Enterprise AI needs more than a model.

It needs a reliable way to connect AI services to the systems the business already depends on: APIs, databases, messaging platforms, cloud services, CRMs, ERPs, and legacy applications.

We use Apache Camel as the integration foundation for our enterprise AI workflows.

Apache Camel is a mature, open-source integration framework under the Apache Software Foundation. It provides 350+ connectors and established Enterprise Integration Patterns for routing, transformation, messaging, retries, orchestration, error handling, and service composition.

For us, that means we can focus on the AI capability and business process instead of repeatedly rebuilding the integration plumbing.

Visit the Apache Camel project

Enterprise Integration Patterns we lean on

  • Routing
  • Transformation
  • Messaging
  • Retries
  • Orchestration
  • Error handling
  • Service composition

Connectors for the systems enterprises already run.

A selection of the Camel components we reach for most in enterprise workflows. Anything with an HTTP, REST or GraphQL API is covered too.

Business applications

ERP, CRM, HR and service management

Messaging & events

Brokers, queues and event streams

Databases & change data capture

Relational, document and search stores

Files, B2B & legacy

File drops, partner exchange and mainframes

Identity & secrets

Directories, SSO and credential vaults

AI & documents

Models, extraction and vector search

Browse the full Apache Camel component catalogue

Enterprise-backed, without vendor lock-in.

Apache Camel is vendor-neutral and governed by the Apache Software Foundation, but enterprises that require commercial support can use the Red Hat build of Apache Camel.

Red Hat provides a supported distribution of Camel for Spring Boot, Quarkus, and OpenShift, with enterprise lifecycle support, tested configurations, security updates, and production support. Red Hat also positions Camel for AI/ML data ingestion, API-driven processing, event-driven workloads, transformation, and service composition.

You are not locked into a proprietary integration platform, but you still have a path to vendor-backed support when governance, compliance, or procurement requires it.

Red Hat build of Apache Camel
Same integration architectureAdd commercial supportApache CamelOpen source · Apache 2.0Red Hat build of Apache CamelCommercial enterprise support
DrawingTwo ways to run CamelSheet1 of 3

AI models will change quickly. The integration layer should not have to.

A workflow may use OpenAI today, Claude tomorrow, and a private or specialized model for sensitive workloads. Camel keeps those AI capabilities loosely coupled from the rest of the enterprise architecture.

The advantages are practical

  • Integrate with existing systems rather than replace them.
  • Keep deterministic business rules around probabilistic AI.
  • Support synchronous or event-driven architectures.
  • Deploy on cloud or on-prem infrastructure.
  • Keep integrations in normal source control rather than inside a proprietary workflow platform.

For organizations already invested in Red Hat technologies, Camel fits naturally with OpenShift, Spring Boot, and Quarkus, with commercial support available from Red Hat.

Enterprise systemsSAP · CRM · Kafka · APIs · DatabasesApache CamelIntegrationRAI workflow +business rulesAI101AI102AI103ClassifierLLMAI agentPolicy / human approvalEnterprise systems
DrawingWhere Camel sitsSheet2 of 3

Ships as a container. Runs wherever you do.

Every workflow, including its Camel routes, AI steps and configuration, is packaged as a standard Docker image. The same image runs on AWS, Azure or Google Cloud, on any Kubernetes platform including OpenShift, or on a plain Docker host in your own data centre.

  • Build once: the same image moves from test to staging to production.
  • Configuration and secrets are injected at deploy time, never baked into the image.
  • Scale with the platform you already run: containers, pods or serverless containers.
  • Move between clouds, or back on-premise, without rewriting the workflow.

No proprietary runtime, no hosted platform you have to move your data into. The workflow runs inside your infrastructure, under your security controls.

Build once · deploy anywhereDocker imageCamel routes · AI workflow · configAny container platformAWSECS · EKS · FargateAzureContainer Apps · AKSGoogle CloudCloud Run · GKEKubernetesOpenShift · any distroAny Docker hostVM · bare metalOn-premisePrivate or air-gapped
DrawingDeployment modelSheet3 of 3

Apache Camel gives us the openness of an Apache project, the maturity of a proven enterprise integration framework, and the option of Red Hat-backed support when organizations need it.

Bring the problem and the decision that is stuck.

In one hour we look at the workflow, the systems around it, and whether AI is the right tool, and leave you with a clear next step.

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