Browser Health
Frontend errors move product metrics — and most analytics tools never see them. Muon connects browser health to conversions.
What is Muon Browser Health?
Muon collects distilled client-side health signals — JavaScript exceptions, unhandled promise rejections, failed fetches, and resource + navigation errors — and connects them to product metrics like conversion, for a given release and page.
It is not a Sentry replacement. Muon does not aim to symbolicate deep stack traces; it exists to answer whether frontend failures are moving the metrics you care about.
Distilled browser health signals — grouped, not streamed.
Muon captures a small, high-signal set of client-side failures and ties each one to the page, release and segment where it happened.
JavaScript exceptions
Uncaught runtime errors thrown in the browser — the failures that break flows and stop users mid-task.
Unhandled promise rejections
Async failures that never get caught — often invisible to a global try/catch, but very visible to users.
Failed fetch requests
Network calls that error or return failure status — the API and XHR failures behind broken checkouts and empty screens.
Resource loading failures
Scripts, styles and images that fail to load — a missing bundle after a bad deploy can silently disable a feature.
Navigation failures
Route and page-load failures across the app — the transitions that leave users stuck on a blank or broken page.
Affected sessions & releases
Every signal is rolled up to how many sessions were hit and which releases introduced it — so an error is a product fact, not a log line.
Not a Sentry replacement.
Muon does not need full source maps or stack-trace symbolication to answer product questions. It answers a different, complementary set of questions.
What Muon answers
Did browser errors increase? Which users were affected? Which page? Which release introduced the problem? Did the errors correlate with a conversion drop?
These are product questions — answered by tying distilled signals to metrics, releases and segments.
What Sentry is for
Symbolicated stack traces, source maps, breadcrumbs and deep root-cause debugging of a specific exception in your code.
Use Sentry for deep debugging; use Muon to understand product impact. They work well side by side. Sentry integration →
Frontend failures quietly cost you signups and revenue.
A broken build or a failing API call rarely announces itself in a product dashboard — but it still moves the numbers. Muon ties an error spike to the metric it likely moved, so the cost of a bad frontend release becomes visible.
Errors are a leading indicator
A spike in JavaScript exceptions on a checkout page is often the first sign of a conversion drop — before the revenue chart catches up.
The impact is segmented
Failures rarely hit everyone equally. Muon shows the browser, platform, page and release where the errors — and the metric change — are concentrated.
Correlation, stated honestly
Muon links an error spike to the metric it is likely related to. This is correlation, not proven causation — the language stays honest so your team can act with judgment.
An error spike, connected to the metric it moved.
When browser errors surge, Muon surfaces a Finding that names the segment, the release and the product metric that likely moved with it.
This is exactly the shape of a Muon Finding — statistics first, explanation second.
Questions, answered directly.
Does Muon replace Sentry?
What browser errors does Muon capture?
Does Muon symbolicate stack traces?
Can Muon link errors to conversion?
Is this in the open-source version?
See frontend errors move your product metrics.
Self-host Muon, connect browser health to conversions, and let Findings surface the errors that are actually costing you.