ATLAS

ATLAS

Real-world-grade data generation
for physical AI.

Real-world-grade data generation
for physical AI.

ATLAS product interface for uploading thermal reconnaissance footage.
ATLAS product interface for uploading thermal reconnaissance footage.

Real-World-Grade by Design

Real-World-Grade by Design

Atlas matches EO and IR sensor characteristics, avoids the sim-to-real gap, and produces data ready for training without post-processing. The output is built for deployment conditions, not demos.

Atlas matches EO and IR sensor characteristics, avoids the sim-to-real gap, and produces data ready for training without post-processing. The output is built for deployment conditions, not demos.

ATLAS visualization demonstrating real-world-grade synthetic data generation.
ATLAS mobile visualization demonstrating real-world-grade synthetic data generation.
Animated ATLAS performance visualization showing model testing under varied conditions.
Animated ATLAS performance visualization showing model testing under varied conditions.

PERFORMANCE NO MATTER WHAT

PERFORMANCE NO MATTER WHAT

Performance is decided by the conditions least likely to appear in collected data. With Atlas, teams generate rare targets, degraded conditions, occlusion, concealment, camouflage, unusual viewpoints, low-pixel targets, swarms, sensor-specific variation, and other mission-specific edge scenarios on demand.

Performance is decided by the conditions least likely to appear in collected data. With Atlas, teams generate rare targets, degraded conditions, occlusion, concealment, camouflage, unusual viewpoints, low-pixel targets, swarms, sensor-specific variation, and other mission-specific edge scenarios on demand.

Proven Impact

Proven Impact

Atlas operates as an iterative system. New coverage is generated, models are retrained, and performance is evaluated against the scenarios that matter. Synthetic-only training has reached 97–99% of real-data baselines in customer evaluations, combined training has delivered gains above 10% in a single iteration, and zero-shot synthetic training has exceeded a 0.75 F1 score.

Atlas operates as an iterative system. New coverage is generated, models are retrained, and performance is evaluated against the scenarios that matter. Synthetic-only training has reached 97–99% of real-data baselines in customer evaluations, combined training has delivered gains above 10% in a single iteration, and zero-shot synthetic training has exceeded a 0.75 F1 score.

ATLAS impact visualization showing synthetic data performance results.
ATLAS impact visualization showing synthetic data performance results.

Delivery methods

Graphic representation of DiffuseDrive’s self-serve delivery method.

Self-serve

Fully air-gapped enterprise adoption, with all data staying in your environment and full control at scale.

Graphic representation of DiffuseDrive’s self-serve delivery method.

Self-serve

Fully air-gapped enterprise adoption, with all data staying in your environment and full control at scale.

Graphic representation of DiffuseDrive’s managed service delivery method.

Managed service

End-to-end data acquisition and delivery for your targeted use cases, with fast value and minimal internal lift.

Graphic representation of DiffuseDrive’s managed service delivery method.

Managed service

End-to-end data acquisition and delivery for your targeted use cases, with fast value and minimal internal lift.

See how DiffuseDrive helps teams generate missing data with more control, more relevant outputs, and less curation work.

See how DiffuseDrive helps teams generate missing data with more control, more relevant outputs, and less curation work.