Agentic web and product analytics
All the detail you expect from your analytics - pageviews, events, sessions, conversions - with an agent on top. It watches every metric, catches the issues, explains each spike and drop with evidence, and suggests what to improve next.
4.9% → 4.0% against a 30-day baseline
Likely cause
Checkout deployment released 23 minutes earlier
Evidence
Estimated impact
-$3,800/mo
Confidence
High
Recommended action
Inspect the Safari payment-form change and roll it back.
The loop you run by hand across four tabs, run continuously instead.
Stripe or RevenueCat for billing, the GainSignal SDK for product events, Search Console for organic acquisition. Identity is stitched end to end, so a visitor resolves to a session, a user, an account and a Stripe customer.
Every metric gets a baseline per segment. Change detection is computed deterministically, not inferred by a model reading a chart, so what surfaces is a real move and not a plausible story about one.
It ranks candidate causes across deployments, segments, devices, campaigns and content, then gathers evidence for and against each one. Correlation is reported as correlation, never dressed up as attribution.
The change, the affected population, the revenue impact, ranked causes with their evidence, a calibrated confidence level, and a recommended action. The recommendation becomes a monitored hypothesis until the metric recovers or it does not.
Four decisions that make a finding worth reading. Each one is a deliberate constraint, not a feature.
Findings are ranked by what they cost or earn, using actual Stripe and RevenueCat outcomes: new MRR, expansion, contraction, churn, refunds and failed payments. Revenue is the ordering dimension, not an extra tab.
Campaign, content, deployment and support signals join to visitor, session, user, account and Stripe customer. That graph is what lets an investigation cross a tool boundary instead of stopping at one.
Five credible findings beat fifty generic observations. Every alert spends your attention, so findings are deduplicated, suppressed and held to an evidence bar before they reach you.
A recommendation opens an observation window rather than closing a ticket. Finding, action, expected effect, then a verdict on whether the metric actually recovered.
The first loop is billing plus product events. Everything else earns its place by improving an investigation.
Stripe
Subscriptions, invoices, refunds, disputes
RevenueCat
App Store and Google Play subscriptions
GainSignal SDK
Web and server events, identity, attribution
Google Search Console
Queries, clicks, CTR, position
GitHub
Deployments and commits as candidate causes
Vercel
Release timing correlated to metric moves
Sentry
Errors as evidence for a conversion drop
Slack
Findings delivered where the team already is
Especially the sceptical ones. A product about calibrated evidence should answer them plainly.
Connect Stripe and your product events, and let the first week of findings tell you what your dashboards have not.