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# Setloop

### We build the infrastructure that takes AI from the lab into production.

**Engineering consultancy · AI infrastructure products · Applied research**

[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)

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.

## What we do

| Consultancy | Products | Setloop Lab |
| --- | --- | --- |
| 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. |

## Products and platforms

- **[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.

## Research and open artefacts

Setloop Lab investigates privacy and security in distributed AI. We publish papers, datasets and reproduction artefacts so that findings can be inspected and extended.

- **[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.

## Work with us

Planning AI infrastructure, evaluating a platform or moving an AI system into production? **[Tell us what you are building →](https://setloop.io/contact)**