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EnglishWhat Is Galileo AI in LogRocket and How Does It Work?
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Galileo AI is LogRocket's built-in AI layer that watches every session for you. Instead of waiting for users to report problems, it proactively identifies what's causing friction, analyzes how severe each issue is across real sessions, and delivers reproduction steps so your team can act immediately. It's most useful if you already collect session data and want to cut through noise to find the issues actually affecting users.
What Galileo AI does
According to LogRocket, Galileo is "the AI that watches every session for you." Its job is to surface the issues impacting your users rather than leaving you to hunt through raw session replays manually. Specifically, it:
- Proactively identifies technical and UX issues causing friction
- Analyzes severity across real sessions, so you can tell which problems matter most
- Delivers reproduction steps so engineers can act without re-investigating from scratch
The stated goal is to reduce manual triage and focus your team on the areas of your app most relevant to the business.
How it fits into the LogRocket platform
Galileo isn't a standalone tool — it sits on top of LogRocket's existing capabilities and feeds into them. The platform's core surfaces are:
| Capability | What it covers |
|---|---|
| Product Analytics | Conversion funnels, path analysis, timeseries; Galileo surfaces insights |
| Issues | AI proactively identifies technical and UX issues; pinpoints changes to make |
| Session Replay | DOM playback, console and network logs, errors, performance data |
| UX Analytics | Individual and aggregate views of user experience |
| Error Tracking | Frequent errors, network failures, crashes; Galileo cuts through noisy issues |
| Frontend Performance Monitoring | Correlate frontend performance with user experience impact |
The practical flow: Galileo flags an issue → you drill into the affected sessions via replay → you use the reproduction steps and telemetry to fix it. Session replay is the drill-down layer that gives individual context behind an aggregate insight.
How to get value from it
- Confirm you're capturing sessions. Galileo analyzes real sessions, so it depends on LogRocket already recording activity in your app.
- Start with the Issues view. This is where AI-identified technical and UX problems surface, ranked by severity across sessions.
- Use the reproduction steps. Rather than reconstructing a bug from logs, take the steps Galileo provides and verify the problem directly.
- Drill into replay for context. When an issue needs more detail, open the session replay to see DOM playback, console and network logs, errors, and performance data.
- Prioritize by business relevance. LogRocket notes you can focus on the areas of your app most relevant to you, so tune attention toward issues tied to conversion or adoption.
Expected result: fewer hours spent manually scanning replays, and a shorter path from "something is wrong" to "here's the fix."
Common sticking points
- It's not a replacement for instrumentation. Galileo works on the sessions LogRocket captures; if events aren't recorded, there's nothing to analyze.
- AI output still needs judgment. Severity ranking helps you prioritize, but you decide what's worth fixing given your roadmap.
- Replay is where the detail lives. Treat Galileo's summaries as the entry point, not the full picture — the console, network, and performance data in replay is what confirms root cause.
Who it's for
LogRocket says 3,000+ customers use it, spanning retail/eCommerce engineering (for example, optimizing conversion in self-checkout) and software product/UX teams (for example, surfacing user struggle to improve adoption). If your team already runs session replay and wants AI to do the first pass of triage, Galileo is the feature aimed at that job.
To evaluate it for your own stack, the practical next step is to check current plans and trial terms on LogRocket's pricing page, since availability and limits are set there rather than assumed.