Technology
Technology
Our Platform
Our Platform
↳
Our vertically integrated AI factory simplifies visual intelligence upgrades in four key steps.
[01]
Generate
Sarab transforms limited real-world datasets into full training libraries by generating high-fidelity synthetic data. It creates sensor-accurate imagery that replicates specific hardware outputs, including RGB and IR formats across multiple biomes and lighting conditions. This automated pipeline accelerates model development by delivering 50000+ fully annotated images per day, eliminating the logistical burdens of manual data collection and labeling.
[01]
Generate
Sarab transforms limited real-world datasets into full training libraries by generating high-fidelity synthetic data. It creates sensor-accurate imagery that replicates specific hardware outputs, including RGB and IR formats across multiple biomes and lighting conditions. This automated pipeline accelerates model development by delivering 50000+ fully annotated images per day, eliminating the logistical burdens of manual data collection and labeling.
[02]
Train
VisionEdge automates the fine-tuning and optimization of domain-specific vision AI models for diverse hardware architectures. The platform employs a comprehensive MLOps pipeline targeting specific device constraints. This process utilizes 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 automates the fine-tuning and optimization of domain-specific vision AI models for diverse hardware architectures. The platform employs a comprehensive MLOps pipeline targeting specific device constraints. This process utilizes 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 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 create fully modeled autonomous systems and environments. The platform subjects models to extensive stress testing within these virtualized worlds to guarantee reliability across deployment conditions.
[03]
Test
SimQC validates mission-critical performance by immersing 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 create fully modeled autonomous systems and environments. The platform subjects models to extensive stress testing within these virtualized worlds to guarantee reliability across deployment conditions.
[04]
Deploy
Our deployment services prioritize portability through containerized software solutions that allow for flexible implementation across diverse architectures. 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
Our deployment services prioritize portability through containerized software solutions that allow for flexible implementation across diverse architectures. 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.
[01]
Generate
Sarab transforms limited real-world datasets into full training libraries by generating high-fidelity synthetic data. It creates sensor-accurate imagery that replicates specific hardware outputs, including RGB and IR formats across multiple biomes and lighting conditions. This automated pipeline accelerates model development by delivering 50000+ fully annotated images per day, eliminating the logistical burdens of manual data collection and labeling.
[01]
Generate
Sarab transforms limited real-world datasets into full training libraries by generating high-fidelity synthetic data. It creates sensor-accurate imagery that replicates specific hardware outputs, including RGB and IR formats across multiple biomes and lighting conditions. This automated pipeline accelerates model development by delivering 50000+ fully annotated images per day, eliminating the logistical burdens of manual data collection and labeling.
[02]
Train
VisionEdge automates the fine-tuning and optimization of domain-specific vision AI models for diverse hardware architectures. The platform employs a comprehensive MLOps pipeline targeting specific device constraints. This process utilizes 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 automates the fine-tuning and optimization of domain-specific vision AI models for diverse hardware architectures. The platform employs a comprehensive MLOps pipeline targeting specific device constraints. This process utilizes 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 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 create fully modeled autonomous systems and environments. The platform subjects models to extensive stress testing within these virtualized worlds to guarantee reliability across deployment conditions.
[03]
Test
SimQC validates mission-critical performance by immersing 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 create fully modeled autonomous systems and environments. The platform subjects models to extensive stress testing within these virtualized worlds to guarantee reliability across deployment conditions.
[04]
Deploy
Our deployment services prioritize portability through containerized software solutions that allow for flexible implementation across diverse architectures. 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
Our deployment services prioritize portability through containerized software solutions that allow for flexible implementation across diverse architectures. 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
RECONNAISANCE
RECONNAISANCE
ASSET COORDINATION
ASSET COORDINATION
SAFETY
SAFETY
Foundation Model Architectures
Our foundation model architectures are agentic AI pipelines integrating vision-language-action models, tools, hardware-software interfaces, and other services. They serve as starting points for post-training, further service integration, and optimization to achieve specific customer end-goals. They are created from the ground up in partnership with experts in the field, helping solve key issues out of the box.
Ember
Ember Flame
The next evolution in precision wildfire detection, delivering enhanced intelligence for both on-board and dock-borne operations. Ember Flame is engineered to provide >95% context-aware accuracy in smoke and fire detection and infrastructure assessment. Created in partnership with the University of Maryland Department of Fire Protection Engineering, Ember Flame ensures rapid identification of threats, enabling faster response times while minimizing false positives in critical wildfire scenarios.
Ember
Ember Flame
The next evolution in precision wildfire detection, delivering enhanced intelligence for both on-board and dock-borne operations. Ember Flame is engineered to provide >95% context-aware accuracy in smoke and fire detection and infrastructure assessment. Created in partnership with the University of Maryland Department of Fire Protection Engineering, Ember Flame ensures rapid identification of threats, enabling faster response times while minimizing false positives in critical wildfire scenarios.
Ember
Ember Flame
The next evolution in precision wildfire detection, delivering enhanced intelligence for both on-board and dock-borne operations. Ember Flame is engineered to provide >95% context-aware accuracy in smoke and fire detection and infrastructure assessment. Created in partnership with the University of Maryland Department of Fire Protection Engineering, Ember Flame ensures rapid identification of threats, enabling faster response times while minimizing false positives in critical wildfire scenarios.
Ember
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, coordinate other drones, 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
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, coordinate other drones, 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
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, coordinate other drones, 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.
Talos
Talos Light
Engineered for the strict SWaP (Size, Weight, and Power) constraints of Group 1 and 2 UAVs, Talos Light brings agentic AI capabilities to the tactical edge. This pipeline delivers SOTA zero-shot detection and target recognition onboard. Talos Light maintains full operational continuity in comms-denied scenarios for immediate threat identification. By leveraging synthetic training data from Sarab, the model is pre-validated on rare threat signatures and complex targets, providing field units with reliable, autonomous intelligence in dynamic combat zones.
Talos
Talos Light
Engineered for the strict SWaP (Size, Weight, and Power) constraints of Group 1 and 2 UAVs, Talos Light brings agentic AI capabilities to the tactical edge. This pipeline delivers SOTA zero-shot detection and target recognition onboard. Talos Light maintains full operational continuity in comms-denied scenarios for immediate threat identification. By leveraging synthetic training data from Sarab, the model is pre-validated on rare threat signatures and complex targets, providing field units with reliable, autonomous intelligence in dynamic combat zones.
Talos
Talos Light
Engineered for the strict SWaP (Size, Weight, and Power) constraints of Group 1 and 2 UAVs, Talos Light brings agentic AI capabilities to the tactical edge. This pipeline delivers SOTA zero-shot detection and target recognition onboard. Talos Light maintains full operational continuity in comms-denied scenarios for immediate threat identification. By leveraging synthetic training data from Sarab, the model is pre-validated on rare threat signatures and complex targets, providing field units with reliable, autonomous intelligence in dynamic combat zones.
Talos
Talos Heavy
A robust ISR intelligence engine designed for Group 2 and 3 platforms, offering deep analytical capabilities that extend beyond simple detection, Talos Heavy acts as an autonomous analyst aloft. This agentic pipeline integrates advanced contextual inference to perform Battle Damage Assessment (BDA) and structural analysis onboard. Like its counterpart, Talos Heavy operates independently of ground stations, ensuring critical decision-making support during electronic warfare or bandwidth-constrained operations.
Talos
Talos Heavy
A robust ISR intelligence engine designed for Group 2 and 3 platforms, offering deep analytical capabilities that extend beyond simple detection, Talos Heavy acts as an autonomous analyst aloft. This agentic pipeline integrates advanced contextual inference to perform Battle Damage Assessment (BDA) and structural analysis onboard. Like its counterpart, Talos Heavy operates independently of ground stations, ensuring critical decision-making support during electronic warfare or bandwidth-constrained operations.
Talos
Talos Heavy
A robust ISR intelligence engine designed for Group 2 and 3 platforms, offering deep analytical capabilities that extend beyond simple detection, Talos Heavy acts as an autonomous analyst aloft. This agentic pipeline integrates advanced contextual inference to perform Battle Damage Assessment (BDA) and structural analysis onboard. Like its counterpart, Talos Heavy operates independently of ground stations, ensuring critical decision-making support during electronic warfare or bandwidth-constrained operations.
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