MongoDB

Your documents.
Your apps.

Connect your MongoDB Atlas cluster. Describe the app you need. camelAI reads your collections, understands nested documents, and builds it.

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users
{
  "_id": ObjectId("64f1a2b3c8e9d1f2a3b4c5d6"),
  "name": "Amara Chen",
  "email": "amara@acme.io",
  "role": "admin",
  "profile": {
    "avatar": "https://cdn.acme.io/avatars/amara.jpg",
    "timezone": "US/Pacific",
    "preferences": {
      "theme": "dark",
      "notifications": true
    }
  },
  "teams": ["engineering", "platform"],
  "loginHistory": [
    { "ts": "2026-03-12T09:14:00Z", "ip": "192.168.1.42" },
    { "ts": "2026-03-10T14:33:00Z", "ip": "10.0.0.7" }
  ],
  "createdAt": ISODate("2025-08-14T00:00:00Z")
}

Your data is already flexible. Your tools should be too.

companies
idnameaddresstags
1Acme CorpNULLNULL
2Globex Inc123 Main StNULL
3Initech456 Oak Avesaas
NULLs everywhere
No nested data
Arrays? Add another table and JOIN
companies
{
  "name": "Acme Corp",
  "address": {
    "street": "123 Main St",
    "city": "San Francisco",
    "state": "CA"
  },
  "tags": ["saas", "enterprise"],
  "contacts": [
    { "name": "Jordan", "role": "CTO" },
    { "name": "Riley", "role": "VP Eng" }
  ]
}
Nested objects — address is structured, not a flat string
Optional fields — no NULLs, no wasted columns
Arrays inline — no extra tables, no JOINs

What you can build from your collections.

Customer CRM

Search, filter, and manage customer records with nested contact history, notes, and activity timelines. Edit deeply nested profile fields inline.

db.users
Product Catalog

Browse products with faceted search across variant attributes — sizes, colors, pricing tiers. Detail pages with nested specs and inventory data.

db.products
Support Dashboard

Track open tickets by priority, monitor average resolution time, and let agents update ticket status. Threaded message history built in.

db.tickets

camelAI speaks your aggregation language.

Source
orders
42,817 documents
Stage 1
$match
status: "paid" and date >= 2025-01
Stage 2
$group
by month — SUM(total), AVG(value), COUNT
Stage 3
$sort
by total, descending
Result
Monthly revenue by category

Describe an aggregation in plain English. camelAI builds the pipeline, runs it, and visualizes the output.

What will you build from your documents?

“Connect to our MongoDB Atlas cluster and build an admin panel for the users collection. I need search, filters by role and status, and the ability to edit nested profile fields.”

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“Our products collection has deeply nested variant data — sizes, colors, prices. Build a product catalog app with faceted search and a detail page for each product.”

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“Run an aggregation pipeline on our orders collection: group by month and category, calculate average order value, and visualize the trends as a line chart.”

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“Build a support dashboard from our tickets collection. Show open tickets by priority, average resolution time, and let agents update ticket status from the dashboard.”

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Your documents are ready.

Start building.

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