Start with the decision. Prove the signals. Then ask.

Review the minimum identity-linked product evidence for an authorized support case, corroborate any real error case, and prepare a reply that preserves uncertainty.

Review cases first seen after a trusted deploy, distinguish SHA attribution from timing correlation, and inspect the highest-impact evidence before proposing a fix.

Rank API endpoints by p95 latency, error rate, customer, and plan so engineering can fix performance where it affects revenue.

Compare page-load and interaction timing by release, browser, and route so frontend regressions are visible beside errors.

Show where customers fail while connecting integrations so engineering and CS can fix the highest-impact setup blockers.

See which background jobs are backing up, retrying, or landing in dead-letter queues before customers notice stale state.

Background sync, refresh, prefetch jobs fail silently and degrade UX. Group failures by task_name and network_type to find the patterns.

Split client-side errors by browser and version. Surface fingerprints concentrated in one engine — usually older Safari or a stale Chromium.

Group asset 404s by URL and deploy SHA. Catch stale CDN caches and missing hashed bundles before users see a blank page.

Find CORS-blocked requests grouped by origin and endpoint. Surface the misconfigured Access-Control-Allow-Origin from a recent service move.

Map three ordered lifecycle events to live project signals, run the canonical funnel blueprint, and explain drop-offs without inventing causes or benchmarks.

Compare verified exposure and outcome signals across variants, surface readiness and sample problems, and prepare an evidence-first decision memo.

Tie acquisition campaigns to paid conversion, revenue, and retention so marketing and finance can see which campaigns pay back.

Break down checkout completion, failures, and latency by payment method so growth teams know which option hurts revenue.

Capture failed docs searches and connect them to activation, support load, and API adoption so docs work targets real blockers.

Measure failed-payment recovery from invoice failure through retry, card update, recovery, or cancellation for subscription revenue.

See which locked features users try to access, which plans hit upgrade prompts, and where entitlement gates create conversion opportunities.

Rank feature requests by account value, usage pain, and renewal risk so product and GTM teams can prioritize with context.

Compare acquisition sources by activation, retention, and paid conversion instead of only measuring clicks or signups.

Correlate email volume, opens, clicks, unsubscribes, activation, and churn to find where lifecycle messaging helps or hurts.

Render the bounded weekly-funnel-review template so a scoped runner records an exact-period aggregate report without raw users or mutation authority.

Combine verified account usage, revenue context, and support-friction signals into a review queue without pretending a universal score proves churn or intent.

A bounded self-healing workflow that claims one trusted production case, prepares one reviewable draft PR, and waits for CI, deploy, and real-traffic verification.

Create a weekly account digest for each CSM with risks, expansion signals, product adoption, and customers needing outreach.

Follow an enterprise account from kickoff through integration, first data, first value, and blocked steps so solutions teams can unblock launches.

Assemble trusted deploy, error-case, and product-event evidence into a human release review without collapsing unlike signals into a misleading score.

Combine usage decline, errors, seat utilization, and support friction into a renewal-risk brief CS can act on before the renewal date.

Find which production errors cannot be unmangled because sourcemaps are missing, stale, or uploaded under the wrong release.

Find suppressions that are hiding high-volume, high-impact, or recently regressed errors before noise rules mask real incidents.

Combine errors, complaints, affected users, and plan data to show which accounts are consuming the most support attention.