Support & Maintenance
Keep your private AI healthy, current, and secure.
Cloud, on-prem, or at the edge.
Same model, same governance, same control plane — sized and operated for the environment that fits your security, latency, and cost profile.
- On-prem for full data sovereignty
- Private cloud (AWS · Azure · GCP) for elastic scale
- Edge for offline + low-latency environments
Ongoing model updates, monitoring, evaluation, and support so your deployment stays performant and compliant as your needs — and the model landscape — evolve.
What you get
We design, deploy, and operate private LLM infrastructure for organizations that need full control of the model and the data flowing through it.
From a single pilot to a multi-tenant platform, you get a deployment sized to your hardware, optimized for your models, and operated against the SLA your business actually needs — with handoff or co-operation, your choice.
Common questions
01Where can the platform be deployed?
On-prem inside your data center, in your private cloud (AWS, Azure, or GCP), in air-gapped/offline environments, or on edge hardware. Hybrid setups can route sensitive workloads to private models while still tapping frontier APIs for non-sensitive tasks.
02How do you handle updates?
Models, retrieval indexes, and orchestration are versioned and updated on a cadence you control, with rollback. You decide when, on what cadence, and against what evaluation suite.
03What does ongoing support look like?
We monitor and respond on the SLA you need — from business-hours to 24/7. You can also pair our team with your internal platform engineers for joint operation.
04What SLA options are available for managed LLM support?
We offer tiered SLAs ranging from next-business-day response for non-critical workloads up to 24/7 coverage with defined response and resolution windows for production inference services. SLA terms are defined per environment — a development cluster and a customer-facing deployment typically operate under different agreements. SLA commitments are documented in the support contract before go-live.
05How is model drift detected and remediated?
Our observability stack runs continuous quality evaluations against a benchmark suite agreed with your team at onboarding. When output metrics fall outside the defined tolerance band, an alert triggers a review cycle. Depending on the root cause — prompt regression, data distribution shift, or base model change — remediation may involve prompt updates, fine-tune refresh, or a staged model rollback. All changes go through an evaluation gate before promotion to production.
06Does the support contract cover security patching for the underlying infrastructure?
Yes. Managed support includes patching for the inference runtime, container orchestration layer, and host OS. For on-prem GPU hosts, this extends to driver and firmware updates. Patches are tested in a staging environment first and promoted on a schedule compatible with your change-control process. Critical security patches are expedited under a separate fast-track procedure defined in the support agreement.
07Can we maintain control over when model updates are applied?
You retain full authority over the update schedule. Model updates — whether a new checkpoint, a revised adapter, or a base model upgrade — are staged in a shadow environment and validated against your evaluation suite before any production change. You approve the promotion window. Rollback to the previous version remains available throughout the maintenance cycle.
08How does incident response work for AI-specific failures like guardrail breaches or unexpected output behavior?
AI incidents require a different response path than traditional infrastructure outages. Our runbooks cover initial containment, stakeholder notification within the contracted SLA window, root-cause analysis, and staged remediation with evaluation gating before the fix goes live. Each incident closes with a written post-incident report covering timeline, contributing factors, and any control updates applied — documentation suitable for internal review or external audit.
What Managed Support Covers
A production LLM deployment is not a set-and-forget asset. Our managed support contracts cover the full operational surface: uptime monitoring and alerting, security patching of inference runtimes and underlying OS layers, model version management with controlled rollout and rollback, retrieval-index freshness for RAG pipelines, and regular evaluation runs against your agreed quality benchmarks. Support tiers range from business-hours response to 24/7 incident coverage, with SLAs defined per environment and workload criticality.
For organizations running on-prem or hybrid infrastructure, we also handle firmware and driver updates for GPU hosts, network segmentation reviews, and encryption-at-rest verification — the patching disciplines that are straightforward in managed cloud but often under-resourced in private data centers.
Proactive Monitoring and Model Drift Management
LLMs degrade silently. Output quality can shift as base model weights change, prompts evolve, or user request distributions drift — none of which trigger traditional infrastructure alerts. Our observability stack traces latency, throughput, and output quality continuously, surfacing regressions before they reach end users. Drift detection runs against your evaluation suite on a cadence you set, and any out-of-tolerance result triggers a review cycle rather than a manual on-call page.
When model updates are available — whether a new fine-tuned checkpoint, a revised custom adapter, or an updated base model — we stage the rollout against a shadow environment, validate against your golden evaluation set, then promote with a documented change record. Rollback is always available within the same maintenance window.
Incident Response and Security Patching
AI incidents follow a different remediation path than traditional software defects. A guardrail failure, a prompt-injection exposure, or an unexpected output distribution shift each requires both rapid containment and deliberate root-cause analysis before a fix is promoted to production. Our incident response runbooks are built for this: initial triage and containment, stakeholder notification within SLA, staged remediation with evaluation gating, and a post-incident report with timeline and control updates.
Security patching covers the full stack — inference server, container runtime, host OS, and any cybersecurity controls layered on top. For enterprise environments with change-control requirements, patches are tested in a lower environment first and promoted on a schedule that satisfies your change advisory board, not on an ad-hoc basis.
Support Tiers and Co-Operation Models
Teams vary in how much they want to own. Some want a fully managed service where we operate the platform end-to-end; others want their internal platform engineers to hold the controls while we provide an expert escalation layer. We offer both — and the boundary can shift as your team builds capability. Either way, you get a named support contact, a shared runbook, and a regular operational review cadence.
Support agreements align with your governance and data-privacy requirements: all access is logged, all change activity is auditable, and operational data never leaves your defined trust boundary. For organizations in regulated industries, we can provide evidence packages compatible with audit frameworks on request.
Private AI On Your Terms
Tell us your use case and constraints — on-prem, cloud, or edge — and we'll map a compliant deployment within one business day.
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