Backend & APIs

Node.js gives us a single language across the stack, an event-driven model that handles high-concurrency workloads well, and one of the largest package ecosystems available — which matters when you need to integrate payments, messaging, or analytics without reinventing them. For most product backends, from a scrappy MVP to a system handling millions of requests, it's a proven, well-understood choice.

MongoDB's document model maps naturally onto how most application data actually looks — nested, evolving, and not always neatly relational. That flexibility is powerful, but it's also where a lot of MongoDB projects go wrong: schemas that grow organically without a plan eventually become a performance and consistency problem. We design schemas deliberately around your actual query patterns, not just whatever shape the data first arrives in.

REST remains the pragmatic default for most APIs — it's simple, cacheable, and every client library understands it. GraphQL earns its complexity when you have multiple client types with very different data needs — a mobile app, a web dashboard, and a partner integration all pulling different slices of the same data — where a single flexible query layer avoids a sprawl of bespoke REST endpoints.

We don't pick GraphQL by default because it introduces real operational overhead: caching gets harder, N+1 query problems need active management, and the learning curve for a new team member is steeper. We'll recommend REST unless there's a concrete reason your product needs GraphQL's flexibility.

A backend that works but isn't secure is a liability waiting to surface. We build in input validation and sanitisation at every boundary, rate limiting to blunt abuse and brute-force attempts, properly scoped JWT or OAuth-based authentication, role-based access control so users only ever see what they're meant to, and secrets management that keeps credentials out of source control entirely.

We also follow OWASP's guidance as a baseline rather than an afterthought — checking for injection vulnerabilities, insecure deserialization, and the other categories that show up in almost every real-world breach. None of this is exotic; it's just consistently applied discipline.

Backends fail in production in ways they never do in development — under real load, with real network partitions, and real edge cases in user data. We design with that in mind: structured logging and monitoring from day one, graceful error handling instead of silent failures, database indexes that match actual query patterns rather than guesswork, and load testing before a launch, not after a slowdown.

The goal is a system that degrades gracefully under pressure and gives you clear signal when something is wrong — so incidents get caught and fixed in minutes, not discovered by an angry customer three days later.