Orpius Platform

Workflow tools such as Zapier, Make, or n8n focus primarily on connecting services and triggering short-lived automation steps. AI agent frameworks such as LangChain or AutoGen focus on building agent logic within application code and often require engineering teams to design and maintain those implementations. These tools usually follow predefined workflows. Orpius, on the other hand, allows agents to adapt and coordinate work dynamically. Orpius is designed to own and run AI-driven operational work over time, not just connecting services or building agent code. Tasks can be defined by anyone who can describe the outcome they want to achieve, allowing teams to set up the tasks once and have Orpius continue running them automatically.

Orpius includes workflow capabilities, but it moves away from fixed, predefined automation processes. You simply describe the task you want completed. If you ask Orpius to run something every day at 9:00, it will create and manage the schedule for you. The task then runs automatically, and you can update it whenever needed. Agents can also reason, delegate specialised work, and adapt as tasks change.

No. Orpius is an external managed execution platform, not a library or framework. While frameworks (like Microsoft’s MAF) provide code-level patterns you must stitch together and host yourself, Orpius provides a pluggable environment that runs outside your application. You can think of it like a database: you don’t build the engine or manage the storage logic; you simply connect to it. By offloading the "plumbing" (orchestration, state management, security, and observability) to Orpius, developers can focus entirely on business logic rather than infrastructure.

It’s both. One execution engine serving two audiences. Anyone can use Orpius to define and run automated tasks that operate over time. At the same time, developers can integrate Orpius into applications as an execution platform for AI-driven workflows without needing to build and maintain complex orchestration infrastructure. It is a single platform that powers both operational automation and application integration, serving different types of users through the same underlying execution engine.

No. Once a task is set up, Orpius runs it independently. You can close the Console or shut down your device and the task will continue running on the Orpius platform. The Console is simply the interface used to configure and monitor work. The actual execution happens on the Orpius server. Once you hit 'send,' the work moves to our secure, autonomous execution environment. You can close the app, shut down your computer, or go on vacation. The AI co-worker stays on the job and keeps you updated via email.

You’re in control. Just tell the agent when you want to be notified. For example, you can ask to be notified when work is complete, when no new files are found, or if the AI needs your input.

No. You talk to Orpius just like you would a human co-worker. If you can describe a task, you can use Orpius.

No. Orpius is a sovereign, standalone platform engineered from the ground up. It does not depend on external workflow engines, orchestration frameworks, or LLM toolkits. This ensures we can guarantee security, reliability, and long-term maintainability.

Orpius is hosted in Switzerland by default. It can also be deployed on-premise in your own infrastructure, or in your cloud environment. The platform is designed to run anywhere you need it.

Orpius is designed to work with any model, whether hosted on-premise or in the cloud. If you use an LLM that isn’t currently supported, simply contact us and we'll do our best to add support for it.

You can connect directly through your existing services or APIs. Orpius doesn't require direct database access, instead, your workflows can call your application layer or dedicated endpoints that interact with your DB securely. This approach preserves your existing architecture and security controls while still giving agents access to the data they need.

Yes. Orpius makes it easy to switch between models at any time. You can experiment with different LLMs, compare performance, or reduce costs by seeing whether a more affordable model can handle the task just as well. Switching requires no code changes or redeployment.