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Every frontend, mobile app, and integration layer relies on a backend that works correctly, handles failures gracefully, and stays fast under load. Backend engineering is where the real complexity lives — and where shortcuts cost the most.
We design the API contract — endpoints, request/response schemas, authentication flow, and error codes — before writing implementation code. This allows frontend and mobile teams to work in parallel.
We design the database schema, define indexes and relationships, and validate the model against the application's query patterns before building.
Layer-by-layer backend build: data access, service/business logic, API controllers, background jobs, and integrations — with unit and integration tests covering each layer.
Bring us the constraint. We'll bring the team.
We build backends with production in mind from day one. That means proper authentication and authorization, database schemas that support the query patterns the application actually needs, queue-based processing for tasks that shouldn't block the request cycle, and logging and error tracking configured before the first deployment.
Our most-used backend stack for web application and API development. Laravel's ORM, queue system, event broadcasting, and testing infrastructure make it a strong foundation for applications that need to be maintainable over years — not just functional at launch.
For real-time applications, event-driven architectures, and services where throughput and non-blocking I/O are critical. We use Node.js with TypeScript for type safety and long-term maintainability.
For data-intensive backends, machine learning integrations, automation scripts, and services where Python's ecosystem provides significant advantages — particularly when connected to data pipelines, analytics systems, or AI/ML models.
We design APIs with the consumer in mind — clear naming conventions, consistent error formats, proper versioning, and documentation that developers can actually use. Every API we build is designed as if it will be consumed by a third party, because often it will be.
We design relational schemas with normalization, indexing, and query performance in mind. For applications with non-relational data requirements, we apply MongoDB with appropriate data modeling for document-oriented workloads.
Authentication verification, input validation, SQL injection prevention, rate limiting, and security headers configured before any staging or production deployment.
Query optimization, caching implementation, and APM tooling configured so performance issues are detectable and diagnosable in production.