IncidentFox

AI SRE agents that respond to incidents and find root causes automatically.

IncidentFox deploys AI site reliability engineering (SRE) agents that automatically learn a customer’s production environment and respond to incidents — performing root cause analysis, correlating signals across systems, and executing remediation steps — with the speed and contextual awareness of an experienced in-house SRE. Production incidents are high-stakes, time-sensitive events where the cost of slow response is significant; most organizations lack sufficient SRE staffing to provide round-the-clock expert response at the pace modern distributed systems require. IncidentFox addresses this by deploying AI agents that develop ongoing knowledge of each organization’s specific environment — their services, dependencies, failure patterns, and runbooks — and apply that knowledge automatically when incidents occur. It targets mid-market and enterprise engineering organizations and differentiates through its environment-learning capability, which makes its agents more effective over time rather than applying generic incident response logic.

Compliance

SOC 2

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Key Features

Automatic environment learning: Continuously learns the topology, dependencies, and behavioral patterns of each customer’s production environment, building context that makes incident response more accurate and relevant than generic rule-based approaches.
Automated incident response: Detects incidents and initiates response workflows automatically — acknowledging alerts, gathering relevant signals, and beginning diagnosis — without waiting for an on-call engineer to wake up and log in.
Root cause analysis: Correlates signals across logs, metrics, traces, and deployment history to identify the likely root cause of an incident, surfacing a structured analysis for engineering teams rather than requiring them to assemble the picture manually.
Remediation execution: Can execute remediation actions — restarting services, rolling back deployments, adjusting configuration — based on established runbooks and learned patterns, reducing mean time to recovery.
Multi-model architecture: Uses multiple LLM providers to power the reasoning and analysis tasks involved in incident response, applying different models to different aspects of the workflow as appropriate.

Use Cases

For engineering teams with limited on-call SRE coverage: A mid-market technology company without a dedicated SRE team deploys IncidentFox to provide expert-level incident response capability around the clock, catching and beginning to resolve incidents faster than an on-call rotation of application engineers would.
For enterprises reducing mean time to recovery on production incidents: A large engineering organization that experiences production incidents regularly uses IncidentFox to accelerate the diagnosis phase — getting to root cause in minutes rather than hours — and automate the remediation steps that don’t require human judgment.

Pricing MODELS

Saas subscription

Pricing Summary

IncidentFox uses a SaaS subscription model with custom enterprise pricing. Specific pricing is not publicly listed — organizations contact the company for a quote based on their environment size and incident volume.