Cloud and backend development services that scale.
APIs, microservices, event-driven architectures, and cloud infrastructure designed to handle real production load - from first request to ten million.
Backend architecture blueprint
Six layers that transform raw requests into reliable, observable, and scalable backend services.
- 01
API Gateway
Route, authenticate, and rate-limit incoming requests
- 02
Service Mesh
Microservices with discovery, load balancing, and circuit breakers
- 03
Event Bus
Async communication via events and message queues
- 04
Data Stores
Polyglot persistence - relational, document, and cache
- 05
Compute Layer
Serverless functions and containerized services
- 06
Observability
Metrics, logs, traces, and alerting across all services
API Gateway
The single entry point for all client requests. The gateway handles authentication, request validation, rate limiting, and intelligent routing - shielding internal services from direct exposure while providing a unified API surface.
- Kong
- AWS API GW
- Nginx
- JWT
Service Mesh
Services discover each other dynamically, communicate over encrypted channels, and degrade gracefully through circuit breakers. The mesh handles retries, timeouts, and load balancing so application code stays focused on business logic.
- Kubernetes
- Istio
- gRPC
- HTTP/2
Event Bus
Decoupled services communicate through events rather than direct calls. Message queues guarantee delivery, enable retry logic, and absorb traffic spikes - making the system resilient to individual service failures.
- Kafka
- RabbitMQ
- Redis Pub/Sub
- SQS
Data Stores
Each service uses the storage model that fits its access patterns. Relational databases for transactional data, document stores for flexible schemas, Redis for sub-millisecond caching, and object storage for files and media.
- PostgreSQL
- MongoDB
- Redis
- S3
Compute Layer
Workloads run where they make the most sense: long-lived services in containers with auto-scaling, short-lived tasks as serverless functions. The compute layer scales independently per service based on actual demand.
- Lambda
- ECS
- Cloud Run
- Docker
Observability
Distributed tracing follows requests across service boundaries. Structured logs aggregate centrally. Metrics dashboards surface anomalies before users notice. Alerting routes to the right team with full context.
- Prometheus
- Grafana
- ELK
- Jaeger
Built for production scale
Performance targets we design for - and hit - on every backend system we ship.
P95 Latency
Optimized hot paths with connection pooling
Uptime Target
Multi-AZ deployment with auto-failover
Requests/day
Horizontally scaled with auto-scaling groups
Deploy Time
CI/CD with blue-green deployment
What we build
Production backend systems designed for throughput, reliability, and developer experience.
RESTful & GraphQL APIs
Versioned, documented, and type-safe APIs with proper error handling, pagination, and rate limiting for internal and external consumers.
Authentication Systems
JWT, OAuth2, session management, and multi-factor authentication with secure token rotation and granular permission models.
Event-Driven Services
Async processing with guaranteed delivery, dead letter queues, and idempotent consumers for resilient distributed workflows.
Real-Time Infrastructure
WebSocket connections, Socket.IO rooms, and WebRTC signaling servers for live collaboration, chat, and streaming use cases.
Cloud Deployment
Infrastructure as code, container orchestration, auto-scaling policies, and multi-environment promotion pipelines.
Database Architecture
Schema design, migration strategies, read replicas, connection pooling, and query optimization for high-throughput systems.
How we approach backend architecture
Backend systems fail silently unless they are designed for observability, testability, and operational excellence from the start.
API-first design
We design the API contract before building the implementation. OpenAPI specs, generated types, and contract tests ensure frontend and backend teams move in parallel.
Observability from day one
Structured logging, distributed tracing, and metric dashboards ship with the first deployment - not as an afterthought when production breaks.
Infrastructure as code
Every environment is reproducible from a Git repo. Terraform modules, Helm charts, and CI/CD pipelines mean no manual server configuration - ever.
Load testing before launch
We run realistic load tests against staging environments to find bottlenecks before users do. Connection pools, query plans, and cache hit rates are all validated under pressure.
Technology stack
Modern backend and infrastructure tools chosen for performance, type safety, and operational maturity.
Languages
Frameworks
Database
Message Queue
Real-Time
Infrastructure
CI/CD
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