AI Cluster Reliability, Automated.

An AI-native operations system that continuously monitors your AI clusters, finds problems before they cause downtime, and fixes them automatically.

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The Problem

AI Clusters Fail Frequently

Reactive alerting & no failure prevention.

Manually triages hundreds of alerts.

Every failure is diagnosed from scratch.

A rack is down = $1,000s/hour lost revenue.

Meta Llama 3 405B Trainingevery 3 hoursbetween hardware failures, on average.

419 interruptions in 54 days. 54%+ caused by GPU or memory hardware failures.

OpsPilot closes this gap.

Key Value Propositions

OpsPilot

An AI-native operations system that continuously monitors your AI clusters, finds problems before they cause downtime, and fixes them automatically.

PILLAR 01

Failure Prevention

Safe Operating Envelope through calibration. Continuous monitoring & failure prediction. Auto-recalibration when drifts are detected.

PILLAR 02

Diagnosis & Remediation

Issue detection and root cause identification. End-to-end self-healing through AI agents. Learning from every incident.

PILLAR 03

Enterprise-Grade Security

On-premise deployment — data never leaves your facility. Enterprise guardrails on every action. Human-in-the-loop for critical actions.

How It Works

Four Stages. Fully Automated.

OpsPilot works continuously in the background. No manual intervention for routine issues.

01/

Calibrate

Establish performance baselines. Define Safe Operating Envelope. Build physical models for failure prediction.

02/

Monitor

Catch anomalies in real-time. Detect drifts before problems happen. Predict failures.

03/

Diagnose

Pinpoint root cause in minutes via XPerf-trained AI agent. Analyze telemetry trends to identify issues. Reuse knowledge from past incidents.

04/

Intervene

Prevent: take preventive actions and recalibrate when hardware drifts. Remediate: apply proven fix, requiring manual approval for critical actions.

Patent-Pending · Failure Prediction

A Safety Model for Every Node.

OpsPilot learns each node’s behaviour on day one, forecasts trouble 10–30 minutes ahead, and applies the lightest action to keep it safe.

01 · Custom Safety Model

A safety model per node on Day One

A one-hour calibration captures how the node behaves under load. Generic limits give way to its real boundaries.

02 · Predict, Then Prevent

See trouble 10–30 minutes early

OpsPilot projects each node's path and steps in with the lightest action. Training keeps running.

03 · Always Current

Refreshes the forecast every cycle

OpsPilot recomputes each node's forecast every few seconds with the latest telemetry, using the same per-node model.

Patent-Pending · Failure Prediction

A Coordinated Fleet. Self-Adapting at Scale.

Beyond each node, OpsPilot watches the whole fleet as one system. And it retunes those models whenever the system changes.

04 · Fleet-Wide Signals

Catch failures no single node would see

OpsPilot surfaces cluster-wide drift, like hot and cool zones forming across racks, even when every node still looks healthy on its own.

05 · Self-Tuning

Retunes when the system changes

Hardware aging, config updates, component swaps. OpsPilot widens safety margins live and queues a full recalibration when needed.

Failures caught before they cascade. Models that stay accurate as the cluster evolves.

Closed-Loop Remediation

Alert to Resolution

What used to take an on-call engineer hours of manual triage now runs end-to-end in minutes — fully autonomous, with you reviewing the audit trail.

Today·Manual triage

Alert fires at 3 AM.
On-call engineer wakes up.
Manually checks dashboards, logs, etc.
Searches for similar past issues.
Applies a fix, monitors, hopes it holds.

With OpsPilot·Autonomous

Alert fires. OpsPilot picks it up instantly.
Automatically queries cluster telemetry.
Cross-references with past incidents.
Identifies root cause within seconds.
Applies proven fix, requires approval for critical changes.

On-Premise & Secure AI Execution

Your Data Center, Your Data. Operating Securely.

Fully On-Premise Deployment

Runs entirely inside your infrastructure.
No cloud dependency. Works even offline.
Meets data sovereignty and compliance.
Cluster telemetry never transmitted externally.

Enterprise Safety Controls

Human-in-the-loop for critical actions.
Input guardrails prevent misuse & injection.
Output guardrails. No sensitive data exposed.
Full audit trail on every action taken.

Learns From Every Incident

The More You Use It, The Smarter It Gets

Incident Knowledge Base

Every resolution stored with root cause and fix.
Auto-recalls similar past incidents.
Diagnosis accelerates over time.
Never forgets, never leaves the company.

Predictive Safety Envelope

Baselines from calibration.
Continuously monitors for drifts.
Predicts failures and improves safe operating envelopes.
Auto-recalibrates as hardware evolves.

The only AIOps system with ground-truth performance models for your specific hardware.

Demo

See OpsPilot in action

ClusterReady Integration

ClusterReady is now integrated into OpsPilot

ClusterReady handles day-0 calibration and ongoing re-calibration. OpsPilot uses that baseline to run the cluster safely day after day.

Explore ClusterReady

Competitive Landscape

Comparison

Capability	NVIDIA NVSentinel	Grafana AI	Penguin ICE	Nebius Soperator	OCI GPU Scanner	Resolve.ai	2501.ai	OpsPilot
Hardware aware	Yes	Partial	Yes	Yes	Yes	No	No	Yes
On-premise	Yes	No	Yes	Yes	Yes	No	Yes	Yes
Predictive	No	Partial	Yes	No	Partial	No	No	Yes
AI diagnosis	No	Yes	Yes	No	No	Yes	Yes	Yes
Learns + HITL	No	No	No	No	No	Partial	No	Yes
Serves any data center	Yes	No	Partial	No	No	No	Yes	Yes
Limitation	No reasoning, no predictive	No GPU depth, no memory	Engineer service dependent	Nebius locked	Oracle locked	SaaS only	Not applicable to GPU	—

OpsPilot

Hardware aware: Yes
On-premise: Yes
Predictive: Yes
AI diagnosis: Yes
Learns + HITL: Yes
Serves any data center: Yes
Limitation: —

NVIDIA NVSentinel

Hardware aware: Yes
On-premise: Yes
Predictive: No
AI diagnosis: No
Learns + HITL: No
Serves any data center: Yes
Limitation: No reasoning, no predictive

Grafana AI

Hardware aware: Partial
On-premise: No
Predictive: Partial
AI diagnosis: Yes
Learns + HITL: No
Serves any data center: No
Limitation: No GPU depth, no memory

Penguin ICE

Hardware aware: Yes
On-premise: Yes
Predictive: Yes
AI diagnosis: Yes
Learns + HITL: No
Serves any data center: Partial
Limitation: Engineer service dependent

Nebius Soperator

Hardware aware: Yes
On-premise: Yes
Predictive: No
AI diagnosis: No
Learns + HITL: No
Serves any data center: No
Limitation: Nebius locked

OCI GPU Scanner

Hardware aware: Yes
On-premise: Yes
Predictive: Partial
AI diagnosis: No
Learns + HITL: No
Serves any data center: No
Limitation: Oracle locked

Resolve.ai

Hardware aware: No
On-premise: No
Predictive: No
AI diagnosis: Yes
Learns + HITL: Partial
Serves any data center: No
Limitation: SaaS only

2501.ai

Hardware aware: No
On-premise: Yes
Predictive: No
AI diagnosis: Yes
Learns + HITL: No
Serves any data center: Yes
Limitation: Not applicable to GPU

Get Started

Download OpsPilot

OpsPilot is rolling out to select GPU operators. Public downloads are coming soon.

Coming Soon

OpsPilot is launching to general availability

We’re onboarding early customers now. Get in touch to join the private preview or be notified at launch.

Request early access

About

About XPerf Inc.

Founded by ex-Intel engineers with extensive experience deploying clusters with thousands of accelerators, XPerf Inc. is the control plane for data centers — AI infrastructure software for GPU cluster performance validation and optimization, taking an AI-native approach to cluster operation problems. Austin / Round Rock, Texas.