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Rivac Labs
MLOps & LLMOps

AI Production Incident Response & On-Call

A model returning confidently wrong outputs, or an LLM hallucinating in a customer-facing flow, doesn't throw a 500 error — generic software on-call rotations are built to catch crashes, not that. This service builds ML/LLM-specific incident-response playbooks that define what counts as an incident for these systems, how it's detected, who gets paged, and how to roll back a model or prompt fast, and, where needed, staffs the on-call rotation itself. It's for teams running production ML or LLM systems whose response plans were written for traditional software and don't cover how these systems actually fail. The outcome is a response capability built around real ML/LLM failure modes, not adapted from a generic runbook.

How We’d Approach This

A clear, staged plan — not a black box

  1. 1

    Diagnose how ML and LLM systems currently fail in production and where existing on-call and alerting misses those failure modes.

  2. 2

    Pilot the incident playbooks against a recent or simulated incident to test detection and escalation paths.

  3. 3

    Review playbooks, severity definitions, and escalation paths with the team responsible for production systems.

  4. 4

    Operate the on-call rotation and playbooks going forward, refining them after every real incident.

What You Get

Deliverables from this engagement

  • ML/LLM-specific incident-response playbooks by failure mode
  • Defined severity levels and escalation paths
  • Staffed on-call coverage where requested
  • Post-incident review process with playbook updates baked in

Six Ways We Could Architect This

Different engagement, different build — pick the shape that fits

There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.

Ready to get started?

Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start — scoped small enough to prove itself before you commit to anything bigger.

Talk to us about AI Production Incident Response & On-Call
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