From a 1-star review to the commit that caused it.
Product intelligence, not another dashboard
From a 1-star review to the commit that caused it.
Error trackers watch your servers. Feedback tools read your users. Seismark is the layer that fuses both - reviews, crashes, logs, tickets - into one cited incident, down to the suspect commit. The part neither can do alone.
No credit card · Bring your own AI key or use ours · PII stripped at ingest
criticalCheckout is failing on iOS after 2.4.0
ios · 2.4.0 · 94%
14 new 1-star reviews “can’t pay, it just spins and fails since the update”
App Store
Error spike PaymentSheet: token\_expired \- 312 events vs 4 baseline
Sentry
New crash signature PKPaymentAuthorization across 47 sessions
Logs
Suspect commita1f9c2“cut Apple Pay token TTL to 60s”merged 6h before the spike
Three sources, one incident, one commit - found automatically, cited end to end.
Your stack has enough dashboards
What it doesn't have is one place that connects what your users feel to what your systems did to the line of code that changed. Three tools, three blind spots:
Error / APM
Watches your servers
Sentry, Datadog, and friends catch the crash and the metric - but never the 1-star review or the support ticket that arrived with it.
Feedback tools
Read your users
App-store, support, and survey tools capture the sentiment - but can't see the crash signature or the deploy that set it off.
Seismark
Connects all three
One incident that ties the review, the crash, the log spike, and the suspect commit together - every claim cited to a real source.
One pipeline, signal to fix
No new agent to babysit. Point Seismark at the sources you already run and it takes them from raw noise to a cited, actionable incident.
01
Ingest everything
Reviews, crashes, logs, traces, tickets, and OTLP from your AI agents - normalized to one shape, PII stripped at the door.
02
Correlate across sources
A review burst, a crash spike, and a log anomaly on the same build collapse into a single incident - not three alerts in three tools.
03
Explain, with citations
Cheap stats gate the expensive model calls. Every claim points to a real item, log line, or commit - or it is dropped. Cents per run.
04
Ship the fix
The suspect commit, a fix hint, and a push to Slack, Jira, or your IDE over MCP - so it lands where you already work.
Connect everything you already run
44 connectors across errors, logs, reviews, support, issues, code, and your AI agents. One-click setup with a live health check, not another API-key form.
ErrorsLogsReviewsSupportCommunityEventsIssuesCodeDocsDeliveryAI agents
Sentry
Datadog
App Store
GitHub
Jira
SSlack
Grafana Loki
Zendesk
PostHog
Notion
PagerDuty
BBugsnag
Linear
Elastic
SSigNoz
Sentry
Datadog
App Store
GitHub
Jira
SSlack
Grafana Loki
Zendesk
PostHog
Notion
PagerDuty
BBugsnag
Linear
Elastic
SSigNoz
New Relic
Intercom
GitLab
Bitbucket
Splunk
Dynatrace
Trustpilot
Discord
AAmplitude
Mixpanel
Confluence
Opsgenie
Rollbar
HHoneycomb
FFreshdesk
OOTLP / Claude Code
New Relic
Intercom
GitLab
Bitbucket
Splunk
Dynatrace
Trustpilot
Discord
AAmplitude
Mixpanel
Confluence
Opsgenie
Rollbar
HHoneycomb
FFreshdesk
OOTLP / Claude Code
Don't see yours? Request a connector and vote it up.
One-click connectors
OAuth and app installs, not API-key forms. We even scan your repo and suggest what to connect.
Meets your dev loop
An MCP server and OTLP endpoint plug Seismark into Claude Code, Cursor, and your IDE - both directions.
Governed by design
Per-tenant budgets with auto-pause, encrypted secrets, PII stripped at ingest, and no training on your data.
One layer over feedback, telemetry, and code
Feedback tools read reviews. Error trackers watch crashes. Seismark ingests from both - including the observability stacks you already run - and correlates them down to the commit.
| | Seismark | Feedback tools | Error / APM | | ---------------------------------------------- | -------- | -------------- | ----------- | | User feedback (stores, support, forums) | | | | | Crashes, errors, logs, traces | | | | | Ingests Sentry / Datadog / SigNoz / your stack | | | | | Cross-source incident correlation | | | | | Code-grounded root cause + suspect commit | | | | | AI triage with mandatory citations | | partial | | | Bring your own LLM key or platform credits | | | | | MCP / IDE agent integration | | | |
Turn the firehose into a short list.
Connect two sources and get your first cited digest in under fifteen minutes.