AI for MongoDB

Query your document database in plain English — no aggregation pipelines required.

MongoDB is the leading NoSQL document database, trusted by thousands of organizations for its flexible schema, horizontal scalability, and developer-friendly JSON-like document model. From real-time analytics to content management to IoT data, MongoDB handles unstructured and semi-structured data with ease.

AI for Database translates your natural language questions into MongoDB aggregation pipelines and queries. You no longer need to memorize $match, $group, $lookup, or $unwind stages — just describe what you want in plain English and let the AI build the pipeline.

Whether your data lives in MongoDB Atlas, a self-managed replica set, or a sharded cluster, AI for Database connects securely and gives your entire team the ability to explore documents, run analytics, and build dashboards.

What's supported

Full-featured AI querying tailored for MongoDB.

Natural language to MongoDB aggregation pipelines

Support for $match, $group, $lookup, $unwind, and more

Nested document and array field querying

Schema inference from document samples

Compatible with MongoDB Atlas and self-managed deployments

Read-only connection mode for safety

Visualization of document-based results

Scheduled alerts based on collection conditions

Example queries

Ask in plain English. AI for Database writes the MongoDB query.

“How many orders were placed per product category last month?”

db.orders.aggregate([
  { $match: { createdAt: { $gte: ISODate("2026-02-01"), $lt: ISODate("2026-03-01") } } },
  { $unwind: "$items" },
  { $group: { _id: "$items.category", count: { $sum: 1 } } },
  { $sort: { count: -1 } }
]);

“Show me users who have more than 5 active sessions”

db.users.aggregate([
  { $lookup: { from: "sessions", localField: "_id", foreignField: "userId", as: "sessions" } },
  { $addFields: { activeSessions: { $size: { $filter: { input: "$sessions", as: "s", cond: { $eq: ["$$s.active", true] } } } } } },
  { $match: { activeSessions: { $gt: 5 } } },
  { $project: { name: 1, email: 1, activeSessions: 1 } }
]);

“What is the average response time per API endpoint?”

db.api_logs.aggregate([
  { $group: { _id: "$endpoint", avgResponseMs: { $avg: "$responseTimeMs" }, count: { $sum: 1 } } },
  { $sort: { avgResponseMs: -1 } }
]);

Get started in minutes

Enter your MongoDB connection string (mongodb:// or mongodb+srv://) in the connection form.

Specify the database name and optionally restrict access to specific collections.

AI for Database samples documents from each collection to infer schema and field types.

Ask questions in plain English and receive aggregation pipeline results instantly.

For MongoDB Atlas, whitelist the AI for Database IP or use VPC peering for private connectivity.

Use cases

Popular ways teams use AI for Database with MongoDB.

Query user activity logs stored as nested documents without writing aggregation pipelines.

Build real-time dashboards from IoT sensor data stored in MongoDB collections.

Let product teams explore feature usage data and build cohort analyses.

Monitor API performance and get alerts when error rates exceed thresholds.

Generate weekly content performance reports from your CMS MongoDB collections.

Also supported

🔥

All databases

Ready to query MongoDB with AI?

Connect your MongoDB database and start asking questions in plain English. Free to start.

Start free View pricing