Runbooks are scattered across wikis, docs, and tribal knowledge that...
Resolve incidents faster with institutional memory
When systems go down, your team scrambles through runbooks, past incidents, and Slack threads for answers. Context surfaces relevant resolutions, connected systems, and the right people instantly -- so your MTTR drops, not your uptime.
Book a Demo45%
REDUCTION IN MEAN TIME TO RESOLUTION
3x
FASTER RUNBOOK DISCOVERY DURING INCIDENTS
70%
OF INCIDENTS HAVE RELEVANT PAST RESOLUTIONS IN THE GRAPH
0
PRODUCTION DATA EXPOSED TO EXTERNAL SYSTEMS
The Problem
Why this is hard without a knowledge graph
Runbooks are scattered across wikis, docs, and tribal knowledge that only senior SREs can find
Past incident resolutions are buried in post-mortem documents that nobody searches
On-call engineers waste critical minutes identifying which systems are connected to the failing component
Incident knowledge walks out the door when experienced SREs leave the organization
The Solution
How Context solves it
01
Instant runbook and resolution search
Query across every runbook, post-mortem, and incident report in seconds. Context surfaces the most relevant past resolution with citations to the original fix.
02
System-to-people knowledge graph
Context maps the relationships between services, teams, and incidents. Know immediately who owns the failing system and what has changed recently.
03
Post-incident learning that compounds
Every incident resolution strengthens the knowledge graph. The next time a similar issue occurs, the answer surfaces automatically -- even if the original responder has left.
04
Secure enough for production systems
Context deploys on-premise with access to your monitoring, ticketing, and communication tools. No production telemetry or incident data leaves your infrastructure.
Beforeraw signals
Past incident resolutions are buried in post-mortem documents that nobody...
On-call engineers waste critical minutes identifying which systems are connected...
Incident knowledge walks out the door when experienced SREs leave...
Runbooks are scattered across wikis, docs, and tribal knowledge that...
Past incident resolutions are buried in post-mortem documents that nobody...
On-call engineers waste critical minutes identifying which systems are connected...
Incident knowledge walks out the door when experienced SREs leave...
Afterstructured outcomes
01
Instant runbook and resolution search
02
System-to-people knowledge graph
03
Post-incident learning that compounds
04
Secure enough for production systems
How It Works
From deployment to answers
01
Incident triggered
PagerDuty or your alerting system fires. The on-call engineer opens Context alongside their existing incident workflow.
02
Query past incidents
"What happened last time the payment service had high latency?" -- Context returns the post-mortem, the fix, and the Jira ticket with the root cause.
03
Map blast radius
Context's knowledge graph shows which services depend on the failing component, who owns them, and what runbooks exist for each.
04
Resolve and document
Apply the fix. The post-incident review is automatically connected to the knowledge graph for future incidents.
FAQ
Frequently asked questions
How does Context reduce mean time to resolution (MTTR)?
Context surfaces relevant past incidents, runbooks, and system relationships instantly when an incident occurs. Instead of searching through wikis and Slack, on-call engineers get citation-backed answers about what worked before, reducing MTTR by an average of 45%.
Does Context integrate with PagerDuty and other alerting tools?
Context integrates with PagerDuty, Datadog, ServiceNow, Jira, Slack, and Confluence. It ingests incident data, runbooks, and post-mortems from your existing tools to build a comprehensive incident knowledge graph.
How does the knowledge graph help during incidents?
The knowledge graph maps relationships between services, teams, incidents, and changes. During an incident, you can see which services are connected, who owns them, what changed recently, and what past incidents affected the same components.
Is Context fast enough for real-time incident response?
Yes. Context is designed for sub-second query response times. The knowledge graph is pre-built and continuously updated, so answers are available instantly when you need them during an incident.
Can Context help with post-incident reviews?
Context automatically connects incident resolutions, post-mortems, and follow-up actions into the knowledge graph. This means future incidents benefit from every past resolution -- even if the original responder is no longer on the team.
Industries
Industries that benefit most
See Context in action
30-minute technical walkthrough. No slides -- just a live demo tailored to your environment and use case.
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