By Kandan Kathirvel, CEO, MantisGrid AI, and Muckai Girish, CEO, Rockfish Data

AI workloads are exploding in scale and fragility, driven by widespread AI adoption and the prevalence of AI agents. Training pipelines run across hundreds of GPUs, span hours or days, and depend on flawless execution across nodes, drivers, networks, and storage layers. One GPU hiccup — a memory spike, a misconfigured driver, a delayed heartbeat — can reset an entire run.

Rockfish Data sits at the center of this challenge and is a well-established technology leader in this space. Its mission is to accelerate enterprise AI development and deployment through large-scale synthetic dataset generation. To achieve this, Rockfish operates some of the most demanding distributed AI/ML workloads running today.

MantisGrid AI operates at the next layer up — delivering predictive reliability intelligence that boosts performance, prevents outages, and keeps modern AI infrastructure running smoothly.

MantisGrid AI senses, predicts, and prevents failures and performance degradation across training, inference, and data pipelines — and continuously detects configuration drift, emerging vulnerabilities, and compliance risks across the stack.

Individually, each company tackles a hard problem.

Together, they create a powerful closed loop for enterprises.

Why Rockfish Data Needs Predictive Reliability

Enterprise-grade synthetic data generation is not a lightweight process, especially when it comes to generating time-series and transaction data on a continuous basis. Rockfish routinely runs:

• multi-node training pipelines
• high-throughput data-generation workloads
• GPU-dense clusters pushed to saturation
• long-duration jobs where failure is extremely expensive

In these environments, even a single unstable GPU, container, or node can force a complete job restart — wasting compute, delaying output, and driving unpredictable costs.

This is exactly where MantisGrid AI operates. Its AI-native reliability engine continuously identifies the subtle early signals that often precede failures, including:

• configuration drift
• GPU driver and hardware instability
• memory pressure
• network bottlenecks
• node degradation
• container runtime anomalies

Instead of learning after a failure, MantisGrid AI detects risks early and resolves them (with approval) before training collapses.

For Rockfish, this translate to fewer restarts, smoother long-running pipelines, higher GPU efficiency, and predictable end-to-end throughput — all critical for scaling AI operations reliably.

Why MantisGrid AI Needs Synthetic Reliability Data

Building predictive reliability models requires a rare resource: high-fidelity failure data.

Real-world outages occur infrequently — and when they do, they’re noisy, incomplete, and expensive to replicate.

Rockfish solves this gap.

By generating structured, repeatable, and controlled synthetic reliability scenarios, Rockfish strengthens the training signals for our reliability graph AI model — the predictive engine behind MantisGrid AI.

Across thousands of synthetic events, we can model:

• GPU faults
• node crashes
• driver anomalies
• container failures
• network degradation
• and other real-world failure patterns

These synthetic signals augment, not replace, production telemetry. Together, they provide the depth and diversity needed to improve prediction accuracy, reduce false positives, and strengthen the reliability intelligence powering modern AI infrastructure.

A Symbiotic Loop

This partnership creates a self-reinforcing cycle:

• Rockfish Data gains predictive reliability — fewer failures, tighter GPU utilization, and more predictable high-volume workloads.
• MantisGrid AI gains deeper training data — strengthening our reliability graph model and enhancing failure prediction across distributed AI systems.

One accelerates AI with synthetic data.

One protects it with predictive, preventive intelligence.

Together, they shape a more resilient AI future — where models not only run faster, but run smarter, safer, and far more predictably at scale. 

Enterprises and Agentic AI builders globally can benefit greatly from this partnership.