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.

This is a capability we deliver for you as a service, not a role we’re hiring for.

How We’d Approach This

A clear, staged plan, not a black box

These are the service-specific steps inside our six-step process.

1
Diagnose how ML and LLM systems currently fail in production and where existing on-call and alerting misses those failure modes.
2
Pilot the incident playbooks against a recent or simulated incident to test detection and escalation paths.
3
Review playbooks, severity definitions, and escalation paths with the team responsible for production systems.
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.

Read all 6 architectures as text

Lean Fast-Start Architecture

Detection

Model Output Looks Wrong
Single On Call Engineer Notified

Triage

Manual Severity Judgment
Basic Runbook Consulted

Response

Manual Rollback of Model or Prompt
Fix Verified Manually

Follow Up

Incident Noted in Shared Doc
Root Cause Discussed Later
Runbook Updated if Time Allows

Cloud-Native / Managed Services Architecture

Managed Alert Layer

Managed Alerting Service
On Call Rotation Schedule
Cloud Logging Service

Incident Operations

Managed Incident Tracker
Severity Classification Applied
Incident Channel Auto Created

Response Operations

Rollback Playbook Executed
Status Page Updated

Delivery Layer

Post Incident Review Scheduled
Playbook Updated in Repository
Metrics Dashboard Updated
Stakeholder Summary Sent

Event-Driven & Automation-First Architecture

Triggers

Anomaly Event Detected
Automated Severity Classifier

Automation Layer

Known Failure Pattern
Automated Rollback Triggered
Automated Page to On Call

Response Path

Auto Verification Check Run
Incident Channel Auto Created
Resolved

Delivery

Escalated to Secondary On Call
Auto Logged Resolution
Post Incident Review Auto Scheduled

Enterprise-Grade & Compliance-Ready Architecture

Governance Layer

Incident Response Policy
Tiered Escalation Policy
Severity Definitions Documented

Resilient On Call

Redundant On Call Rotation Across Regions
Role Based Response Access
Backup Responder Assigned

Auditable Response

Full Audit Trail of Incident Actions
Post Incident Compliance Report
SLA Tracking Dashboard

Compliance Delivery

Signed Off Incident Closure
Audit Trail Export
Retention Schedule for Incident Records

AI-Augmented, Human-in-the-Loop Architecture

Signal Inputs

Confidence and Hallucination Signals
Failure Classifier Model
Recent Deployment History

AI Analysis Layer

Likely Failure Mode Identified
Suggested Root Cause and Rollback Drafted

Human in the Loop

On Call Engineer Reviews Suggestion
Rollback Action Appropriate
Engineer Chooses Alternate Fix
Human Approved Rollback

Delivery

Fix Executed and Verified
Post Incident Review with Playbook Update

Integration-Heavy / Ecosystem-Connected Architecture

Client Systems

Client On Call Tool
Incident Integration Layer
Client Ticketing System
Client Observability Stack

Incident Core

Severity and Routing Engine
Playbook Execution Layer

Two Way Sync Layer

Model Registry Rollback Sync
Client Slack or Teams Sync
Rollback Executed in Production
Stakeholder Notification Channel

Shared Governance

Joint Post Incident Review with Client
Published Updated Playbook

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

Most engagements like this start as a $500–$2,500 pilot, see full pricing.