ResolveOps AI turns scattered incident data into timelines, root-cause insights, postmortems, and reliability actions.
Live demo · Payment outage caused by a database migration
The problem
Alerts fire in PagerDuty. Context lives in Slack. Tickets pile up in Jira. Metrics sit in Datadog and CloudWatch. Customer pain shows up in support. Responders waste precious minutes reassembling what happened.
Critical details are spread across six tools while the clock is running.
Correlating deploys, migrations, and alerts by hand delays mitigation.
Postmortems are rushed and recurring patterns go unnoticed.
The solution
ResolveOps AI brings every signal into a single incident event model stored in Amazon DynamoDB — then generates summaries, likely root causes, recommended actions, and structured postmortems.
Incidents, services, timeline events, responders, root causes, postmortems, and action items are stored as related tables — so reliability analytics and AI run on clean, structured data.
incidentsservicestimeline_eventsroot_causesaction_itemsai_recommendationsHow it works
Events from PagerDuty, Slack, Jira, CloudWatch, and Datadog stream into one incident record.
Data is normalized into an Amazon DynamoDB event model: incidents, timeline events, services, responders.
ResolveOps AI builds the timeline, surfaces likely root causes, and detects recurring reliability patterns.
Generate postmortems and prevention-focused action items that reduce future incident risk.
Data sources
ResolveOps AI ingests events from your alerting, collaboration, ticketing, and monitoring stack — and maps them to the right incident automatically.
On-call alerts & escalations
War room updates & context
Tickets & follow-up actions
AWS alarms & metrics
APM, deploys & SLO monitors
Zendesk support signals
Why teams use it
Stop stitching together Slack, Jira, and dashboards mid-incident. Everything lives in one timeline.
AI-suggested root causes and mitigations help responders act with confidence under pressure.
Draft structured, executive-ready postmortems from the data you already captured.
Reliability signals reveal recurring failure patterns before they become the next SEV1.
Built for reliability teams
Cut time-to-context with a single source of truth during active incidents.
Trace incidents back to deploys, migrations, and service dependencies.
Track MTTR, services at risk, and overdue prevention work.
Run the room with live timelines, responders, and mitigation status.
Explore the demo workspace following a payment outage caused by a database migration — from detection to postmortem.