The challenge
Every AI provider is good at something different, and the best model for a task changes month to month. Meanwhile, the most common blocker we hear from European businesses is the same: "we can't send this data to a US cloud." We wanted one assistant that could use many models — and could run entirely on local hardware when the data demands it.
What we built
- A multi-model orchestrator that routes requests across multiple AI providers and locally-running open models, from one interface.
- A web interface for day-to-day use — chat, tasks and tools in the browser, served from the same machine.
- A local-first mode: open-source models running on our own GPU workstation, so prompts and documents never leave the building.
- Fallback logic: if a provider is down or unsuitable, the orchestrator reroutes instead of failing.
How it works
The orchestrator sits between the user and the models. It decides per task which engine fits — a fast local model for routine work, a stronger cloud model where allowed and needed — and keeps configuration, keys and routing rules in one controlled place. The same pattern scales down to a single workstation or up to a company server.
Why this matters for your business
This build is our own R&D, and it is the exact architecture we deliver to clients who need AI without shipping their data abroad: EU-hosted or fully on-premise, open models where privacy requires it, cloud models where policy allows it — behind one interface your team actually uses.
Privacy-first is not a slogan we put on a slide. It's infrastructure we run ourselves, every day.