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Secure, scalable API and backend development: custom REST/GraphQL APIs, database design, authentication, cloud infrastructure, and system integrations that power your applications.
A beautiful frontend means nothing if the backend behind it is slow, fragile, or insecure. The backend is the engine: it processes requests, manages your data, handles authentication, and connects your systems. When it's built poorly, you get slow load times, server crashes during traffic spikes, data silos, and security vulnerabilities. We build backends and APIs that are fast, secure, and designed to scale with your business.
We start with architecture design: mapping your data flows, defining API endpoints, and identifying bottlenecks before writing code. Development follows clean, modular patterns with comprehensive documentation. Every build goes through security testing (input validation, rate limiting, stress testing under simulated load) before deployment. After launch, we set up real-time monitoring and stay on as a technical partner for ongoing support and iteration.
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Well-documented APIs with versioning, pagination, rate limiting, and authentication — following OpenAPI 3.0 specification standards for seamless frontend and third-party integration.
Schema design, index optimization, query performance tuning, and migration scripts for PostgreSQL, MySQL, MongoDB, and Redis — built for correctness first, speed second.
JWT-based auth, OAuth2 flows, role-based access control (RBAC), and multi-tenant architectures — securing your API endpoints against unauthorized access at every layer.
Asynchronous task processing for email sending, report generation, data imports, and webhook deliveries using BullMQ, Redis, or serverless functions.
Auto-generated OpenAPI documentation, consistent error responses, and typed client SDKs — so frontend developers and third-party integrators can work independently and efficiently.
Stateless API design with connection pooling, caching layers, and queue-based processing ensures your backend handles traffic spikes gracefully — without architectural rewrites.
Well-structured APIs with consistent naming conventions, predictable pagination, and comprehensive error handling make integrations faster and less error-prone for every team that consumes them.
Defining resource schemas, endpoint contracts, authentication requirements, and database entity relationships in a collaborative specification document.
Building API routes, database queries, business logic, and validation layers — with unit tests, integration tests, and end-to-end test coverage.
Input validation, SQL injection prevention, rate limiting, CORS configuration, and load testing to verify performance under expected traffic volumes.
Containerized deployment (Docker), structured logging, health check endpoints, error tracking (Sentry), and performance monitoring (New Relic or Datadog).
Our primary backend stack is Node.js (Express/Fastify) with TypeScript, PostgreSQL for relational data, and Redis for caching and queues. For serverless workloads, we use Vercel Edge Functions and AWS Lambda.
Yes. We frequently build adapter layers and middleware that bridge modern API standards with legacy databases, SOAP services, and proprietary data formats.
REST is simple, widely understood, and well suited to straightforward resource-based operations — the right default for most projects. GraphQL earns its added complexity when clients need to fetch varied, deeply nested data in a single request, or when multiple different front ends consume the same API with different data requirements. Choosing GraphQL for a simple CRUD API usually adds tooling and caching complexity without a corresponding benefit.
Authentication and authorisation appropriate to the risk, input validation and parameterised queries to prevent injection, rate limiting to prevent abuse and runaway costs, HTTPS throughout, and secrets held in environment configuration rather than committed to the repository. We also implement structured logging and error monitoring, because an API you cannot observe is one you cannot debug when it matters most — under load, in production.
Real scale planning means understanding where your specific bottleneck will appear, which is usually the database rather than the application layer. We design schemas and indexes for your actual query patterns, add caching where it demonstrably helps, and build stateless services that scale horizontally. We do not over-engineer for hypothetical volume — architecture built for imagined scale you never reach is a real and common cost.
Yes, and this is frequently the most valuable part of a backend engagement. We build integration layers that normalise inconsistent third-party responses into a single internal schema, with retry logic, idempotency handling, and failure logging. The difference between a robust integration and a fragile one is almost entirely in how it behaves when the upstream system is slow, rate-limited, or down — not in how it behaves when everything works.
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