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Enterprise Layer Intelligence
Categories/AI Orchestration & MLOps/Striveworks
Striveworks

Striveworks

Founded 2018

Build, deploy, and monitor machine learning models across cloud and edge environments

You can use Striveworks to build, deploy, and maintain machine learning models across cloud and edge systems. The system helps teams track model performance, detect data drift, and update models when operational conditions change. It specializes in automated target recognition and situational awareness by combining satellite imagery, edge sensor video, and electronic signals. Teams can deploy models to low-compute or disconnected environments with a single click, ensuring computer vision and predictive models continue to run reliably in defense and enterprise operations.

What Striveworks does

Deploy machine learning models to remote edge hardwareMonitor live model predictions for data driftClassify objects in aerial drone and satellite video streamsFuse sensor feeds with electronic intelligence dataRetrain models automatically when environmental conditions shiftPackage AI models for disconnected operational environmentsDeploy models to cloud and disconnected edge devices with one clickFuse satellite imagery, drone video feeds, and radio signalsTrack target custody across varying sensor perspectives and anglesMonitor model performance and detect drift in productionRetrain and update models continuously without system downtimeRun vision models in compute-constrained operational environments

Frequently asked

Azure Cognitive Services, AWS / Google Cloud, Kubernetes (AKS/EKS), Hugging Face, OpenAI-compatible APIs, Geospatial data formats, Python SDK (chariot-client), OAuth 2.0 / SAML

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Striveworks, MLOps, AIOps, model monitoring, edge AI deployment, automated target recognition, computer vision, model drift, geospatial data, sensor fusion, defense AI