01
Schema design & document modeling
Choosing between embedding and referencing to avoid oversized documents and write contention.
Technologies — Databases & storage
MongoDB Atlas architecture & performance optimization
We design scalable MongoDB document models and aggregation pipelines that maximize throughput and avoid collection scans.
Core capabilities
Why we build with MongoDB
02
Aggregation pipelines
Fast multi-stage aggregations backed by compound indexes for real-time analytics.
03
Atlas sharding & tiered storage
Distributing large collections across shard clusters with zone-based data placement.
Use cases
Where MongoDB fits
Flexible product catalogs
E-commerce catalogs with highly varied, dynamic product attributes.
Content management platforms
Hierarchical document stores with nested metadata and revision history.
How we staff it
MongoDB engineers you interview first
Seniority and experience are agreed in the proposal, and you interview every engineer before they start.
Working-hours overlap is agreed for each engagement and written into the statement of work — the shared window, who shifts hours, and how handoffs work outside it.
Technical FAQs
Frequently asked engineering questions
MongoDB excels for rapidly evolving hierarchical schemas, polymorphic documents, and large-scale horizontal sharding.
We analyze the explain plan, build targeted compound indexes following the Equality-Sort-Range (ESR) rule, and avoid full collection scans.
Ecosystem
Related technologies
Planning a MongoDB project?
Tell us about your architecture, backlog and team. We'll reply within one business day with an honest read on whether we can help.