The problem, today

Adopting AI in the enterprise means giving something up — whichever door you take.

Proprietary cloud

Quality, modern agents, a real product experience. But your prompts, your files, your knowledge base leave the building. You are locked to one vendor's catalog. Every request is one more thing to justify to your DPO.

Local AI, as it stands

Your hardware, your weights, nothing leaves. But too often a chat box and little else. No real memory, no agents, models capped by a single machine's RAM. The experience of 2023.

Between data leaving and local tinkering, the third door was missing.

The honest objection

You're thinking local means slow, weak, and fragile. Here are the numbers.

6 nodes

It runs the big models.

Inference distributed across up to six Apple Silicon nodes over Thunderbolt 5 RDMA. One Mac Studio runs a serious model; six run what the cloud runs — up to 1T parameters in Q8.

94.4%

Within one point of the frontier.

Qwen 3.5 397B in full BF16, on a 4-node cluster, measured against Claude Opus as the ceiling. 16 local configurations tested — real per-task scores, not a generic leaderboard.

0

It stays up.

One node, unattended, continuous: 96h 44m uptime, 23,549 inferences served, zero failed requests. Two models in parallel. No Docker, no Python, no babysitting.

The doubt is legitimate. The numbers answer it.

Where it stands

This is not a roadmap. It runs today.

OdyssAI-XIn production
CompanionIn production
TelemakIn production
DeliveryInstalled on-prem, supported
OriginBuilt in Europe, for European regulatory reality
The boundaryYour hardware. Everything above it is yours.

The only question an enterprise really asks is whether it will still be running on Monday. It will.

hello@odyssai.eu · Answered from Europe, in French, English, or Spanish.

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