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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

  • Williams-Sonoma

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

  1. 14:11

    The alert storm

  2. 14:20

    The war room

  3. 15:40

    Manual digging

  4. 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

  1. 14:11

    The same alerts

  2. 14:14

    The top 3 root causes

Works with

Why truffleroot?

  • 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

  • Kubernetes
  • Amazon Web Services
  • Google Cloud
  • Microsoft Azure
  • Cloudflare
  • Jira
  • Confluence
  • Linear
  • Kubernetes
  • Amazon Web Services
  • Google Cloud
  • Microsoft Azure
  • Cloudflare
  • Jira
  • Confluence
  • Linear
  • Kubernetes
  • Amazon Web Services
  • Google Cloud
  • Microsoft Azure
  • Cloudflare
  • Jira
  • Confluence
  • 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.

Ask about your stack →

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. 1

    Open the demo

    Nothing to install. The environment is already modelled.

  2. 2

    Take the guided tour

    A short walkthrough of triage, the causal graph and the explanation.

  3. 3

    Work a live incident yourself

    Open an incident, follow the causal chain to its root cause, and ask the investigation agent about it.