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Karimoto

A real car-rental SaaS built on @adonisjs-lasagna/*, exercising the whole platform: two auth realms, schema-per-tenant isolation, the nine satellites (admin, billing, ai, crypto, sso, backup, websockets, reporting) plus the core feature set, a telematics plugin, and two Inertia + React consoles (the platform operator and the rental company).

  • Operator lives on the apex host localhost:3333.
  • Companies live on a vanity host <slug>.localhost:3333 (e.g. acme.localhost:3333), stored as the tenant's custom_domain.

Runtime processes

A real deployment runs three processes. In dev:

  • npm run dev — the HTTP server (Vite is auto-started, no --hmr needed).
  • npm run dev:worker — the queue worker (queue:work). Required for tenant provisioning: company creation dispatches an InstallTenant job, and the schema only exists once the worker has run it.

Infrastructure (Postgres pgvector/pgvector:pg16 + Redis + MailCatcher) comes up with npm run infra:up. Ports are 55433 / 56380 / 1025+1080, distinct from the core demo so both run side by side.

Setup from a clean database

Provisioning is asynchronous, so setup is two passes with the worker running in between. From apps/rental:

npm run infra:up                     # Postgres + Redis + MailCatcher

# 1. Control plane: operator account, central car catalog, and the two demo
#    companies (each dispatches InstallTenant).
npm run setup                        # backoffice:setup + central migrate + rental:seed

# 2. Materialise the schemas: start the worker (leave it running) so it drains
#    the InstallTenant jobs. The AI provider's after('provision') hook installs
#    pgvector into the `extensions` schema as each company is provisioned.
npm run dev:worker                   # in a second terminal; wait for the jobs to drain

# 3. Data plane: migrate each tenant schema, then fill it with demo data.
npm run setup:demo                   # tenant:vector:provision + migration:tenant:run + rental:seed:demo

setup:demo runs tenant:vector:provision first as a belt-and-suspenders step: it is idempotent, and it guarantees the vector extension exists on a pre-existing database (or one whose schemas were provisioned before the AI provider's hook was in place) before the ai_embeddings vector(N) migration runs.

Then start the server:

npm run dev                          # http://localhost:3333

Logins (dev only, refused in production)

Realm Host Email Password
Operator localhost:3333 operator@karimoto.test operator-demo-password
Company staff acme.localhost:3333 owner@karimoto.test owner-demo-password
Company staff sahara-cars.localhost:3333 owner@karimoto.test owner-demo-password

The two seed commands

  • rental:seed (control plane) — the operator account, the shared central car catalog, and the demo company rows (dispatching provisioning). Idempotent.
  • rental:seed:demo (data plane) — fills each already-migrated company with branches, a rate card, a fleet drawn from the catalog, renters with encrypted PII, bookings across the lifecycle (invoices + payments for completed ones), and a small RAG corpus of policy docs whose bodies are embedded into the tenant vector store. Idempotent; safe to re-run to top up missing rows. Sizing follows the company plan (fleet/enterprise get a full fleet, starter a smaller one that stays under its vehiclesPerTenant quota).

The fleet assistant (RAG)

The assistant streams over SSE at POST /ai/chat. Retrieval is opt-in (retrieve: true): it embeds the query and searches the tenant's ai_embeddings store, which rental:seed:demo populates from the policy docs. Offline (no ANTHROPIC_API_KEY) the chat and embeddings run on the in-process mocks, so retrieval returns real matches but the ranking is a deterministic hash, not semantic relevance. Set ANTHROPIC_API_KEY (and a real embedding backend) in .env and the same path uses the real model with no code change — re-run rental:seed:demo so the corpus is re-embedded into the real vector space.