Parallel Processing Voxel Space

Compute trillions of voxels at cloud scale

Massive spatial datasets used to demand massive compute time. Hours of processing. Overnight jobs. Slow iterations. And when models change daily , or hourly, that delay becomes costly.

Voxel Space eliminates these bottlenecks.

Using cloud-scale parallelism, Spatial Lambdas distribute computation across hundreds of thousands of active nodes, accelerating analysis of the largest volumetric datasets. The result: fast, decision-ready insight from data volumes that would overwhelm traditional systems. This is volumetric computation built for modern operations.

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Analyze and Simulate at Scale

Voxel Space makes high-complexity workflows practical

What once required specialized HPC clusters can now be performed directly inside your Digital Twin.

Run volumetric analytics across billions or trillions of voxels
Classify materials using AI and machine learning

Detect anomalies, voids, and geotechnical risks
Simulate stress, fluid flow, or excavation scenarios

Cloud-Native Parallel Processing

Spatial Lambdas are serverless functions designed to run where your data lives, in the cloud, without moving or downscaling massive datasets. Just pure, distributed, cloud-powered computation across your volumetric Digital Twin.

No local hardware dependency

No workstation bottlenecks

No exporting or reprocessing required

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Speed for data-driven operations

Dramatically Faster Results

Spatial Lambdas accelerate operations that traditionally take hours, or days, into workflows that return results dramatically faster, empowering rapid decision-making across complex environments.

Run analytics

and simulations at cloud performance

Receive results

faster than traditional batch workflows

Iterate quickly

quickly as new data is acquired

Voxel Space

Built for Continuous, High-Volume Data Streams

Modern mines, cities, infrastructure systems, and environmental operations generate data nonstop. Your operations don’t pause — neither should your models. Spatial Lambdas were built specifically for this reality.

Designed for real-time or near real-time updates
Ideal for daily or hourly resurvey workflows

Handles dynamic, evolving Digital Twins
Enables rapid re-computation when new scans data arrive

Why it matters

Massive-Scale Computation

With breakthrough cloud-parallelism, teams gain:

Rapid insight into complex environments

Results derived at full volumetric resolution

Agility to iterate quickly in fast-changing conditions

Predictive power through simulation and modeling

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