Service · Managed service
P10Managed Private AI.
The private deployment is live. Someone has to run it — the monitoring, the patches, the adapter refresh, the usage governance, the quarterly evaluation runs. This is that, with a named lead and a real SLA.
Outcomes
What changes when the engagement lands.
Stack that stays healthy
Monitoring, patching, and adapter refresh handled without pulling engineering off the roadmap.
Quality that doesn't drift
Quarterly evaluation runs against the original harness. Drift caught, not discovered.
Governance sustained
Usage policy, access controls, and audit posture reviewed on cadence.
One accountable lead
Named contact who owns the operation. Not a shared ticket queue.
Deliverables
What's in the engagement.
Ongoing operation of a private AI deployment — monitoring, patching, adapter refresh, usage governance, quarterly evaluation runs, named support with SLA.
Monitoring and alerting
Continuous monitoring. Named-contact alerting for material events.
Patching cadence
Regular patching against tested update procedure. Change management preserved.
Adapter refresh
New adapters trained and evaluated on request. Included up to a defined threshold.
Quarterly evaluation
Structured evaluation run every quarter. Drift and quality change reported.
Named support with SLA
One lead. Response and resolution targets in the contract.
How we deliver
Fixed scope. Named phases. Duration on the cover.
The engagement is priced against the outcome, not open-ended hours. Every phase has a duration, a named deliverable, and a check-out.
Total durationOngoing monthly
Sales cycleFollow-on from a delivered engagement
- 011 to 2 weeks
Onboarding
Access, runbook confirmation, cadence and SLA agreed.
- 02Monthly
Ongoing operation
Monitoring, patching, adapter refresh, governance oversight, quarterly evaluation.
Built for
Buyers this engagement fits.
Typical buyer
CTO, CIO, Head of Infrastructure
Clients whose private deployment is live and needs sustaining
The build finished. This is what keeps it working.
CIOs who don't want AI operation to become a headcount problem
The team that built it operates it. No hiring cycle.
Related services
Where this leads next.
Private LLM Deployment
Inference stack on your infrastructure. Optimisation. Identity, network, observability. Runbooks.
Production Fine-Tune & Deployment
Reproducible pipeline. Multiple adapters. Serving stack. Guardrails. Provenance. Client IP.
Air-Gapped Deployment
Everything in P8, plus offline install and update. Dependency mirror. Isolation validation. DR.
Talk to us.
45 minutes on your operation and the engagement you have in mind. No pitch, no deck.
By briefing only
This engagement is scoped one to one.
This engagement requires a delivery team assembled against your specific scope. Every engagement starts with a direct conversation, not an open form. Email us and we'll route it to the right lead.