Intelligent product analytics for modern software teams
Muon is product analytics with a diagnostic layer: it combines events, funnels and browser health with checks that turn meaningful changes into evidence-backed diagnoses.
What does Muon do?
Muon collects product data, finds meaningful changes, checks the possible explanations, and tells you what happened and what to do next.
It checks the data first, then technical, traffic and product explanations. You see what fits, what was ruled out and what the data cannot yet prove.
Collect the product data that matters.
Muon collects more than pageviews. Product events, funnels and browser health give it enough context to check different explanations when something changes.
Every data point uses the same event model, so Muon can compare changes across web, mobile and backend projects.
Detect meaningful changes.
Muon builds historical baselines for your metrics and detects changes that are both statistically unusual and practically important — so you hear about the drop that matters, not every wobble in the noise.
Statistically unusual
Each metric is compared against its own historical baseline. A change has to stand out from normal variation before Muon flags it.
Practically important
Significance is weighed against impact. A tiny, noisy segment does not outrank a real move in conversion or revenue.
Structured Findings
Detections become Findings: what moved, how serious it is, what may explain it and what the data does not prove.
How Findings work →Check what explains the change.
Muon checks whether the data is correct and the change is real, then tests technical, traffic and product explanations. Segment and release analysis are used when they fit the case.
Muon uses segment contribution, information gain and correlation scoring to narrow a global number down to the exact place it moved — deterministic first, explanation second. Instead of smearing a drop across everyone, it shows you the one segment that changed and what it lines up with.
Add context with integrations. Soon
Metrics are easier to understand when Muon knows what happened around them. Connect the systems that ship code, track errors, bill customers and serve traffic — so a Finding can point at the deploy or incident it is likely related to.
Explore your data conversationally. Soon
Ask follow-up questions and get a plain-language explanation of results Muon has already computed. The model reads checked facts; it does not decide what changed or invent a diagnosis.
1 · Statistics
Deterministic change detection against historical baselines. Significance and impact, computed — not guessed.
2 · Correlation
Segment contribution and correlation scoring narrow the change to where it is concentrated and what it lines up with.
3 · Explanation
Only now does an LLM read the structured evidence and write the summary. Correlation is described as likely or related — never claimed as proven cause.
Fast enough that the answers are basically free to run.
In an internal benchmark using the same Umami-compatible /api/send endpoint and identical PostgreSQL setup, Muon processed 111,000 events with materially lower cost per event. Deploy it yourself with Docker or Kubernetes; your data stays with you.
This is an internal benchmark — read the methodology before making production decisions. Ready to run it? Deploy with Docker →
Questions, answered directly.
What makes Muon product analytics intelligent?
Is Muon just analytics?
Does Muon support mobile and backend?
Does Muon use AI?
Run your own intelligent product analytics tonight.
Clone it, docker compose up, and open the dashboard. Backend, tracker, dashboard and benchmark harness — all open source.