Connect MongoDB, DocumentDB, and Cosmos DB as governed NoSQL sources for AI answers. Answerplane understands document structure, arrays, and embedded documents so teams can inspect aggregation pipelines with provenance.
14-day trial / no credit card / 150 governed questions included
Scope MongoDB, inspect the plan, and publish governed answers
Provide your MongoDB connection string (mongodb:// or mongodb+srv://). Supports MongoDB Atlas, self-hosted MongoDB, AWS DocumentDB, and Azure Cosmos DB (MongoDB API). Authentication and SSL included.
Answerplane samples your MongoDB collections to understand document structure, field types, nested objects, and arrays. This schema awareness keeps generated filters and pipelines grounded in the collection.
Ask 'Show me users who purchased in the last week' or 'What's the average order value by category?' Then inspect the MongoDB filter or aggregation pipeline with source context attached.
Optimized for MongoDB's unique capabilities
Answerplane understands MongoDB's document model, aggregation framework, and query language so teams can govern approved collections, inspect generated filters and pipelines, and keep provenance attached to each answer.
Automatically creates complex MongoDB aggregation pipelines with $match, $group, $project, $lookup, $unwind, and more. Perfect for analytics and data transformation.
Analyzes your MongoDB collections to understand document structure, nested objects, arrays, and field types. Dynamic schemas become reviewable collection context.
Plan answers over nested arrays and embedded documents. Generated filters use $elemMatch, array operators, and dot notation with provenance attached.
See how governed questions become inspectable MongoDB plans
"Show me all active users from California"
db.users.find({
status: "active",
"address.state": "CA"
}).sort({ createdAt: -1 })Explanation: Simple MongoDB find query with dot notation for nested fields
"Find orders with total greater than $500"
db.orders.find({
total: { $gt: 500 }
}, {
orderId: 1,
customerName: 1,
total: 1,
orderDate: 1
}).sort({ total: -1 })Explanation: Query with comparison operator and projection
"What's the average order value by product category?"
db.orders.aggregate([
{ $unwind: "$items" },
{ $lookup: {
from: "products",
localField: "items.productId",
foreignField: "_id",
as: "productDetails"
}},
{ $unwind: "$productDetails" },
{ $group: {
_id: "$productDetails.category",
avgOrderValue: { $avg: "$total" },
orderCount: { $sum: 1 }
}},
{ $sort: { avgOrderValue: -1 }}
])Explanation: Aggregation pipeline with $unwind, $lookup (JOIN), $group, and $sort
"Find users who have purchased more than 3 times"
db.orders.aggregate([
{ $group: {
_id: "$userId",
purchaseCount: { $sum: 1 },
totalSpent: { $sum: "$total" }
}},
{ $match: { purchaseCount: { $gt: 3 }}},
{ $lookup: {
from: "users",
localField: "_id",
foreignField: "_id",
as: "userDetails"
}},
{ $unwind: "$userDetails" },
{ $project: {
userName: "$userDetails.name",
email: "$userDetails.email",
purchaseCount: 1,
totalSpent: 1
}},
{ $sort: { purchaseCount: -1 }}
])Explanation: Multi-stage aggregation with grouping, filtering, and lookup
"Show products with 'electronics' tag in tags array"
db.products.find({
tags: "electronics"
}, {
name: 1,
price: 1,
tags: 1
}).sort({ price: -1 })Explanation: Query array field - MongoDB automatically checks array membership
"Find orders containing a specific product by name"
db.orders.find({
items: {
$elemMatch: {
productName: "iPhone 15 Pro"
}
}
}, {
orderId: 1,
customerName: 1,
orderDate: 1,
"items.$": 1
}).sort({ orderDate: -1 })Explanation: Uses $elemMatch to query array of embedded documents
These are just a few examples. Answerplane keeps the plan, query, and provenance visible before teams save or embed an answer.
Your MongoDB data is protected with enterprise-grade security
Read-only MongoDB user roles enforced for all connections
Connection strings encrypted at rest with AES-256
SSL/TLS required for MongoDB Atlas and production databases
Query execution timeouts prevent resource exhaustion (configurable)
No wholesale source-data replication; saved artifacts retained only when features require it
Respects MongoDB user permissions and role-based access control
Multi-tenant isolation at the organization level
Comprehensive audit logging for compliance (who queried what, when)
Everything you need to know about using Answerplane with MongoDB
Go to Settings > Databases > Add Database, select MongoDB, and provide your connection string (mongodb:// or mongodb+srv://). For MongoDB Atlas, copy the connection string from your cluster's Connect dialog. We recommend creating a read-only user.
Yes. Answerplane builds reviewable MongoDB aggregation plans for complex questions. Stages like $match, $group, $project, $lookup, $unwind, and $sort stay visible before the answer is reused.
Yes. Answerplane understands nested structures, arrays, and embedded documents, then builds reviewable plans with dot notation, $elemMatch, array operators, and explicit document hierarchy handling.
Answerplane supports MongoDB 4.0+, including MongoDB 5.x, 6.x, and 7.x. Also compatible with MongoDB Atlas, AWS DocumentDB, and Azure Cosmos DB (MongoDB API).
We sample documents from your collections to infer the schema structure, field types, nested objects, and arrays. Answerplane handles MongoDB's dynamic schema and adapts to variations in document structure.
Yes! Answerplane excels at analytics with MongoDB aggregation framework. It generates pipelines for metrics, grouping, time-series analysis, joins ($lookup), and complex calculations. Perfect for dashboards and reporting.
Answerplane plans against MongoDB collections directly. Answers use native filters and aggregation pipelines, with the generated plan, source context, and provenance available for review before teams publish dashboards or embeds.
Answerplane treats MongoDB as a governed source instead of forcing document data into a SQL-only workflow. Teams can combine approved collections, inspect aggregation pipelines, and keep source boundaries attached to the answer.
Yes! Answerplane generates aggregation pipelines with $lookup stages to join data across collections, similar to SQL JOINs but using MongoDB's aggregation framework.
We use read-only credentials, SSL/TLS encryption, encrypted connection strings at rest, and multi-tenant isolation. We do not ingest or replicate your MongoDB contents wholesale; saved chats, dashboards, exports, and materialized artifacts are retained only when you use those features.
Still have questions?
Contact our teamLet teams ask questions, inspect plans, and publish trusted MongoDB answers with provenance attached.
14-day trial / no credit card / 150 governed questions included