Hyperscale customers demand four-nines uptime, yet support engineers waste 30+ minutes per session parsing BMC logs and IPMI data.
AI analyzes BMC telemetry and log patterns in seconds, presents root cause with resolution steps, and auto-populates session notes—reducing remote session duration by 65% while cutting escalations by 40%.
Support engineers manually parse thousands of lines of BMC logs, IPMI data, and thermal sensor readings to identify root cause. Each remote session requires switching between log viewers, vendor documentation, and internal wikis.
Solutions to recurring issues exist only in senior engineers' heads or buried in past case notes. New hires repeatedly ask for help with problems that were solved months ago, creating bottlenecks and frustration.
Remote sessions require juggling BMC consoles, IPMI tools, vendor portals, and ticketing systems. Engineers lose context switching between interfaces, miss critical alerts, and waste time re-entering data across platforms.
The platform ingests BMC telemetry, IPMI alerts, thermal data, and RAID logs in real time. When a remote session starts, it automatically correlates patterns across components—power supply voltage drops, memory ECC errors, cooling system anomalies—and identifies root cause before the engineer finishes reading the case description.
Instead of manually parsing logs, support engineers see a fully analyzed diagnosis with step-by-step resolution guidance. The system auto-populates session notes, suggests preventive actions for similar servers, and flags when a hardware swap is needed. No more switching between seven tools or escalating to senior staff for problems the platform has seen before.
Data center OEMs support hyperscale customers managing tens of thousands of servers where a single component failure can cascade into SLA penalties. Support engineers need to diagnose power distribution issues, thermal hotspots, and RAID degradation before customers notice performance drops.
The platform parses BMC and IPMI data streams to detect early-stage failures—memory ECC spikes indicating imminent DIMM failure, PDU voltage irregularities, or RAID controller anomalies. Engineers receive pre-analyzed diagnostics with server-specific resolution steps, part numbers for replacements, and predictive alerts for similar hardware in the fleet. Sessions that previously required senior escalation now resolve in a single contact.
The platform parses thousands of log lines per second, cross-references historical patterns from similar hardware, and correlates multi-component telemetry that would take engineers 30+ minutes to analyze manually. It identifies root cause by matching current symptoms against resolved incidents across the entire fleet.
The system flags the incident as novel, surfaces the most similar historical cases, and escalates with full diagnostic context already assembled. When the senior engineer resolves it, the platform captures that resolution to handle similar patterns automatically next time.
Yes. The platform works alongside your current BMC consoles, IPMI tools, and ticketing systems. It ingests telemetry via API, presents diagnostics in your existing workflow, and auto-populates case notes back into your CRM or ticketing platform.
Track three metrics over 90 days: average remote session duration, escalation rate to senior engineers, and remote resolution rate. Data center OEMs typically see 50-70% reduction in session time and 35-45% drop in escalations within the first quarter.
The platform shows its reasoning—which telemetry patterns triggered the diagnosis, similar past incidents, and confidence level. Engineers review and approve before acting. Over time, as they see consistent accuracy, trust builds and they rely on AI for routine cases while focusing expertise on complex problems.
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