Each incident sent engineers hunting for context from scratch, inconsistently across teams. Edwin AI now delivers it upfront.
A specialty insurer managing IT operations across a global hybrid environment spanning traditional on-premises infrastructure and cloud services.
Troubleshooting required engineers to manually gather context for each incident, and monitoring practices varied across teams and shifts, making...
Specialty insurance and reinsurance underwriting group.
AI-powered hybrid observability platform for enterprise IT infrastructure and AIOps.
Markel's Incident investigation is part of this use case:
Related implementations across industries and use cases
Engineers lost hours manually triaging alerts. AI agents now correlate events and trigger self-healing, automating 60% of remediation.
Finding root cause meant stitching logs, traces, and metrics by hand during incidents. Now AI surfaces the hypothesis; engineers verify.
IT tracked just 26% of live events. Now, AI monitors everything and resolves bugs autonomously.
Three engineers, two products, 82M users — signals stitched by hand. AI now delivers root causes before they open their laptops.
Manual testing left engineers prioritizing fixes by intuition. Now, an AI classifier calculates error impact to target critical updates.
Reviewing handwritten, unstructured documents took handlers up to an hour per claim. Now, AI extracts and validates data automatically.
Agents spent up to 6 minutes writing inconsistent notes. Now, AI instantly summarizes 55,000 weekly calls directly into the CRM.
Finding root cause meant stitching logs, traces, and metrics by hand during incidents. Now AI surfaces the hypothesis; engineers verify.
30-minute report-digging kept fleet audits chronically deferred. Now one prompt delivers every device, flag, and follow-up emails at once.
Each incident sent engineers hunting for context from scratch, inconsistently across teams. Edwin AI now delivers it upfront.
A specialty insurer managing IT operations across a global hybrid environment spanning traditional on-premises infrastructure and cloud services.
Troubleshooting required engineers to manually gather context for each incident, and monitoring practices varied across teams and shifts, making...
Specialty insurance and reinsurance underwriting group.
AI-powered hybrid observability platform for enterprise IT infrastructure and AIOps.
Markel's Incident investigation is part of this use case:
Related implementations across industries and use cases
Engineers lost hours manually triaging alerts. AI agents now correlate events and trigger self-healing, automating 60% of remediation.
Finding root cause meant stitching logs, traces, and metrics by hand during incidents. Now AI surfaces the hypothesis; engineers verify.
IT tracked just 26% of live events. Now, AI monitors everything and resolves bugs autonomously.
Three engineers, two products, 82M users — signals stitched by hand. AI now delivers root causes before they open their laptops.
Manual testing left engineers prioritizing fixes by intuition. Now, an AI classifier calculates error impact to target critical updates.
Reviewing handwritten, unstructured documents took handlers up to an hour per claim. Now, AI extracts and validates data automatically.
Agents spent up to 6 minutes writing inconsistent notes. Now, AI instantly summarizes 55,000 weekly calls directly into the CRM.
Finding root cause meant stitching logs, traces, and metrics by hand during incidents. Now AI surfaces the hypothesis; engineers verify.
30-minute report-digging kept fleet audits chronically deferred. Now one prompt delivers every device, flag, and follow-up emails at once.