AI Infrastructure to Power Telecommunications Networks

NeuralMesh™ supports major shifts in telecom: AI moving inside the network and GPU infrastructure as a revenue stream.

USE CASES

One Data Layer for Every AI Workload in Telecommunications

Network telemetry, GPU training, and inference all hit the same storage at once. NeuralMesh serves every tier from one architecture, so speed for one workload never comes at the cost of another.

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AI-Native Network Operations

Feed near real time RIC (Radio Access Network (RAN) Intelligent Controller) decisions and edge inference at the speed a 10-millisecond control loop demands.

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AI Infrastructure & GPU Monetization

Turning fiber, spectrum, and data center footprint into GPU-as-a-service infrastructure takes isolation and SLAs every tenant can trust. NeuralMesh delivers both.

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AI-Powered Customer & Network Operations

Give field technicians instant, grounded answers from technical documentation, network topology, and vendor manuals during tower and edge deployments.

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Agentic Customer Care

Let AI agents read subscriber tickets, check networking diagnostics, confirm billing history, and resolve or escalate, without storage lag slowing every step.

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Fraud, Churn & Account Takeover

Run fraud, churn, and SIM-swap account takeover models on real time network and subscriber account data, not a stale batch export.

Telecom AI Factory Performance, Proven at Scale

From GPU utilization to decreased costs, these are NeuralMesh benchmarks from WEKA customer AI workload deployments.

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GPU utilization achieved
Drives GPU utilization to over 95%, versus roughly 30% on legacy protocols, on comparable AI factory infrastructure
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Faster metadata throughput
10x faster metadata throughput than legacy all-flash NAS for small-file, high-frequency network telemetry.
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Lower infrastructure cost
Up to an 86% reduction in infrastructure cost on comparable telecom AI workloads.
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Latency from edge to core
Sub-millisecond latency sustained across distributed edge-to-core deployments.

WHY WEKA

Get More AI Out of the Network You Already Built

WEKA's NeuralMesh delivers sub-millisecond performance from edge to core to feed real-time network AI and turn shared infrastructure into a GPU revenue platform.

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Feed Telecommunication AI at Network Speed
NeuralMesh sustains millions of IOPS at sub-millisecond latency, reacts to signals instantly, resolves decisions in 10ms, and adjusts network signals fast.

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Feed Telecommunication AI at Network Speed
NeuralMesh sustains millions of IOPS at sub-millisecond latency, reacts to signals instantly, resolves decisions in 10ms, and adjusts network signals fast.
Unify Telemetry Data From Edge to Core to Cloud
NeuralMesh reads S3 and POSIX as one namespace, skips full-dataset copies, hydrates data only on access, and transfers it zero-copy straight to GPU memory.
Run More AI Without a New Build
NeuralMesh Axon™ tiers TLC and QLC flash automatically, cuts capacity needs by up to 6x, adds compute instead of concrete, and lowers cost per terabyte by up to 40%.

Provision Tenants in Under 30 Minutes
NeuralMesh dedicates hardware per tenant, scales to 50,000+ tenants on one cluster, bills with per-tenant QoS, and supports overlapping IP address spaces, all in under 30 minutes.
Secure Every Tenant Independently
Every tenant uses unique encryption keys, authenticates independently, and receives guaranteed service, so no tenant's workload is ever starved by another's.
Deploy in Hours, Not Weeks
NeuralMesh Kubernetes Operator automates the cluster lifecycle, retires manual file system tuning, and lets NeuralMesh Observe watch every deployment live.

Keep Data Sovereign
NeuralMesh encrypts every byte at rest, eliminates the performance tax residency requirements usually force, and meets residency mandates natively.
Serve 10x Concurrent Users
NeuralMesh caches key-value pairs in memory, extends GPU memory 1000x, and speeds time to first token by up to 20x.
Act Within Your Network's Latency Budget
NeuralMesh holds latency steady even at peak network load, so edge AI decisions land inside the network's timing budget instead of occasionally missing it.

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