MongoDB.
A document database for records that vary in shape, such as catalogues, content and activity logs.
Talk to us about MongoDB
Key features
What MongoDB does well.
Change streams
Applications can subscribe to inserts, updates and deletes as they happen, without polling the database.
Schema validation
JSON Schema rules on a collection can reject documents that are missing fields or have the wrong types.
Time series collections
Collections built for measurements over time store readings compactly and query them by time range.
Replica sets
Every production deployment keeps copies on several nodes and elects a new primary automatically if one fails.
What we build with it
Store each record the way the app reads it.
MongoDB stores data as JSON-like documents, so a record and its nested details are read and written together. That suits catalogues, content and logs, where one record rarely looks like the next. A database without a fixed schema still needs a plan, so we design the documents around the queries the product actually runs.
Product catalogues
Items with different attributes in each category, without a new table for every variation.
Search and AI features
Atlas Search and Vector Search over the same documents, with no second system to keep in sync.
Activity and event data
High-volume records with time-based indexes and aggregation pipelines for reports.
When it’s the right choice
MongoDB suits data that is naturally nested or keeps changing shape, and teams already invested in MongoDB or Atlas.
When we’d pick something else
When records have many relationships and transactions span them, as with orders, invoices and payments, PostgreSQL is usually simpler.
Our MongoDB services
How we can help.
Data modelling review
A review of documents, indexes and embedding choices against the queries the product actually runs.
Aggregation and indexing
Aggregation pipelines for reports and indexes checked with explain plans, so slow queries are found early.
Migration to Atlas
Moves from self-hosted MongoDB to Atlas, with version upgrades handled along the way.
Search features
Atlas Search and Vector Search indexes for product search, document lookup and AI retrieval.
How we work
Starting a MongoDB project.
Review what exists
Code, data, hosting and the people who maintain it, before any MongoDB decision is made.
Agree the approach
A written scope: what changes, what stays, the risks and how we will know it worked.
Build in reviewable slices
Working increments your team can test, rather than one large delivery at the end.
Hand over or stay on
Documentation and access for your team, or a defined support scope if you want us to keep going.
Where AI fits
AI in data work.
Vector search where your data lives
PostgreSQL with pgvector (including on Supabase and Neon), MongoDB Atlas and Redis can all store embeddings next to your records, so search does not need a separate system.
Knowledge assistants ↗Access rules before retrieval
Row-level security in PostgreSQL limits what an assistant can retrieve to what the signed-in user is allowed to see.
Knowledge assistants ↗
Where it stops: Embeddings are derived from personal data too, so they need the same retention and deletion rules as the records they came from.
Where it fits
Related expertise.
- Web application development Browser-based tools for customers and staff, built for the data, roles and permissions behind them.
- APIs & integrations Connect your product to the systems your team relies on, with clear contracts and handled failures.
- Knowledge assistants Answer questions from your approved documents, with sources shown and access rules respected.
Common questions
MongoDB questions.
Does MongoDB support transactions?
Yes, across multiple documents and collections. We still design documents so most writes touch a single document, which keeps them fast.
Should we use MongoDB Atlas or run it ourselves?
Atlas, for most teams. It runs on AWS, Azure and Google Cloud, takes care of backups, scaling and upgrades, and includes search and vector search without extra setup.
Further reading
- Answers staff can trust: an assistant built on approved documents How to build a document assistant that cites its sources, respects permissions and says when it does not know.
- Awaab’s Law and the software landlords use for repairs What Awaab’s Law asks of the systems social landlords use to log, investigate and repair hazards, and the records they need to keep.
- Firebase, Supabase or a custom back end: how to choose How to choose between Firebase, Supabase and a custom back end by data shape, access rules, cost, exit paths and skills.
