[01]

Generate

Sarab transforms limited real-world datasets into robust training libraries by generating high-fidelity synthetic data. Sarab creates sensor-accurate imagery that replicates specific hardware outputs, including RGB and infrared formats across multiple biomes, lighting conditions, and angles. This automated pipeline accelerates model development by delivering tens of thousands of fully annotated images per day, eliminating the financial and logistical burdens of manual data collection and labeling.

Service Image

[01]

Generate

Sarab transforms limited real-world datasets into robust training libraries by generating high-fidelity synthetic data. Sarab creates sensor-accurate imagery that replicates specific hardware outputs, including RGB and infrared formats across multiple biomes, lighting conditions, and angles. This automated pipeline accelerates model development by delivering tens of thousands of fully annotated images per day, eliminating the financial and logistical burdens of manual data collection and labeling.

Service Image

[01]

Generate

Sarab transforms limited real-world datasets into robust training libraries by generating high-fidelity synthetic data. Sarab creates sensor-accurate imagery that replicates specific hardware outputs, including RGB and infrared formats across multiple biomes, lighting conditions, and angles. This automated pipeline accelerates model development by delivering tens of thousands of fully annotated images per day, eliminating the financial and logistical burdens of manual data collection and labeling.

Service Image

[02]

Train

VisionEdge accelerates the deployment of domain-specific AI by automating the fine-tuning and optimization of genAI vision models for diverse hardware architectures. The platform employs a comprehensive MLOps pipeline targeting specific device constraints. This process utilizes advanced quantization and agentic pipelines to ensure high-fidelity performance and state of the art capabilities across the NVIDIA ecosystem, including low SWaP and multi-device scenarios.

[02]

Train

VisionEdge accelerates the deployment of domain-specific AI by automating the fine-tuning and optimization of genAI vision models for diverse hardware architectures. The platform employs a comprehensive MLOps pipeline targeting specific device constraints. This process utilizes advanced quantization and agentic pipelines to ensure high-fidelity performance and state of the art capabilities across the NVIDIA ecosystem, including low SWaP and multi-device scenarios.

[02]

Train

VisionEdge accelerates the deployment of domain-specific AI by automating the fine-tuning and optimization of genAI vision models for diverse hardware architectures. The platform employs a comprehensive MLOps pipeline targeting specific device constraints. This process utilizes advanced quantization and agentic pipelines to ensure high-fidelity performance and state of the art capabilities across the NVIDIA ecosystem, including low SWaP and multi-device scenarios.

[03]

Test

SimQC validates mission-critical performance by immersing Vision Language Models in high-fidelity digital twins powered by NVIDIA Omniverse. This rigorous quality control layer constructs physics-informed environments from satellite imagery and photogrammetry to simulate complex scenarios for fully modeled autonomous systems. The platform ensures deployment readiness by subjecting models to extensive stress testing within these virtualized worlds to guarantee reliability across edge hardware architectures and deployment conditions.

[03]

Test

SimQC validates mission-critical performance by immersing Vision Language Models in high-fidelity digital twins powered by NVIDIA Omniverse. This rigorous quality control layer constructs physics-informed environments from satellite imagery and photogrammetry to simulate complex scenarios for fully modeled autonomous systems. The platform ensures deployment readiness by subjecting models to extensive stress testing within these virtualized worlds to guarantee reliability across edge hardware architectures and deployment conditions.

[03]

Test

SimQC validates mission-critical performance by immersing Vision Language Models in high-fidelity digital twins powered by NVIDIA Omniverse. This rigorous quality control layer constructs physics-informed environments from satellite imagery and photogrammetry to simulate complex scenarios for fully modeled autonomous systems. The platform ensures deployment readiness by subjecting models to extensive stress testing within these virtualized worlds to guarantee reliability across edge hardware architectures and deployment conditions.

[04]

Deploy

VisionEdge bridges the gap between model development and operational reality by partnering directly with client technical teams to ensure seamless integration into existing technology stacks and API specifications. Our deployment services prioritize portability through containerized software solutions that allow for flexible implementation across diverse architectures, ranging from high-power field base stations to low-power on-board edge modules. For immediate field readiness, we also offer ready-to-implement hardware configurations, optimizing Size, Weight, and Power (SWaP) constraints to deliver mission-critical performance instantly.

[04]

Deploy

VisionEdge bridges the gap between model development and operational reality by partnering directly with client technical teams to ensure seamless integration into existing technology stacks and API specifications. Our deployment services prioritize portability through containerized software solutions that allow for flexible implementation across diverse architectures, ranging from high-power field base stations to low-power on-board edge modules. For immediate field readiness, we also offer ready-to-implement hardware configurations, optimizing Size, Weight, and Power (SWaP) constraints to deliver mission-critical performance instantly.

[04]

Deploy

VisionEdge bridges the gap between model development and operational reality by partnering directly with client technical teams to ensure seamless integration into existing technology stacks and API specifications. Our deployment services prioritize portability through containerized software solutions that allow for flexible implementation across diverse architectures, ranging from high-power field base stations to low-power on-board edge modules. For immediate field readiness, we also offer ready-to-implement hardware configurations, optimizing Size, Weight, and Power (SWaP) constraints to deliver mission-critical performance instantly.

INSPECTION

INSPECTION

INSPECTION

RECONNAISANCE

RECONNAISANCE

RECONNAISANCE

ASSET COORDINATION

ASSET COORDINATION

ASSET COORDINATION

SAFETY

SAFETY

SAFETY

a high tech road

Model Families


Ember

Ember

Ember

When firefighting resources are stretched thin, false alarms waste precious time and assets. Ember, created in partnership with the University of Maryland's Department of Fire Protection Engineering, is our advanced wildfire intelligence model family that goes beyond simple fire detection. Trained across diverse environments and fire types with input from leading fire experts, Ember understands critical context: distinguishing controlled burns from wildfires, identifying threats to people and infrastructure, and autonomously directing response assets where they're needed most.

When firefighting resources are stretched thin, false alarms waste precious time and assets. Ember, created in partnership with the University of Maryland's Department of Fire Protection Engineering, is our advanced wildfire intelligence model family that goes beyond simple fire detection. Trained across diverse environments and fire types with input from leading fire experts, Ember understands critical context: distinguishing controlled burns from wildfires, identifying threats to people and infrastructure, and autonomously directing response assets where they're needed most.

Ember Flame:
The next evolution in precision wildfire detection, delivering enhanced intelligence for both on-board and dock-borne operations. Ember Spark engineered to provide superior accuracy in smoke and fire detection and context-aware infrastructure detection. By leveraging high-speed contextual analysis through on-board compute capabilities, Ember Flame ensures rapid identification of threats, enabling faster response times while minimizing false positives in critical wildfire scenarios.

Ember Flame:
The next evolution in precision wildfire detection, delivering enhanced intelligence for both on-board and dock-borne operations. Ember Spark engineered to provide superior accuracy in smoke and fire detection and context-aware infrastructure detection. By leveraging high-speed contextual analysis through on-board compute capabilities, Ember Flame ensures rapid identification of threats, enabling faster response times while minimizing false positives in critical wildfire scenarios.

Ember Blaze:
Upgrading the capabilities of Ember Flame into a comprehensive command and control engine, Ember Blaze can, in addition to advanced fire and smoke detection, execute full FCCS fuel classification, structural analysis, and long-form report generation directly from the field. Going beyond simple detection, Ember Blaze turns base stations into central hubs for fleet management and risk modeling, integrating directly with simulation APIs to predict fire behavior and optimize resource allocation based on physics-informed simulations.

Ember Blaze:
Upgrading the capabilities of Ember Flame into a comprehensive command and control engine, Ember Blaze can, in addition to advanced fire and smoke detection, execute full FCCS fuel classification, structural analysis, and long-form report generation directly from the field. Going beyond simple detection, Ember Blaze turns base stations into central hubs for fleet management and risk modeling, integrating directly with simulation APIs to predict fire behavior and optimize resource allocation based on physics-informed simulations.

Gorgon (Coming Soon)

Gorgon (Coming Soon)

Gorgon (Coming Soon)

The Gorgon model family brings advanced visual intelligence directly to the tactical edge. Deployed on drones and autonomous platforms, it delivers real-time ISR and battle damage assessment without relying on cloud connectivity or remote processing. From identifying targets and assessing structural damage to conducting search operations in contested environments, Gorgon provides warfighters with immediate, actionable intelligence when communication links are denied.

Gorgon Light:

Engineered for the strict SWaP (Size, Weight, and Power) constraints of Class 1 and 2 UAVs, Gorgon Light brings agentic VLM capabilities to the tactical edge. This agentic pipeline delivers real-time zero-shot detection and target recognition without reliance on cloud connectivity. Designed for contested environments, Gorgon Light processes visual data locally to maintain full operational continuity in comms-denied scenarios, significantly reducing latency for immediate threat identification. By leveraging synthetic training data from the Sarab pipeline, the model is pre-validated on rare threat signatures and complex targets using battlefield data, providing field units with reliable, autonomous intelligence in dynamic combat zones

Gorgon Heavy:

A robust ISR intelligence engine designed for Long Endurance Class 2 and 3 platforms, offering deep analytical capabilities that extend beyond simple detection, Gorgon heavy acts as an autonomous analyst aloft. This agentic pipeline integrates advanced contextual inference to perform immediate Battle Damage Assessment (BDA) and structural analysis, converting raw aerial feeds into detailed, long-form intelligence reports while still airborne. Capable of executing complex instructions and context-informed targeting analysis, Gorgon Heavy operates independently of ground stations, ensuring critical decision-making support during electronic warfare or bandwidth-constrained operations. The system creates a strategic edge by fusing multi-modal data to monitor complex environments and identify targets with zero-shot precision.