Scale • Backend & Distributed Systems
Backend &
Distributed Systems
Engineer high-throughput, reliable backend systems and distributed architectures for demanding workloads. We build the infrastructure that powers your product at scale.
25+
Engineers
40+
Clients
58
Case Studies
Backend
Distributed Systems • Cloud • Data
Distributed
High Avail.
Data Layer
Petabyte
Latency
<50ms P99
Architecture
Event-Driven
Backend Systems We Build
From high-throughput APIs and event-driven architectures to real-time systems and distributed task processing -- we engineer backend systems built for scale.
API Platforms
High-throughput API platforms (REST, GraphQL, gRPC) serving thousands to millions of requests per second
Event-Driven Systems
Event-driven backends using event sourcing, CQRS and message-based communication for high-volume async events
Real-Time Services
Real-time backend services for live dashboards, collaborative features, notifications and streaming data
Data Processing Pipelines
Data processing pipelines that transform, enrich and route data at scale - batch, stream and hybrid architectures
Microservices Architectures
Decompose monolithic backends into well-bounded microservices with proper data ownership and operational tooling
High-Availability Systems
Multi-region deployment, automatic failover, health checking, circuit breakers and graceful degradation
Integration Platforms
Backend platforms integrating multiple third-party APIs, legacy systems and data sources through standardized interfaces
Backend for AI Products
Backend architectures supporting AI inference, model serving, queue-based AI processing and hybrid sync/async API patterns
Engineering Capabilities
Deep expertise across API architecture, distributed systems, data engineering, performance optimization and reliability engineering.
API Architecture
- REST and GraphQL API design
- API gateway and rate limiting
- Versioning and backward compatibility
- Authentication and authorization
- API documentation and SDKs
Distributed Systems
- Event-driven architecture
- Message queues (Kafka, SQS, RabbitMQ)
- Service-to-service communication
- Distributed transactions and sagas
- Consistency and partition tolerance
Data Architecture
- Database schema design and optimization
- Read/write splitting and replication
- Caching strategies (Redis, CDN)
- Search indexing (Elasticsearch)
- Data partitioning and sharding
Performance Engineering
- Load testing and benchmarking
- Latency optimization
- Connection pooling and resource management
- Query optimization
- Horizontal and vertical scaling
Reliability Engineering
- Circuit breakers and retry patterns
- Graceful degradation
- Health checks and failover
- Monitoring and alerting
- Incident response and post-mortems
Backend Engineering Approach
A structured approach to backend system development -- from architecture assessment through design, build, production hardening and scale.
01
Architecture Assessment
Analyze current system, identify bottlenecks, map dependencies and evaluate scaling requirements.
02
System Design
Design target architecture with service boundaries, data models, API contracts and infrastructure plan.
03
Proof of Concept
Validate approach on the highest-risk component -- performance, data consistency or integration.
04
Iterative Development
Build in sprints with continuous load testing and performance validation.
05
Integration & Migration
Connect with existing systems, migrate data and implement backward compatibility.
06
Production Hardening
Load testing, chaos engineering, monitoring setup and incident response procedures.
07
Scale & Optimize
Performance tuning, infrastructure scaling, cost optimization and capacity planning.
Technology Stack
The exact stack is selected based on system requirements -- not a fixed technology mandate.
Backend
🐍Python / FastAPI
🟢Node.js / NestJS
🔷Golang
TSTypeScript
☕Java / Spring Boot
Messaging
📨Apache Kafka
📬AWS SQS / SNS
🐇RabbitMQ
🔴Redis Pub/Sub
Data & Storage
🐘PostgreSQL
🍃MongoDB
🔴Redis
🔍Elasticsearch
⚡DynamoDB
Infrastructure
☁️AWS
🐳Docker / Kubernetes
🏗️Terraform
🔄GitHub Actions
⚖️Load Balancers
Monitoring
📊Datadog
📈Prometheus / Grafana
👁️AWS CloudWatch
🛡️Sentry
🔍ELK Stack
Related Solutions
Explore related capabilities that complement backend and distributed systems engineering.
Cloud Performance Engineering
Optimize cloud infrastructure costs, auto-scaling and performance for your backend systems.
ExploreAI Product Engineering
Build AI-powered products with production backend infrastructure and distributed AI workloads.
ExploreSoftware Modernization
Decompose monoliths, modernize legacy backends and migrate to microservices architecture.
ExploreBackend Use Cases
We engineer backend systems across industries and use cases -- from API scalability and event-driven processing to multi-region architectures and data pipelines.
API Scalability
Scale APIs from hundreds to thousands of requests per second
Event-Driven Processing
Build async event processing for order fulfillment, notifications, analytics
Microservices Migration
Decompose monoliths into independent, scalable backend services
Real-Time Data Streaming
Live updates, notifications and real-time dashboards
Search Infrastructure
High-performance search and filtering across large datasets
Background Job Processing
Distributed task queues for batch processing, imports and exports
Data Pipeline Architecture
ETL pipelines, data transformation and analytics infrastructure
Multi-Region Architecture
Distribute services across regions for latency and availability
Database Optimization
Query optimization, indexing, caching and read replica strategies
Integration Architecture
Reliable integration with third-party APIs, webhooks and data sources
Engineered for Scale
We engineer backend systems for production -- optimized for throughput, latency, reliability and observability at scale.
Throughput
Horizontal scaling, load balancing, connection pooling and async processing
Latency
Query optimization, caching, CDN and efficient serialization
Reliability
Circuit breakers, retry logic, graceful degradation and failover
Data Consistency
Transaction management, eventual consistency patterns and data validation
Observability
Distributed tracing, metrics, logging, alerting and SLO tracking
Security
API authentication, rate limiting, input validation and data encryption
Production Reference
Engineered backend systems handling millions of daily API requests with sub-100ms latency, 99.9%+ availability and automated scaling for demand spikes.
Engineering Team Model
The team is structured around your system requirements, not a fixed package. Team composition adapts based on architecture needs.
2-Person Squad
Focused backend initiative. Targeted API development, performance optimization or specific service build.
3-Person Squad
Substantial backend module development. Microservices, event-driven systems or data-intensive features.
5-Person Squad
Full backend engineering with Technical Lead, Backend Engineers, DevOps and QA.
Custom Team
Team composition adapts to system requirements. Can include data, infrastructure or domain specialists.
How We Work Together
A flexible engagement model that grows with your system -- from initial assessment to long-term engineering partnership.
1
Architecture Assessment
Understand your system, identify bottlenecks, evaluate scaling requirements
2
System Design & POC
Design the target architecture, validate with a proof of concept on the highest-risk component
3
System Build
Develop the backend systems with a dedicated engineering team
4
Backend Engineering Team
Ongoing team embedded in your engineering organization for continuous development
5
Long-Term Partnership
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.
Relevant Case Studies
Video Encoding
SCTE-35 Ad Marker Signaling & Media Trailer Insertion Pipeline
A streaming media company needed a robust, automated pipeline for injecting SCTE-35 ad markers into live and VOD streams, along with the ability to insert promotional trailers (pre-roll, mid-roll, and post-roll) at precisely timed positions — enabling monetization across FAST channels, live events, and on-demand content libraries.
AWS Elemental MediaLiveAWS Elemental MediaConvertAWS Elemental MediaPackage+9
Read Case Study
Video Encoding
AWS Media Services for FAST Channel Streaming over SRT
A media company needed to establish reliable, low-latency contribution feeds for their FAST channels using the Secure Reliable Transport (SRT) protocol — enabling high-quality content ingest from remote studios, cloud playout systems, and syndication partners over unpredictable internet connections.
AWS Elemental MediaConnectAWS Elemental MediaLiveAWS Elemental MediaPackage+7
Read Case Study
Video Encoding
AWS Media Services for FAST Channel Streaming over HLS
A media company needed to launch Free Ad-Supported Streaming Television (FAST) channels — 24/7 linear streams of curated video content delivered over HLS to smart TVs, set-top boxes, and web/mobile players, monetized through programmatic ad insertion.
AWS Elemental MediaLiveAWS Elemental MediaPackageAWS Elemental MediaTailor+8
Read Case Study
Facing a Backend Challenge?
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.