The immune system
for your software

AI agents that continuously investigate
live data, diagnose emerging issues and
generate preemptive fixes

@Phoebe

Watch product demo

Trusted by top engineering orgs

"Phoebe has changed the way we analyse incidents. One of the early benefits has been better, faster remediations, which have resulted in a substantial drop in the recurrence of similar incidents."

— BERNIE MILES, CTO, PPRO

"Phoebe has had a real impact on how we investigate and remediate incidents. Work that used to take us hours to piece together can now take minutes, which really matters at our scale."

— JAY DAVIES, HEAD OF RELIABILITY ENGINEERING, TRAINLINE

Resolve incidents up to 90% faster

Agents evaluate cause hypotheses with governed, audit-trailed access across siloed data sources.

Prevent outages before they happen

Phoebe predicts incidents from leading indicator data and generates pre-emptive fixes.

Reduce manual engineering work

Turn numerous 10-20 minute investigations into 30-second automations.

Class-leading AI for troubleshooting tasks

Phoebe's strength comes from proprietary models that understand and connect data across diverse, siloed systems.

Log querying accuracy*

Amazon Q

50%

Phoebe

90%

*Natural language generation of accurate AWS Cloudwatch queries (May 2025)

Code commit root cause precision*

Sentry AI

42%

Phoebe

85%

*Prediction success (precision) of code commit causing Sentry alert (Apr 2025)

Product features

Alert Investigation

Phoebe proactively investigates alerts as they come in, adding relevant facts and predicting the root cause along with supporting logic.

After an event is triaged, Phoebe generates hypotheses, runs evidence retrieval, and proposes remediations. Similar events are grouped into wider issues.

Engineers can ask Phoebe to fetch troubleshooting facts by turning a natural language request into queries across logs, traces, commits, and more.

Product features

Pull Request Fixes

Phoebe generates pull requests from alerts and searches, proposing changes grounded in data to fix weaknesses or improve performance.

Engineers review and approve pull requests, retaining control of the codebase while closing the loop from detection to resolution.

Product features

Search Agent

Engineers can ask Phoebe to fetch facts to help troubleshoot an issue.

Phoebe turns a natural language request into queries across logs, traces, commits and other data sources.

Tasks that would take an engineer 10-15 mins in various query interfaces and dashboards can now take 30 seconds.

Product features

Analyst App

Phoebe prepares analysis, surfaces contributing factors, and recommends remediations, reducing manual work and preventing repeat incidents.

Every error and incident is added to an intelligent knowledge base, making each investigation faster and more informed than the last.

All related information is linked to a single report, with a cause graph that visualises the full incident from contributing factors through to resolution.

Agent Data Platform

Getting the right answer isn't just about reasoning. It's about having the right data, at the right time, in the right shape. We built the Agent Data Platform so Phoebe can do that reliably.

Learns your stack before it asks

Schemas, metrics, causal relationships — built before the investigation starts. When an incident fires, Phoebe already knows which signals matter. No cold-start fumbling through 60,000 metrics.

Connects signals across sources

PagerDuty incidents, GitHub PRs, Datadog traces, Slack threads — signals scattered across separate tools. Phoebe connects them so agents follow relationships across the full picture, not reason from fragments.

Knows exactly what it can touch

Scoped access per source and data type. Every query logged against a named identity. Full visibility into what was accessed, when, and why — without a pile of API keys to manage.

How we built Phoebe

We are a team of software engineers and ML researchers from Google, Amazon, Meta and Stripe who have built systems trusted by billions every day.