AI Orchestration: One Layer for Your Entire AI Landscape
Instead of separate AI tools sitting side by side: an orchestration layer that connects, steers and makes models, data sources and workflows traceable. Vendor-neutral and GDPR-compliant.
The problem
Why individual AI tools aren't enough
The more AI tools you have in use, the harder it gets to keep track. Without a connecting layer, you end up with isolated solutions.
Isolated solutions
Every team uses a different tool, and nothing fits together.
No central data access
Models have no governed access to your company knowledge.
No control
Who uses which model with which data? Unclear.
Risk of lock-in
Bet everything on one vendor and it's hard to get back out.
The solution
What an orchestration layer does
One layer connects models, data and workflows and makes them controllable.
Connect models
Different LLMs and embedding models behind one interface, interchangeable.
Connect data sources
Governed, permission-aware access to your company knowledge via RAG.
Steer workflows
AI steps are chained into traceable, repeatable processes.
Routing and fallbacks
The right request to the right model, with fallback options.
Governance and monitoring
Who did what with which model: logged and traceable.
Cost control
Usage and costs in one central view.
Vendor-neutral and in your hands
No dependence on a single vendor
The core of good orchestration is interchangeability. Models and vendors can be swapped out without rebuilding everything.
- Models and vendors interchangeable, no lock-in
- On-premise or in your own cloud
- GDPR-compliant, no uncontrolled data outflow
- Roles and permissions enforced centrally
Use cases
Where orchestration helps
Multiple AI use cases
Different applications share one platform instead of each running its own stack.
Knowledge plus action
Answers drawn from company knowledge that trigger processes.
Switching vendors
Swap models out without rebuilding everything.
Compliance requirements
Central control over data and usage.
What you can connect
Open to your stack
The orchestration layer is vendor-neutral and connects to what you already have.
- LLMs: proprietary and open source
- Data sources: SharePoint, file servers, DMS, databases
- Workflows and line-of-business systems via API
- Identity and permissions
How it works
From landscape to platform
Assessment
Which AI use cases, models and data sources do you have today?
Architecture
We design the orchestration layer, vendor-neutral.
Build and operation
A step-by-step build, then operation with monitoring.
FAQ
Common questions about AI orchestration
What does AI orchestration mean?
A layer that connects and steers models, data sources and workflows, rather than running individual tools side by side.
Are we tied to a single vendor?
No. Models and vendors are interchangeable, and that's the core of good orchestration.
Can it run on-premise?
Yes, on-premise or in your own cloud, with no uncontrolled data outflow.
Do we need a finished AI strategy first?
No. We start with an assessment and set priorities together.
How does this relate to RAG?
RAG is the knowledge access, and orchestration connects it with models, workflows and governance.
Turn AI tools into a platform
Book a free intro call. We'll take a look at your AI landscape and show you the path to a platform you can steer.
Let’s talk about how AI can move your business forward.
Get in touch, we look forward to your project.