The mechanism
From your data to your model. Watch it happen.
This is the state-of-the-art pipeline we run for every deployment — scroll, and each stage lights up in the order it executes. Nothing in this diagram touches the public internet.
01 · Collect & refine
Your ERP records, documents and resolved tickets are cleaned, deduplicated and pseudonymized — on your hardware. An expert from your team approves what enters training. Garbage stays out by process, not luck.
02 · Two products from one corpus
Knowledge that changes weekly goes into a vector index for retrieval. Behavior — your tone, formats, decision patterns — is trained into a LoRA adapter (~60 MB) on a frozen open-weight base. Watch the loss curve drop: that's the model fitting your company.
03 · Gate, seal, predict
No model passes without beating the golden-set evaluation; failures loop back to training automatically. What passes is sealed inside your VPN with outbound-deny firewall proof. The optional world-model twin learns your line's normal physics from camera data and flags trouble seconds before it happens.
The use case behind the last box: a packaging line that flinches first
World models — neural networks that learn the dynamics of the real world, its physics and spatial behavior, from video and sensor streams — are the most underrated near-term tool for industry. Our reference deployment: a cartoning line that jams 3–5 times per shift. A compact world model, fine-tuned on six weeks of the line's own camera footage, continuously predicts the next 2–3 seconds. When reality diverges from prediction — a glue flap lifting where the model expects it flat — that prediction error is the alarm. The PLC slows the feeder; the jam never forms. One industrial GPU at the line, zero cloud, and the most operationally sensitive footage in the plant never leaves it. Read the full world-models guide →
Discovery workshop, data review, training, evaluation, sealed handover — we've run it enough times that it's a checklist, not an adventure.
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