Engineer high-throughput, reliable backend systems and distributed architectures for demanding workloads. We build the infrastructure that powers your product at scale.
From high-throughput APIs and event-driven architectures to real-time systems and distributed task processing -- we engineer backend systems built for scale.
High-throughput API platforms (REST, GraphQL, gRPC) serving thousands to millions of requests per second
Event-driven backends using event sourcing, CQRS and message-based communication for high-volume async events
Real-time backend services for live dashboards, collaborative features, notifications and streaming data
Deep expertise across API architecture, distributed systems, data engineering, performance optimization and reliability engineering.
A structured approach to backend system development -- from architecture assessment through design, build, production hardening and scale.
Analyze current system, identify bottlenecks, map dependencies and evaluate scaling requirements.
Design target architecture with service boundaries, data models, API contracts and infrastructure plan.
Validate approach on the highest-risk component -- performance, data consistency or integration.
Build in sprints with continuous load testing and performance validation.
Connect with existing systems, migrate data and implement backward compatibility.
Load testing, chaos engineering, monitoring setup and incident response procedures.
The exact stack is selected based on system requirements -- not a fixed technology mandate.
Explore related capabilities that complement backend and distributed systems engineering.
We engineer backend systems across industries and use cases -- from API scalability and event-driven processing to multi-region architectures and data pipelines.
Scale APIs from hundreds to thousands of requests per second
Build async event processing for order fulfillment, notifications, analytics
Decompose monoliths into independent, scalable backend services
Live updates, notifications and real-time dashboards
High-performance search and filtering across large datasets
Distributed task queues for batch processing, imports and exports
ETL pipelines, data transformation and analytics infrastructure
Distribute services across regions for latency and availability
We engineer backend systems for production -- optimized for throughput, latency, reliability and observability at scale.
Horizontal scaling, load balancing, connection pooling and async processing
Query optimization, caching, CDN and efficient serialization
Circuit breakers, retry logic, graceful degradation and failover
The team is structured around your system requirements, not a fixed package. Team composition adapts based on architecture needs.
Focused backend initiative. Targeted API development, performance optimization or specific service build.
Substantial backend module development. Microservices, event-driven systems or data-intensive features.
Full backend engineering with Technical Lead, Backend Engineers, DevOps and QA.
A flexible engagement model that grows with your system -- from initial assessment to long-term engineering partnership.
Understand your system, identify bottlenecks, evaluate scaling requirements
Design the target architecture, validate with a proof of concept on the highest-risk component
Develop the backend systems with a dedicated engineering team
Ongoing team embedded in your engineering organization for continuous development
Tell us about your system, where it is struggling and what scale you need to reach. We will assess the architecture and recommend the right approach.
Data processing pipelines that transform, enrich and route data at scale - batch, stream and hybrid architectures
Decompose monolithic backends into well-bounded microservices with proper data ownership and operational tooling
Multi-region deployment, automatic failover, health checking, circuit breakers and graceful degradation
Backend platforms integrating multiple third-party APIs, legacy systems and data sources through standardized interfaces
Backend architectures supporting AI inference, model serving, queue-based AI processing and hybrid sync/async API patterns
Performance tuning, infrastructure scaling, cost optimization and capacity planning.
Query optimization, indexing, caching and read replica strategies
Reliable integration with third-party APIs, webhooks and data sources
Transaction management, eventual consistency patterns and data validation
Distributed tracing, metrics, logging, alerting and SLO tracking
API authentication, rate limiting, input validation and data encryption
Engineered backend systems handling millions of daily API requests with sub-100ms latency, 99.9%+ availability and automated scaling for demand spikes.
Team composition adapts to system requirements. Can include data, infrastructure or domain specialists.
Strategic technology partner for system evolution and engineering scale
Fixed-scope engagements are available when requirements are sufficiently defined. The right investment depends on system scope, technical complexity, team composition and roadmap.