This repository provides a batteries-included starting point for building a multi-tenant, AI-enabled SaaS using a monorepo layout. It contains production-minded service boundaries, shared packages for prompts and retrieval-augmented generation (RAG), infrastructure manifests, and guardrails that make it easy to plug in foundation models while staying compliant, observable, and cost-aware.

1. Install prerequisites
   • pnpm (>=8.15)
   • Node.js 18 LTS or newer
   • Python 3.11+
   • Docker (for local services)
2. Install JavaScript/TypeScript dependencies

   pnpm install

3. Bootstrap Python environments

   cd apps/inference && uv venv && source .venv/bin/activate && uv pip install -r requirements.txt
   # or use: uv sync
   cd ../../apps/batch && uv venv && source .venv/bin/activate && uv pip install -r requirements.txt

4. Start backing services

   docker compose -f infra/compose/compose.dev.yml up --build

5. Run the apps

   pnpm dev:web
   pnpm dev:api
   pnpm --filter mobile start
   uvicorn apps/inference.app.main:app --reload
   python apps/batch/jobs/nightly_eval.py

repo/
├── apps/
│   ├── web/          # Next.js 14 (App Router) frontend
│   ├── mobile/       # Expo/React Native mobile client
│   ├── api/          # NestJS BFF, multi-tenant product logic
│   ├── inference/    # FastAPI service for LLM, embeddings, rerankers
│   └── batch/        # Scheduled jobs: indexing, evals, training hooks
├── packages/
│   ├── prompts/      # Versioned prompt templates and builders
│   ├── llm-clients/  # Provider abstractions with retries/fallbacks
│   ├── rag/          # Chunking pipelines and retriever utilities
│   ├── embeddings/   # Embedding client with dimensionality guards
│   ├── evals/        # Continuous evaluation harness
│   ├── datasets/     # Dataset loaders and synthetic data tools
│   ├── featurestore/ # Feature builders for ML + hybrid RAG
│   ├── guardrails/   # Safety, jailbreak, and compliance filters
│   ├── costs/        # Token accounting, budget alerts
│   ├── utils/        # Shared helpers
│   ├── contracts/    # DTO schemas shared across services
│   └── db/           # Prisma client and migrations
└── infra/
    ├── compose/      # Docker Compose stacks (dev + GPU)
    ├── docker/       # Dockerfiles for CPU/GPU inference
    └── terraform/    # IAC scaffolding for cloud deployment

• Quality gates: pnpm lint, pnpm test, and pnpm build leverage Turborepo so only affected workspaces run.
• Continuous evaluation: pnpm --filter @repo/evals test executes golden prompts, RAG recall checks, and safety guard tests. Wire this into CI after your unit tests.
• Database: packages/db/prisma/schema.prisma defines shared models. Run pnpm --filter @repo/db prisma migrate dev to evolve the schema.
• Prompts: create typed templates in packages/prompts/src. Consumers import builders rather than hardcoding strings.
• RAG: packages/rag ships chunkers, retrievers, and pipeline definitions. Use the semantic cache helpers in packages/costs + Redis to skip repeated calls.

• Connect real model keys in apps/inference/app/dependencies.py and configure routing rules in packages/llm-clients.
• Flesh out CI pipelines (GitHub Actions examples coming soon) to run lint/build/test + pnpm --filter @repo/evals test for continuous evaluation.
• Hook up telemetry sinks (OpenTelemetry/OTLP, Prometheus, ClickHouse) by extending packages/utils logging wrappers.
• Integrate production-ready auth (Clerk/Auth0/Cognito) inside apps/api's AuthModule, and wire rate limiting with @nestjs/throttler.

This boilerplate favors clarity over completeness—replace the placeholder logic with your product-specific flows and models.