The problem
Your team has an AI prototype that works on a laptop. Production is different: the model has to be served fast and at a predictable cost, tools have to be exposed to agents without opening the whole company to them, and someone has to be able to show that the answers are still good next month.
What we do
- GPU and LLM inference: serving open models with vLLM or Triton on Kubernetes, autoscaling on queue depth, cost per token measured, and a plan for the day a GPU node fails
- MCP servers and gateways: your internal systems exposed to AI agents with authentication, per-tool permissions, rate limits and an audit log, so you can answer who called what
- LLMOps: deployment, observability, tracing and rollback for model-backed services, the same discipline as any other production system
- AI Oversight and Evaluation: an evaluation harness that runs against every change, quality gates in CI, and a monthly review of results, so a model or prompt change cannot quietly make things worse
- Governance basics: logging and human-oversight points that support your EU AI Act duties, including AI literacy and transparency
Outcomes
- Inference that is fast enough, with a cost per request you can state
- Agents that reach only the tools you decided they should reach
- A test suite for AI behaviour, not just for code
- Evidence you can show an auditor or a customer
Get in touch to talk about what you want to run.
Ready to work with us on this?
Let's discuss how we can help with your ai infrastructure.