We build the infrastructure that takes AI from the lab into production.
Setloop combines engineering consultancy, AI infrastructure products and applied research. We design and build production AI systems, develop tools to operate them, and investigate privacy and security risks through Setloop Lab.
Keep control of your AI infrastructure.
We help enterprises reduce dependence on a single provider, control access to their data, run private multi-node clusters and route workloads according to cost. Our work covers private, sovereign and hybrid AI deployments.
We help leadership and engineering teams decide what to build or buy and identify the requirements for launch. The proprietary Setloop GPU Maturity Framework structures our assessment of hardware adoption.
Improve GPU utilisation and control inference costs.
We tune scheduling, dynamic batching and KV caching to reduce idle GPU time. We evaluate changes against the workload’s latency, throughput and cost targets.
Our engineering references include the vLLM paper (Kwon et al., 2023) (arXiv) and the NVIDIA TensorRT-LLM optimization guides. We assess the techniques against each deployment’s requirements.
Engineering services for production AI.
Architecture reviews, implementation plans and engineering support for private AI, GPU clouds, security, operations and research automation.
GPU Workload Advisory
Architecture advice for training, fine-tuning, inference, RAG, agents, batch workloads, and GPU-heavy applications.
GPU Cloud Platform Design
Guidance for companies building rental platforms, private GPU clusters, deployments as a service, and distributed training services.
AI Infrastructure Architecture
Workload-first decisions across GPU selection, fabric, storage, orchestration, tenancy, observability, security, and cost.
Private AI & Technical Evaluation
Private and sovereign AI architecture plus independent review of vendors, startups, procurement plans, and platform claims.
Automatic RL Research
Bespoke closed-loop RL research systems for long-running experiments, evaluation guardrails, GPU execution, and traceable model improvement.
Support across the AI infrastructure lifecycle.
Explore our services for planning, building and operating AI infrastructure.
GPU Cloud turns capacity into a secure commercial platform.
We design and build GPU platforms using our own tools. GPU Cloud brings rentals, deployments, private clusters and distributed training together with access controls, usage billing and workload recovery.
AI FinOps connects model spend to product value.
AI FinOps tracks token-level costs and connects provider spending to product work and customer outcomes. Finance, engineering and product teams can see where AI spending goes and assess its value.
LLMTrace secures and observes production AI agents.
LLMTrace detects prompt injection attempts, potential PII leaks and anomalous requests in agent traffic. Traces, alerts and policy controls help teams investigate findings and manage production risks.
Evidence for your next AI security decision.
Investigate how an AI workflow can fail, compare defences and build an evidence package your team can act on. ProofSeam connects application and agent testing with model privacy evaluation through specialist-led assessments.
Scope
Define the system, attacker access and decision.
Compare
Measure exposure alongside task quality and compute cost.
Decide
Review findings, remediation priorities and open questions.
AutoOps delivers governed autonomous SRE.
AutoOps turns telemetry into diagnosis, remediation plans, and auditable operational evidence for AI infrastructure and Kubernetes platforms.
Automatic RL Research automates RL experiment loops.
Automatic RL Research is a proprietary bespoke consultancy service for AI teams running long-lived research campaigns. An LLM proposes hyperparameters or code changes, training runs locally or on cloud GPU, results are evaluated, and the loop keeps improving runs while discarding weak ones.
Investigating privacy and security in distributed AI.
Setloop Lab tests the assumptions behind private AI infrastructure and publishes its findings. The research examines observable data channels, the cost of privacy mitigations and methods for evaluating distributed AI protocols.
Privacy Failure in Split-LLM Training: The Returned Gradient Nullifies the Decoys
Our split-LLM training research examines whether decoy-based privacy defences hold when an attacker can observe the returned gradients. Read the findings and access the paper through Setloop Lab.
Questions about Setloop.
Setloop combines engineering consultancy, AI infrastructure products and applied research. We design and build production AI systems, develop tools to operate them, and investigate privacy and security risks through Setloop Lab.
Discuss your infrastructure, product or research needs.
Send a short brief to [email protected] to discuss consultancy, our products or Setloop Lab research.