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| title: README |
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| # Setloop |
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| ### We build the infrastructure that takes AI from the lab into production. |
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| **Engineering consultancy · AI infrastructure products · Applied research** |
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| [Website](https://setloop.io) · [Services](https://setloop.io/services) · [Setloop Lab](https://setloop.io/lab) · [GitHub](https://github.com/setloop-io) · [Contact](https://setloop.io/contact) |
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| Setloop helps teams design, build and operate production AI systems. Our work spans private and sovereign AI, GPU platforms, inference, training, security, observability and cost control. |
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| ## What we do |
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| | Consultancy | Products | Setloop Lab | |
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| | Architecture reviews, implementation plans and engineering support for production AI. | Tools for operating GPU capacity, controlling AI spend, securing agents and automating operations. | Applied research into privacy and security risks in distributed AI. | |
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| ## Products and platforms |
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| - **[GPU Cloud](https://setloop.io/gpu-cloud-platform)** — secure GPU rentals, deployments, private clusters and distributed training. |
| - **[AI FinOps](https://setloop.io/finops)** — token-level cost visibility connected to product work and customer outcomes. |
| - **[LLMTrace](https://setloop.io/llmtrace)** — security, policy controls and full-fidelity observability for production AI agents. |
| - **[ProofSeam](https://setloop.io/proofseam)** — evidence-led security assessments for AI workflows and model privacy. |
| - **[AutoOps](https://setloop.io/autoops)** — governed autonomous SRE for AI infrastructure and Kubernetes platforms. |
| - **[Automatic RL Research](https://setloop.io/autoresearch-rl)** — bespoke closed-loop systems for long-running reinforcement-learning experiments. |
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| ## Research and open artefacts |
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| Setloop Lab investigates privacy and security in distributed AI. We publish papers, datasets and reproduction artefacts so that findings can be inspected and extended. |
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| - **[Privacy Failure in Split-LLM Training: The Returned Gradient Nullifies the Decoys](https://huggingface.co/papers/2609.04382)** |
| - **[Paper A reproduction package](https://huggingface.co/datasets/Setloop/llm-attacker-paper-a)** — privacy, utility and delegation feasibility in split language models. |
| - **[Paper B reproduction package](https://huggingface.co/datasets/Setloop/llm-attacker-paper-b)** — a unified red-team framework for split and distributed LLM systems. |
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| ## Work with us |
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| Planning AI infrastructure, evaluating a platform or moving an AI system into production? **[Tell us what you are building →](https://setloop.io/contact)** |
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