Awetomaton provides a cloud‑agnostic platform that automates extraction of intelligence from optical, infrared, and hyperspectral remote‑sensing data using scalable deep‑learning pipelines. The system includes synthetic data generation, DevSecOps‑driven DoD ATO compliance, and searchable metadata APIs to enable rapid data discovery for defense and intelligence users.
Funding
Funding not disclosed
Founders
Product
Problem
National security agencies face an overwhelming influx of raw remote‑sensing data from satellite and airborne platforms, yet existing tools lack the scalability and automation needed to convert these volumes into timely, actionable intelligence. Additionally, transitioning experimental research into secure, cloud‑native operational systems is hampered by complex compliance and supply‑chain requirements.
Solution
Awetomaton delivers an end‑to‑end platform that fuses domain‑specific machine‑learning models with resilient cloud infrastructure to automate the extraction of insights from multi‑modal sensor feeds. Deep‑learning pipelines perform feature detection, pattern recognition, and anomaly detection at scale, while synthetic data generators produce high‑fidelity training sets for emerging sensor modalities. The platform is built with DevSecOps practices, providing automated governance, continuous security monitoring, and DoD Authority‑to‑Operate (ATO) compliance across hybrid cloud environments. Integrated search and discovery APIs expose richly tagged metadata, enabling analysts to locate relevant data instantly. By abstracting cloud‑provider specifics and embedding security controls as code, the solution accelerates the transition from research prototypes to production‑grade systems for defense and intelligence customers.
Target Audience
Primary customers are defense and intelligence agencies, as well as contracted R&D programs that require automated processing of large‑scale remote‑sensing data and secure, cloud‑native deployment of operational analytics.
Features
- Scalable deep‑learning models optimized for processing optical, infrared, and hyperspectral remote‑sensing streams in near real‑time
- Synthetic data generation engine that creates labeled training datasets for novel sensor configurations
- Cloud‑agnostic architecture with automated workload placement across commercial and government cloud regions, preserving hybrid portability
- DevSecOps pipeline that enforces policy‑as‑code, continuous compliance checks, and automated DoD ATO certification
- Real‑time security monitoring and supply‑chain integrity verification to detect and remediate threats before deployment
- Search and discovery service with metadata tagging, full‑text indexing, and RESTful APIs for rapid data retrieval
- Extensible SDKs and well‑documented APIs for integration with existing command‑and‑control and analytics tools