We build the AI systems behind GPU rack onboarding, unified telemetry, real-time observability, and autonomous operations for data centers. Deployed in production at scale — not proof-of-concept.
Data center operators running GPU clusters face a specific set of AI problems that general-purpose consulting firms aren't equipped to solve: heterogeneous vendor telemetry that doesn't normalize naturally, GPU rack onboarding that takes weeks instead of hours, SIEM pipelines that don't scale to operational data volume, and an ambition for agentic operations that most organizations can't safely implement without the right architecture.
The organizations that get ahead in AI-era operations are the ones who move from reactive dashboards to proactive reasoning — validated infrastructure before it powers on, anomalies explained before they become incidents, remediation executed before the pager fires.
Our EdgeTelemetry product reduces GPU rack onboarding from weeks to hours. Automated hardware discovery, vendor-normalized telemetry ingestion, configuration validation against readiness criteria, and a signed operational handoff. What used to take weeks of SRE back-and-forth is now an automated validation run.
Real-time telemetry ingestion from GPUs, hosts, cooling, power, and network fabric — normalized to a single operational schema. One view across your entire infrastructure, regardless of vendor mix. Built on Kafka, Spark, and your existing data warehouse.
Reasoning layers built on Claude that interpret telemetry state, follow operational runbooks, execute remediation via tool use, and escalate to humans with full context. Designed to be auditable and defensible in front of your operations and compliance teams.
Tell us about your infrastructure environment — rack count, vendor mix, onboarding pain, current observability stack. We'll tell you where we'd start and what we'd need to deliver.