File size: 3,297 Bytes
d69677e 0579f93 d69677e 0579f93 d69677e 0579f93 4384607 8f50e90 0579f93 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | ---
title: README
emoji: 🟡
colorFrom: gray
colorTo: yellow
sdk: static
pinned: false
---
# 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)** — the returned gradient exposes the real rows in every audited frame. [alphaXiv:2609.04382](https://www.alphaxiv.org/abs/2609.04382)
- **[What Gradients Add to Text Leakage in Split Language Models, Counted per Token and per Document](https://huggingface.co/papers/2610.04128)** — measured per word the gain is modest; measured per whole document it triples. [alphaXiv:2610.04128](https://www.alphaxiv.org/abs/2610.04128) · [reproduction package](https://huggingface.co/datasets/Setloop/What-Gradients-Add-to-Text-Leakage-in-Split-Language-Models-Counted-per-Token-and-per-Document)
- **[The LLM-Attacker: A Unified Red-Team Framework for Privacy Evaluation of Split and Distributed LLMs](https://huggingface.co/datasets/Setloop/The-LLM-Attacker-Unified-Red-Team-Framework-for-Privacy-Evaluation-of-Split-and-Distributed-LLMs/tree/v1.0)** — the evaluation framework behind both studies: combined manuscript, code and full evidence release (v1.0); arXiv preprint in submission.
## 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)**
|