VERONICA
An AI operating environment for work that cannot cross into third-party inference. Every model in the path is Senon Solutions' own, and confidential material is stripped before any request leaves.
Cognition, voice, and computer use all execute on Senon Solutions' own model family, served through SGLang over its own network, so confidential material never reaches a third-party inference endpoint.
Core capabilities
- Execute confidential company and client work without egress to third-party inference endpoints
- Run long-horizon background missions concurrently with the foreground conversation
- Drive desktop applications through computer-use models under approval-gated execution
- Maintain scope-isolated persistent memory across users, projects, and artifacts
- Enforce deny-by-default capability grants per user, per device, and per feature
- Coordinate specialised models, AI councils, and hierarchical agent execution
- Fail over to a reserve model fleet through FleetRouter when the GPU tier is unreachable
Why it exists
Confidential company and client material cannot be sent to a third-party inference endpoint, which disqualifies every hosted assistant regardless of capability. VERONICA occupies that gap: an operating environment for running the company, coordinating confidential projects, and executing across multiple domains concurrently, with capability grants issued per user and per device.
The intelligence layer
Cognition runs on the Senon AI family rather than external APIs. Thread-Ripper 1.2 is a sparse mixture-of-experts model at 1.1 trillion total parameters with 32 billion active per token, across a one-million-token context window; Thread-Ripper 3.5 carries 1.6 trillion total with 49 billion active at the same context length. Both are fine-tuned from open-source bases. Capybara STT and Capybara TTS handle the voice path, and Platypus pairs with Capybara Vision for computer use. FleetRouter, an in-house AI gateway, holds a reserve fleet for failover when the GPU tier is unreachable.
Where it runs
Models serve through SGLang on Modal GPU capacity, chosen for concurrency headroom, scaling to zero when idle and returning within roughly 45 seconds of the first request. M-Core Engine handles model execution, M-Core Autoscaler adjusts origin capacity against traffic, and requests travel over Senon Cloud. Every model in the path is Senon Solutions' own, so no third-party model ever sees the work, and a privacy preprocessing layer rewrites each conversation in full on every request to strip confidential material before it leaves. That layer is the dominant latency cost in the path and is accepted as such.