Systems Integration

APIs and Integrations That Hold Up Under Real Load

Connecting the systems your team actually uses. Reliable data flow, automated handoffs, fewer spreadsheets in between.

The Problem

When systems do not talk, your staff pays the difference.

Most organizations run on a stack of platforms that were never designed to talk to each other. The CRM, the data warehouse, the case management system, the third-party reporting tool. Data crosses these boundaries through some combination of CSV exports, email attachments, and someone's spreadsheet macros.

The cost shows up as quiet drag. Staff spend hours reconciling records that should match. Reports are always slightly behind reality. New vendors take months to integrate because every connection is a custom project. The team accepts this because the alternative looks expensive, but the ongoing labor tax usually exceeds what proper integration would cost.

Our Approach

Patterns over point-to-point.

We design integrations the way we design any production system. Clear contracts between services, idempotent operations, retry logic that handles real-world failures, monitoring that tells you when something is wrong before users do. The point is not to add another connection. It is to set up patterns your team can extend without us.

Where it makes sense we use API gateways, event-driven architectures, or message queues to decouple the systems. Where it does not, we keep things simple and focused on reliability over abstraction. The right integration is the one your team can operate.

Systems integration patterns reference architecture Detailed reference architecture for systems integration. Triggers (user action, scheduled job, system event, vendor webhook) feed internal applications and data stores. The integration layer hosts three patterns: an API Gateway for synchronous request/response with TLS, auth, rate limiting, schema validation, circuit breakers, and reliability features like health checks, graceful degradation, and failover routing; an Event Bus for asynchronous publish/subscribe with at-least-once delivery, idempotent consumers, dead-letter queues, and reliability features including schema registry evolution, partition ordering, and consumer offset management; and Data Pipelines for batch and streaming workloads with CDC, schema evolution, watermarks, quality monitoring, and reliability features for replay-safe transforms, late-arrival handling, and cost-aware partitioning. A shared services layer below provides cross-cutting concerns: identity broker for federation and SSO, service mesh for mTLS and traffic management, distributed tracing via OpenTelemetry, and an idempotency layer for request keys and dedup. The integration layer connects to external systems including third-party SaaS vendors and partner data systems. TRIGGERS · WHAT KICKS INTEGRATIONS OFF User action UI click · form submit interactive API call Scheduled job cron · batch window SLA-driven cadence System event state change · threshold queue depth · failure Vendor webhook third-party push async notification YOUR SYSTEMS Internal applications Web · backend services · workflow tools Internal data stores Operational DB · warehouse · object storage INTEGRATION LAYER · PATTERNS API Gateway SYNCHRONOUS · REQUEST/RESPONSE · TLS termination · auth (OAuth · mTLS) · Rate limit · quotas · throttling · Schema validation · contract tests · Circuit breaker · timeouts · retries · Versioning · deprecation policy Reliability built in · Health checks · readiness probes · Graceful degradation · Failover routing · canary REST · GraphQL · gRPC OpenAPI · contract tests Event Bus ASYNCHRONOUS · PUBLISH/SUBSCRIBE · Pub/Sub topics · partitions · At-least-once delivery · Idempotent consumers · dedup keys · Dead-letter queue · alerts · Backpressure · replay Reliability built in · Schema registry · evolution · Ordering per partition · Consumer offset management Pub/Sub · Kafka · SQS · EventBridge Idempotency keys throughout Data Pipelines BATCH + STREAMING · CDC + change events · Schema evolution · contracts · Retry · exponential backoff · Watermarks · checkpointing · Quality monitoring · SLAs Reliability built in · Replay-safe transforms · Late-arrival handling · Cost-aware partitioning Dataflow · Airflow · dbt · Spark Batch + streaming alike SHARED SERVICES · CROSS-CUTTING CONCERNS Identity broker Federation · SSO · SAML · OIDC bridge · Service-to-service tokens · Workload identities Service mesh mTLS · traffic mgmt · mTLS between services · Retries · circuit breaker · Traffic shaping · canary Distributed tracing OpenTelemetry · spans · Trace propagation · Span correlation · Latency profiling Idempotency layer Request keys · dedup · Idempotency keys · Replay protection · At-most-once semantics EXTERNAL SYSTEMS Third-party SaaS · vendors CRM · payment · analytics · monitoring Partner data systems Research platforms · clinical · regulatory
Reference patterns we reach for. Each pattern has its own reliability features baked in. The shared services layer carries cross-cutting concerns so the patterns do not each reinvent identity, tracing, or idempotency. Specific platforms swap based on what already lives in your environment.
What We Deliver

Connections that actually work.

API design and implementation with versioning, authentication, and a documentation story that holds up

Third-party platform integration with vendors, SaaS tools, research systems, and enterprise applications

Data pipelines and ETL for batch and streaming workloads, with error handling and replay capability

Event-driven workflows with message queues, dead-letter routing, and observable processing

Identity and access integration across systems, including SSO and federation work

Monitoring and alerting on integration health so failures surface immediately instead of being discovered weekly

Documentation covering data flows, contracts, and operational runbooks

Technologies We Use

Tools we reach for.

API & Service Layer

REST, GraphQL, gRPC, OpenAPI, API gateways

Messaging & Events

Pub/Sub, SQS, SNS, Kafka, EventBridge, Cloud Tasks

Data Pipelines

Dataflow, Airflow, dbt, Glue, custom Python pipelines

Languages

Python, Node.js, TypeScript, Go

Identity & Auth

OAuth 2.0, OIDC, SAML, IAM federation, service-to-service auth

Observability

Distributed tracing, structured logging, metrics, integration health dashboards

Related Case Study
Federal Health Research · Automated Pipelines

Automated deployment and data pipelines that survived a compliance lifecycle.

Our work for a federal health research agency included end-to-end automated deployment pipelines, integrated identity controls, and observable data flows tied into the platform's compliance posture. The integration layer scaled with the system and stayed maintainable as new services came online.

Read the full case study
Connect what matters

Talk to an engineer about your integration challenge.

Whether you are connecting two systems or rebuilding the integration layer between fifteen, we'll help you find the path that does not collapse under its own complexity.

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