When routers and switches fail at 3 AM, hours spent parsing syslogs mean prolonged outages and frustrated NOC teams.
AI automates log parsing and telemetry correlation to identify network equipment failures in minutes instead of hours. Support engineers get root cause analysis with guided resolution steps, eliminating manual syslog searches and reducing escalation rates.
Support engineers spend hours manually searching through SNMP traps, syslogs, and error logs across multiple network devices. Correlating events across routers, switches, and firewalls requires jumping between tools and hoping patterns emerge.
Different equipment vendors require different remote access tools. Support engineers toggle between SSH terminals, web GUIs, SNMP managers, and proprietary diagnostic utilities just to gather basic troubleshooting data.
Without clear root cause identification, support engineers escalate to senior staff or dispatch field service prematurely. Each escalation adds delay, increases costs, and extends customer downtime during network outages.
Bruviti ingests telemetry from routers, switches, firewalls, and optical transport systems in real time. The platform automatically parses syslogs, SNMP traps, NetFlow data, and performance metrics to identify anomalies without manual searches. When an incident occurs, AI correlates events across multiple devices to pinpoint root cause—distinguishing between firmware bugs, configuration drift, hardware degradation, and connectivity issues.
Support engineers receive a pre-built diagnostic summary with probable cause, affected components, and recommended resolution steps. The system auto-populates case notes, suggests configuration rollbacks or firmware patches, and flags when remote resolution isn't feasible. This eliminates hours of log hunting and reduces unnecessary escalations by presenting clear next steps within the first minutes of a remote session.
Network equipment OEMs support customers running thousands of devices across distributed NOCs with 24/7 uptime requirements. A single misdiagnosed router failure can cascade into service-level agreement penalties and lost customer trust. Traditional remote support relies on senior engineers who can mentally correlate syslog patterns, SNMP trap sequences, and historical failure modes—knowledge that isn't documented or shared.
AI-driven diagnostics treat every remote session as a learning opportunity. The platform captures how experienced support engineers resolve complex multi-vendor incidents, then makes that expertise available to the entire team. When a BGP routing loop or optical transport failure occurs, the system surfaces similar past incidents with successful resolutions, turning every support engineer into an expert diagnostician regardless of tenure.
The platform ingests telemetry from all connected devices and applies temporal correlation to event sequences. It distinguishes between upstream failures that cascade downstream versus independent simultaneous issues. By analyzing syslog timestamps, SNMP trap ordering, and configuration changes, AI isolates the originating fault and surfaces it to support engineers with contextual evidence.
The platform can analyze telemetry data that's already flowing out of customer networks via existing SNMP, NetFlow, or syslog forwarding. Even without interactive SSH or GUI access, AI correlates available logs and metrics to narrow probable causes. When remote connectivity is restored, support engineers start with pre-analyzed diagnostics instead of from scratch.
Root cause analysis completes within 2-5 minutes of initiating a remote session, depending on log volume and device count. The system presents findings in real time as telemetry streams in, so support engineers see probable causes and recommended actions while still gathering initial customer details. This eliminates the traditional delay between data collection and analysis.
Yes. The AI platform learns from OEM-specific log formats, error codes, and telemetry schemas. During onboarding, it ingests historical incident data including proprietary diagnostics to build equipment-specific models. Support engineers can also annotate new failure modes as they're discovered, continuously expanding the platform's diagnostic capabilities for custom hardware.
By surfacing root cause with high confidence and recommending tested resolution steps, junior support engineers can resolve incidents that previously required expert escalation. The platform flags cases where remote resolution isn't feasible based on symptom patterns, so escalations happen only when truly necessary. This frees senior staff to focus on novel failures instead of routine diagnostics.
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See how Bruviti turns hours of manual diagnosis into minutes of guided resolution.
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