What it looks like to move real AI workloads inside the network — cutting cloud bills, standing up GPU inference fast, and taking routine work off people, with the data staying home.
Nocodo LTD was paying roughly $10,000 a month for OpenAI's API. We moved them to a custom, tuned open-weight model on their own infrastructure — with intelligent request routing and GPU optimization — so every AI operation stays in-house and compliance overhead dropped ~60%.
Read the case studyInfiano's hackathon had 240 submissions to score in a week. We stood up ephemeral, self-hosted inference on 40 rented GPUs running a custom 20B open-weight model — about $3,000 versus ~$6,750 on a cloud API, with zero infrastructure-caused restarts and time-to-first-token held at 1.1–1.4s across all 40 GPUs.
Read the case studyA worked example drawn from a real contractor portal we reviewed under NDA: an on-prem cascade of open-weight models (Qwen3-VL, PaddleOCR-VL, gpt-oss-120b) handling seven routine jobs, with a transparent, clearly-labeled projection of the payroll impact — not a measured client result.
Read the walkthroughWe get open-weight models running on-premises in 72 hours — with complete data sovereignty.
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