Oracle Integration Cloud services and AI

We also show you how to make the integrations you already run available to AI safely, without rebuilding them.

MCP for AI

Most organisations already have integrations that expose exactly the actions an AI assistant would need. We wrap existing OIC integrations as governed MCP tools that assistants such as Claude, ChatGPT and Oracle Fusion agents can use. Each tool carries trusted sign-in, a tool policy, an approval step for anything that writes data and a full audit trail. You reuse what you have instead of building a second set of connections for AI.

Integration design and build

We design application, data and process integrations across Oracle and non-Oracle systems. Good design starts with the business event, such as a new customer, an approved invoice or a shipped order, and works back to the systems that must react. We choose the right pattern for each case, build it with the adapters OIC provides and document it so that your team can support it.

Migration and modernisation

Many organisations carry legacy integrations that few people understand and nobody wants to touch. We assess them, retire what is no longer used and move the rest to OIC, with clear monitoring and error handling so that a failure is noticed and explained rather than discovered by a customer.

Process automation

Some business processes cross several systems and several people. We automate them, so that a request moves from one step to the next with the right approvals, and so that you can see where anything is waiting.

Managed services

After go-live we monitor integrations, support them and improve them. That includes watching for failures, handling errors, keeping up with Oracle's updates and adjusting integrations as your applications change.

Why integration comes first for AI

An AI assistant is only as useful as the systems it can reach, and only as safe as the controls around that reach. Integrations already contain your business rules, your security and your error handling. Making them available as governed tools means AI works inside those rules instead of around them.

Patterns we use

We use the integration pattern that fits each case: real-time integrations that react to an event, scheduled integrations that move data in batches, file-based integrations for large volumes, and orchestrations that coordinate several systems. Choosing the right pattern matters more than the tool.

Security and governance

Integrations carry business data, so security is part of the design: credentials held safely, access limited to what each integration needs, and monitoring that shows what was called and by whom. For AI tools we add policy and approval steps on top.

What you get

Working, monitored integrations, documentation your own team can read, naming and error-handling standards that make the next integration easier, and a clear view of what is running and what is failing. You also get an owner for each integration, so that nothing is orphaned.

Monitoring and support

Integrations fail quietly unless someone is watching. We set up monitoring, alerts and clear error messages, agree who responds to what, and keep a record of failures and fixes, so that an issue is fixed once instead of repeatedly.

How we work

We begin by listing the integrations you run today and the ones you need. We then agree standards for naming, security, monitoring and error handling, build in small steps, test with realistic data and hand over documentation your own team can use.

Where customers start

Customers usually come to us when they are moving applications to the cloud and need them connected, replacing older integration tools, struggling to see why integrations fail, or planning how AI will reach their systems.