Accelerating HPC and AI Workflows for Government Agencies

WEKA helps federal research, sovereign AI, security & surveillance operations, smart cities, aerospace & defense organizations, and other federally funded functions move faster with NeuralMesh™ – a high-performance, low latency storage solution built for AI training and inference.

The leading innovators leveraging AI training and inference for sovereign AI, federal research and real-time analysis are building with NeuralMesh.

The Optimal Storage Solution For Global Government Agencies

NeuralMesh is designed to remove infrastructure bottlenecks and deliver federal agencies the data security, microsecond latency, and overall throughput required for large-scale training and inference.

Build for Highly
Regulated Environments

Deliver end-to-end encryption with zero operator access, global sovereignty compliance, and high performance across on-prem and cloud environments

Maximize Infrastructure Utilization Rates

Feed compute seamlessly with a zero-copy, high-throughput data path to offer fast, frequent checkpointing and drive 90%+ GPU utilization during training and inference

Accelerate AI Innovation and Real-Time Analysis

Maintain consistent sub-millisecond latency under multi-tenant conditions to process intel faster and drive more actionable decisions in real-time environments

Offer Unparalleled Performance Density

Deploy NeuralMesh™ Axon™ to store more in less rack space, reducing infrastructure footprint, energy use, and operational complexity with a single-tier design

Support Public-Private Partnerships

Leverage trusted relationships with Oracle, AMD, NVIDIA, HPE, and others to support public-private collaboration across federal and sovereign programs

Streamline End-to-End
AI Workflows

Host workflows from data ingestion to deployment in one trusted infrastructure, dynamically bursting compute and storage to reduce cost and complexity

NeuralMesh Delivers Speed and Reliability across Research, Real-Time Analysis, and Sovereign AI Workloads

Storage infrastructure must be flexible enough to support everything from long-term federal research to real-time security and surveillance workloads. Agencies and researchers that can cost-effectively perform this analysis save lives and NeuralMesh is built to meet that mission.

Federal Research

HPC environments for Federal Research require high bandwidth, IOPS, metadata throughput, and small file efficiency to support training, inferencing, and agentic workloads. With an increasing focus on tokenomics, frontier models, and future-proof storage, NeuralMesh is a clear answer.

Sovereign AI

Sovereign AI workloads demand highly performant and secure infrastructure for multi-tenant environments. NeuralMesh offers the ability to scale to millions of IOPS at sub-millisecond latency across hundreds of clients and multi-petabyte datasets to maximize GPU productivity and keep data secure.

Security & Surveillance

Security and surveillance workloads demand high-performance storage for real-time AI inference across defense and civilian agencies. NeuralMesh delivers the GPU efficiency, low-latency throughput, scalability, and resilience these mission-critical environments demand.

Real-Time Data Analysis & Processing

Real-time data analysis spans IoT, smart cities, defense, and intelligence workloads where agencies ingest data from cameras, sensors, and scientific instruments to drive split-second decisions. NeuralMesh delivers the speed and accuracy these mission-critical, life-dependent workloads demand.

NeuralMesh Offers A Proven Solution for Federal Research, Sovereign Cloud, and Real-Time Security and Surveillance Applications

Meet Our Customers

“NeuralMesh enabled us to deploy composable storage leveraging an operator-driven architecture that delivers secure multi-tenancy across our AI cloud infrastructure.”

Download the Case Study

“[We had to] accept performance of hyperscaler AI & surrender data sovereignty OR maintain control & settle for fragmented, underperforming systems. [WEKA & NVIDIA] eliminate this tradeoff.”

Choose NeuralMesh and Accelerate Research Outcomes and Secure Critical Data

NeuralMesh was built for environments where infrastructure performance directly affects federal research and real-time mission outcomes. WEKA supports large-scale data simulations by replacing legacy data file environments and accelerating GPU workloads. Ready to see what’s possible?

Test Drive NeuralMesh Today

FAQ

Common Questions, Straight Answers

Modern federal workloads generate enormous datasets. Signal processing, geospatial intelligence, climate modeling, and simulation pipelines require fast access to data to keep GPUs and CPUs fully utilized. High-performance storage removes latency between compute and data so teams can process information faster and reach mission outcomes sooner.

When storage cannot deliver data fast enough, GPUs and CPUs wait for input. This increases job runtimes and reduces overall infrastructure efficiency. Removing storage bottlenecks allows AI training jobs, simulations, and analytics pipelines to complete faster while improving GPU utilization.

WEKA can accelerate AI inferencing with ultra-low latency, high IOPS, and seamless GPU optimization using infrastructure designed for analytics and optimized for parallel access. This is where WEKA’s Augmented Memory Grid brings significant value.

Federal data volumes grow rapidly due to sensor arrays, high-resolution simulations, and intelligence analysis. NeuralMesh allows agencies to scale performance and capacity independently across on-premises environments, classified networks, and hybrid cloud deployments. This avoids large infrastructure redesigns as data volumes expand.

Modern platforms are designed to support strict encryption and governance requirements common in federal IT. With the WEKApod deployment strategy, it is easy to deploy and scale NeuralMesh with turnkey simplicity, streamlining compliance processes for infrastructure teams.

AI workloads depend on continuous data delivery. If storage cannot keep up, GPUs remain idle during training and inference. NeuralMesh Augmented Memory Grid enables parallel data access across thousands of compute cores. GPUs stay fully utilized throughout the training pipeline, reducing job time and improving infrastructure efficiency.

Federal data centers often operate under strict power and space limits. Distributed architectures that deliver higher throughput per watt allow agencies to run larger AI and HPC workloads without expanding data center footprint or energy consumption.

Legacy systems require many nodes, network ports, and massive rack space, inflating hardware costs. Deploying an architecture designed for the entire AI lifecycle reduces the physical footprint and eliminates storage stalls during training and inference.

At scale, hardware failures are unavoidable. Utilizing a distributed platform helps maintain application performance and reduces downtime during hardware failures by removing prolonged degraded states, ensuring deterministic behavior so mission-critical applications stay online.