Who this is for
- A local or hybrid stack already in production — including ones we did not build
- Teams that do not want a full-time inference person on payroll
Who this is not for
- “Host our model in your cloud” — we are not an LLM-as-a-service
- 24/7 NOC theater we will not staff
- Prompt logs shipped to a third party
What we deliver
- Scheduled maintenance windows for runtime, driver, and OS updates
- Model refresh reviews: evaluate newer open-weight releases against your benchmarks before upgrading
- Quality eval on a frozen prompt set after every runtime or model bump — the thing apt does not catch
- Continuous monitoring of uptime, latency, throughput, and output quality signals
- Incident response with a defined path and root-cause writeups
- Quarterly capacity and cost reviews as your usage scales
How an engagement runs
- Audit. What exists, what is risky, what we will and will not touch. Required even on stacks we built.
- Plan. Monthly or quarterly cadence. No invented Gold/Silver SKUs — the scope is written down.
- Operate. Updates, evals, incidents. Telemetry that does not exfiltrate prompts.
Stack we actually use
- llama.cpp / vLLM / Ollama / LiteLLM — whatever you already run
- privacy-respecting health metrics
Tested on our fleet
Dated lab notes — not a client logo wall.
Related services
Common questions
Why does AI infrastructure need ongoing maintenance?
The open-model ecosystem moves monthly, GPU drivers and inference runtimes update constantly, and silent regressions (worse answers after an upgrade, growing latency under load) are common. Without active management, systems decay even when nothing visibly "breaks."
Do you offer remote monitoring for on-prem systems?
Yes — lightweight, privacy-respecting monitoring that reports health metrics without ever shipping your prompts or documents off-site. You choose exactly what telemetry leaves the building.
Can you take over maintenance of a system someone else built?
Usually, yes. We start with an infrastructure audit documenting what exists, its risks, and a prioritized remediation plan — then maintain it going forward.
Is this a hosted LLM product?
No. The models stay on hardware you own. We operate and advise. If you need someone else to host the weights, that is a different vendor.
What is in a typical month vs a break/fix call?
A plan covers scheduled updates, the post-change quality eval, and a defined incident path. Break/fix without a plan is available after an audit; it is slower and more expensive when something is already on fire.
What telemetry leaves the building?
Only what you approve — typically uptime, latency, and error rates. Prompts and documents stay put. The hardening guide on this site is the pattern we follow.