments and subscriptions
  • RBAC, organizations, and audit trails
MongoDB with Mongoose
AI and learning telemetry
  • AI chats, notes, and session memory
  • Lessons, quizzes, and attempts
  • Activity streams and analytics events
  • RAG documents, chunks, and citations
AI tutor router
Routes maths, coding, Gujarati, science, and general learning requests to the best configured provider.
RAG knowledge base
Indexes textbooks, utility manuals, OCR text, chunks, citations, and embeddings for grounded answers.
Assessment engine
Generates MCQs, coding tasks, scenario exams, hints, and adaptive scoring metadata.
Learning analytics
Tracks progress, streaks, weak topics, quiz behavior, AI usage, and device context.
Multi-model AI

Route every learning request to the strongest model family.

The app already has a provider-aware router service. The next layer is adding provider credentials, usage metering, fallback rules, and cost caps per organization.

Claude
Maths and reasoning

Long-context explanations and step-by-step problem solving.

GPT-4o
General learning and coding

Interactive tutoring, code help, quiz creation, and content drafts.

Gemini
Gujarati RAG and OCR

Multilingual support for Gujarati, Hindi, OCR-heavy documents, and manuals.

Event-driven backbone
Start with Inngest or Trigger.dev before introducing heavier queue infrastructure.
Auth
user.createduser.logged_inuser.role_changed
Learning
lesson.completedquiz.failedquiz.passedai.chat.started
Payment
payment.completedsubscription.renewedinvoice.failed
AI
rag.queryembedding.createdai.response.generated
Gujarati RAG pipeline
Use Qdrant for vector search, MongoDB for document metadata, and Gemini for multilingual answer generation.
01PDF upload
02OCR
03Chunking
04Embeddings
05Vector search
06LLM context
07Answer
Production plan

Convert the blueprint into a SaaS-ready build sequence.

View analytics shell
Security
RBACRate limitsAI abuse controlsJWT/session rotation
Performance
Streaming responsesRedis cachingBackground jobsEdge-friendly UI
SaaS
OrganizationsTeam dashboardsCertificatesLeaderboards
Roadmap

Build foundation first, then add adaptive intelligence.

Keep the first release focused on the core LMS loop: enroll, learn, ask, assess, measure, and bill. The advanced AI workflows become safer once those records are reliable.

Phase 1
Foundation
  • Auth and roles
  • AI chat
  • Quiz generation
  • Dashboard analytics
Phase 2
Learning intelligence
  • Gujarati RAG
  • Adaptive plans
  • Voice tutor
  • Live classroom
Phase 3
Automation
  • AI-generated courses
  • AI grading
  • Coding sandbox
  • Agentic workflows

Recommended stack

Next.js 16, shadcn/ui, Better Auth, Neon, Drizzle, MongoDB, Stripe, OpenAI, Gemini, Claude, Qdrant, Inngest, Upstash, and PostHog.