Aleksander Cichucki@rejonmosciska15938

User and problem: a machining plant's maintenance planner turns machine alarms into tickets by hand, late, and cannot later prove which system produced which advice. With AI writing tickets, the model must never decide whether a machine is at risk, and every piece of advice must trace back to the machine that produced it. SIMON (Sovereign Inference & Model Orchestration Network): your models, independent nodes, inference on demand. A peer-to-peer inference network (Rust, libp2p, MIT) for a company's own GPUs plus cloud GPUs. Every job returns an Ed25519-signed receipt bound to the exact output, verifiable offline, also in the browser (Rust verifier in WebAssembly). OpenAI-compatible API. Live product (/industry): rules from the AI4I 2020 milling-machine physics flag the risk and the failure mode; Gemma 4 26B-A4B drafts the ticket and picks inspection steps by ID from the plant's catalog, never writing one; every ticket is checked (fields, modes and urgency equal to the rules; no new numbers, links or role markers); any problem holds it for a person; the GPU node signs it (key pinned by the gateway). Predeclared sample: 14 of 15 published, 1 held (text raised the urgency), median 1.6 s, receipts all valid: 8 on the plant's RTX 3090, 7 on an AMD Instinct MI300X (vLLM, ROCm, AMD Developer Cloud; live on /network). Measured improvement: on our adversarial corpus the unsafe-draft escape rate fell from 7 of 12 (first, keyword-based validator) to 0 of 15 after the redesign. This is a software safety metric, not yet a measured plant outcome. MI300X benchmark (Qwen3.8-27B BF16, vLLM): 64 concurrent streams; SIMON added no measurable throughput cost (510/510 receipts verified). In our pre-registered cross-vendor test the audit flagged the same 5 of 32 prompts on the MI300X as on an RTX 3090 checked against itself: no penalty for the GPU vendor. Limits: a receipt proves who signed an output, not which model ran; AI4I is synthetic; a technician reviews every ticket.

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