RootCauseAI for DevOps and SRE
Less triage, faster root cause
RootCauseAI gathers the context your on-call engineer would spend the first hour collecting, then shows the likely cause.
The on-call problem
Manual triage
Logs, deploys and code searched by hand before debugging starts.
Tool switching
Each tool holds part of the story. None shows the whole incident.
Senior bottlenecks
Escalations land on the same few engineers.
How it works
Report
Paste the error or describe the symptom.
Correlate
Logs, recent commits, tickets and database state checked together.
Explain
Likely cause, code location, evidence trail and a suggested fix for your engineer to review.
What your team gets
Log correlation
Events matched across services and time windows.
Commit history
Recent changes lined up against incident timing.
Read-only queries
Database state checked without changing data.
Visible reasoning
See what it searched and how it reached a conclusion.
Runs offline
Local models for air-gapped or restricted environments.
Systems we work with
We connect to the platforms you already run. You keep your licences and your data.
- Jira
- Redmine
- ServiceNow
- GitHub
- GitLab
- Bitbucket
- Splunk
- Elasticsearch
- MySQL
- MongoDB
Our own product
We built RootCauseAI for teams whose code and logs cannot go to cloud AI. It connects to your existing observability and ticketing tools and runs inside your infrastructure.
- Self-hosted, container based
- Works across repositories and services
- Your engineers approve every fix
Questions
How is AI root cause analysis different from observability tools?
Observability tools collect and display logs, metrics and traces. AI root cause analysis uses them as sources, then searches, correlates them with recent commits and tickets, and explains the likely cause. RootCauseAI sits on top of tools such as Splunk and Elasticsearch, so nothing is replaced.
Can AI incident investigation run in an air-gapped environment?
Yes. With local models, RootCauseAI runs fully inside your network and makes no external calls, which suits restricted and operational technology environments. Updates and model changes are installed by your team under your own change process.
Can AI trace an error across microservices?
Yes. RootCauseAI follows an error across several repositories, services and time windows, matching log events with recent deployments to find the service where the fault started. It then shows the code path and evidence so your on-call engineer can confirm it.
Related
- Incident investigation that stays in your networkDescribe the incident. RootCauseAI searches your code, logs, tickets and databases, then shows the likely cause and its evidence.
- RootCauseAIOur own AI investigation tool, run inside your boundary.
- Managed AI servicesMonitored and improved after go-live.
- Private AI infrastructureAI that runs inside your own environment.
Try it on last week's incident
We will run a walkthrough on a sample from your environment.
Fixed-fee phases, agreed before we start. Stop after any phase.