Microservices, event-driven pipelines, and real-time data — designed for failure as the default state, so the system stays up when components don't.
Service decomposition along real domain boundaries. API gateway patterns across REST, GraphQL, and gRPC. Service discovery, registration, and deliberate inter-service communication.
Kafka, RabbitMQ, and AWS SQS/SNS. Event sourcing and CQRS. Asynchronous processing pipelines with exactly-once delivery guarantees where correctness depends on it.
Stream processing with Kafka Streams and Flink. WebSockets and Server-Sent Events. Real-time analytics, aggregations, and live dashboards that keep up with the firehose.
Istio, Linkerd, and Consul Connect. Traffic management and routing, mutual TLS for service-to-service auth, and circuit breakers with retry logic baked in.
Distributed transactions via the Saga pattern. Eventual-consistency strategies, conflict resolution and reconciliation, and two-phase commit where it genuinely belongs.
Distributed tracing with Jaeger and Zipkin. Centralized logging through ELK and Loki, metrics aggregation in Prometheus, and live service-dependency mapping.
Systems fail and network partitions happen. We design for that as the default state — circuit breakers, bulkheads, backpressure, and graceful degradation.
Stateless services scale by adding instances, not bigger hardware. Idempotent APIs make retries safe. Decoupled services can't cascade. The same architecture runs the behavioral-health platform we operate — Cadence processes live intake on services built this way.
Need the foundation it runs on? See Cloud Infrastructure →AWS, Terraform, Kubernetes, and CI/CD — the production ground your services stand on.
Explore Cloud Infrastructure → OptimizeThroughput, latency, and tuning for the real-world traffic shapes your system actually sees.
Explore Performance Engineering → ShipThe front end that connects to your backend — fast, custom, API-integrated.
In productionOur behavioral-health intake engine, running on resilient distributed services.
Only where it earns its complexity. We decompose along real domain boundaries and migrate incrementally with the Strangler Fig pattern — replacing functionality piece by piece instead of rewriting everything at once.
We use the Saga pattern with compensating transactions for distributed workflows, event sourcing and CQRS where audit and scale demand it, and we identify exactly where eventual consistency is acceptable so you trade strictness for availability deliberately.
Circuit breakers, bulkheads, backpressure, and idempotent APIs. Services are decoupled through message queues so a slow or failing component degrades gracefully instead of cascading.
Tell us where the load is going. We'll design a distributed architecture that scales and degrades on purpose, not by accident.