
Find the root cause,from technical failures to business breakdowns.
A model built for your business. Deterministic answers, and it grows with you.
Trusted by
Where an incident's time goes
What's missing isn't more alerts.
It's knowing which one caused the incident.
The incident, on the clock
14:11
0 min in
Keep scrolling to run the clock
Today
14:11
The alert storm
14:20
The war room
15:40
Manual digging
Next day
The RCA
Every hour on the long line is downtime, engineers pulled off their work, and customers who noticed before you did.
With trufflerootstopped at 14:14
14:11
The same alerts
14:14
The top 3 root causes
Works with
Why truffleroot?
Better than your favorite LLM.
The engine ranks candidate causes the same way on every run, so a result can be reproduced, replayed and checked, not argued with.
Deterministic
Better than your favorite LLM.
The engine ranks candidate causes the same way on every run, so a result can be reproduced, replayed and checked, not argued with.
Accurate
95% top-3 accuracy.
The true root cause is among the top three candidates, and every link to it cites the rule and the evidence behind it.
Fast
Answers before your coffee order is up.
The top 3 candidates in minutes, not hours. Your team starts from a short, ranked list instead of a blank page and a war room.
Yours
Built from how your systems fail.
The model comes from your topology, incidents, runbooks and code, so it knows how your services depend on each other instead of guessing. Your engineers review every rule, and every version is kept.
Learns
Better after every incident.
After each incident it finds what the model missed and proposes new events, rules and thresholds. Engineers approve them first.
Integrates with what you already run.
And learns from all of it.
truffleroot connects to your topology, telemetry, code and incident history. Each source either shapes the model or becomes evidence when something breaks.
Builds the model
- Linear
- Linear
- Linear
Evidence for an investigation
- Grafana
- Prometheus
- Datadog
- Splunk
- Sumo Logic
- GitHub
- GitLab
- Bitbucket
- Slack
- Grafana
- Prometheus
- Datadog
- Splunk
- Sumo Logic
- GitHub
- GitLab
- Bitbucket
- Slack
- Grafana
- Prometheus
- Datadog
- Splunk
- Sumo Logic
- GitHub
- GitLab
- Bitbucket
- Slack
Topology
Which services exist, where they run and what they call. The skeleton the causal model is built on.
Incident history
Past incidents, postmortems and runbooks: how your systems have failed before, and what your team learned.
Telemetry
The signals events are detected from, and the evidence cited when one event is linked to another.
Changes
Commits, merges and deploys, lined up against the timeline of the incident.
Your team
Start an investigation from the channel your team is already in, and get the explanation back in the thread.
Try our demo.
A live environment you can explore on your own. The demo runs on Train-Ticket, an open microservice benchmark, set up the way a customer environment would be.
- 1
Open the demo
Nothing to install. The environment is already modelled.
- 2
Take the guided tour
A short walkthrough of triage, the causal graph and the explanation.
- 3
Work a live incident yourself
Open an incident, follow the causal chain to its root cause, and ask the investigation agent about it.
