Back End Development Services

Generic API templates give you a starter project. We build APIs designed around your consumers, your data model, and the systems that need to integrate with you.

• RESTful and GraphQL API design with versioning, rate limiting, and developer documentation
• Public APIs, webhooks, and event streams for partner and ecosystem integrations
• Native integrations with CRM, ERP, payment, and data platforms
• Hybrid human-API workflows: the system handles the routine, your team handles the exceptions

Every API we deliver comes production-deployed with monitoring dashboards, contract tests, and clear deprecation strategy. Not a Postman collection. A real platform layer engineered to be consumed by other teams, customers, and partners over time.

Replace tangled monoliths or premature microservices with service architectures designed around your team topology and product reality.

• Domain-driven microservices with clear ownership, contracts, and SLAs
• Event-driven architectures using Kafka, RabbitMQ, NATS, or cloud-native pub/sub
• Service mesh, observability, and distributed tracing built into the platform
• Strangler-fig decomposition strategies to break monoliths down without halting delivery

Companies that match service boundaries to team boundaries consistently outperform peers on deployment frequency, mean-time-to-recovery, and engineering velocity. The difference isn't the framework. It's the architectural decision underneath. We make that decision well.

Every modern back end runs on a deployment decision: containers, serverless, or hybrid. Done well, it earns scalability and cost predictability. Done carelessly, it produces surprise cloud bills and operational pain.

• Cloud-native architectures on AWS, Azure, and Google Cloud
• Containerized deployments with Docker and Kubernetes orchestration
• Serverless functions and event-driven backends for cost-efficient scaling
• Infrastructure as Code (Terraform, Pulumi) for reproducible environments

Tech stack: AWS (Lambda, ECS, EKS, Fargate, API Gateway), Azure Functions, Google Cloud Run, Kubernetes, Docker, Terraform, Pulumi, Kafka, EventBridge.

Traditional CRUD back ends break when data volume, throughput, or latency requirements grow. Data-intensive systems adapt.

• High-throughput ingestion pipelines built on Kafka, Kinesis, and Pulsar
• Stream processing with Flink, Spark Structured Streaming, and ksqlDB
• OLTP, OLAP, and hybrid storage strategies tuned to real workloads
• Hybrid human-AI workflows: the system handles the routine, your team handles the exceptions

Most data-heavy back ends stall because the original CRUD architecture can't keep up with growth. We build systems engineered for streaming, partitioning, and replay from day one. The result: data platforms that actually scale.

If you're building a product where real-time interaction is the differentiator, whether chat, dashboards, marketplaces, or multiplayer tools, execution quality is everything. The market won't forgive lag, message loss, or sync errors.

• Real-time backends with WebSocket, Server-Sent Events, and gRPC streaming
• High-concurrency services in Go, Elixir, and Rust where workloads justify it
• Presence, pub/sub, and notification infrastructure built to scale
• Backpressure, idempotency, and exactly-once semantics where business rules require them

Concurrency model selection, transport architecture, message guarantees, and observability: we evaluate all of it before recommending a stack. You get a production-ready real-time back end, not a proof of concept that breaks at 100 users.

A back end is only as good as the data layer underneath it. Schema design, query patterns, and storage choices compound into either an asset your product builds on or technical debt you spend years paying down.

• Relational, document, key-value, and time-series database architectures
• Schema design, indexing strategy, and query optimization for production workloads
• Read replicas, sharding, and partitioning strategies for scale
• Data migration, integrity validation, and zero-downtime cutover playbooks

Most back end performance issues trace back to the data layer, not application code. We solve both problems in the same engagement.

Back end systems age in place. Legacy stacks compound into engineering velocity loss, hiring difficulty, and rising maintenance cost.

• Strangler-fig migrations that incrementally replace legacy modules without downtime
• Refactoring of monolithic codebases into modular, testable services
• Database modernization, schema migration, and data integrity validation
• Documentation, knowledge transfer, and engineering enablement for your in-house team

Our clients running back end modernization programs have achieved significant reductions in infrastructure spend and measurable improvement in deployment frequency within two quarters of refactoring.