Your AI SRE that never sleeps
Built-in SLO management, real-time error budget tracking, and an AI agent that handles routine incidents autonomously — so your SRE team can focus on reliability engineering, not toil.
What's killing SRE team productivity
SRE was invented to improve reliability through engineering. Yet most teams spend the majority of their time on toil, not engineering.
Drowning in Toil
Repetitive, manual incident response work consumes 60–70% of SRE time. There's little room left for reliability projects that actually move the needle.
On-Call Burnout
Engineers are paged for incidents that an automated system could resolve in seconds. Repeated 3 AM alerts for known issues kill morale and retention.
SLA Compliance Risk
Without real-time error budget visibility, teams discover they've blown their SLO after the fact — scrambling to explain the breach to stakeholders.
Fragmented Reliability Data
SLO definitions live in spreadsheets, dashboards are built by hand, and error budget burn is calculated manually. Nothing is authoritative or real-time.
SLOs, error budgets, and AI — all in one place
SLO Definition & Tracking
Define SLOs in minutes with templates for availability, latency, and error rate. Track burn rate in real time with automated alerts before you breach.
Error Budget Management
Visualize error budget consumption by service, team, and time window. Get predictive alerts when burn rate threatens your monthly budget.
AI SRE Agent
The AI SRE agent triages, diagnoses, and resolves routine incidents autonomously — handling up to 80% of pages without human intervention.
Toil Measurement
Automatically measure and track toil across your team. Get actionable recommendations for automation that will reclaim engineer hours.
How the AI SRE agent handles an incident
From detection to resolution — in seconds, not minutes.
Anomaly Detected
AI SRE detects error rate exceeding SLO threshold at p99. Error budget burn rate calculated instantly.
Signals Correlated
Traces, metrics, and logs cross-correlated across 12 upstream dependencies in under 2 seconds.
Root Cause Identified
Connection pool exhaustion on database primary. Confidence 97.4%. Matching runbook found.
Autonomous Remediation
Pool size scaled, read-replica traffic redistributed. Human SRE optionally notified with full context.
SLO Restored
Error rate returns to normal. Error budget burn stopped. Post-mortem auto-drafted and assigned.
TigerOps cut our on-call toil by 40% in the first month. The SLO dashboard finally gives us a shared language with the business about what reliability actually means — and the AI agent handles our most common incidents without anyone being paged.
Reclaim your on-call hours
Give your SRE team the tools to manage reliability at scale — with AI doing the heavy lifting.