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Multi-domain Awareness Advisor
Enabling Decision Dominance
MDAA helps operators achieve decision dominance in contested multi-domain environments by shortening Observe, Orient, Decide, and Act (OODA) loops via scalable AI-based and hardware-agnostic software that integrates and contextualizes real-time, multi-INT data for analysts and warfighters.
Increasing Weapons System Readiness
Scale Operability
Scalable for use across echelons and geographies, from operations centers to capital assets and to the tactical edge, MDAA uses and deconflicts classified, unclassified, and commercial data within existing processes.
Extend Awareness
MDAA enhances situational awareness and streamlines decision-making by leveraging AI/ML to detect anomalies, expedite insights, and reduce cognitive loads on personnel.
Safeguard Data
MDAA employs rigorous data protection with native zero-day endpoint protection and zero-trust features to ensure trustworthiness and resilience.
Augment Sensing
MDAA adds value to existing systems and augments hardware sensing platforms with AI domain awareness for more complete target detection and classification.
Automate Explainability
MDAA supports the investigation of AI model insights to eliminate black boxes and model update/management as battlefield information evolves and automates data veracity functionality.
Enhance Systems
MDAA enhances and augments existing and emergent systems. It is hardware, domain UI, datatype, and deployment agnostic and uses Kubernetes to create an agile, modular software framework.
As threat levels evolve and grow more complex, reliable data collection is imperative, as is the need for rapid domain awareness collaboration and dissemination networks.
The proliferation of sensors and excess data can cause significant strain on operational cognitive loads and bandwidth, causing gaps that adversely affect command timelines and create uncertainty in decision-making across contested multi-domain operations. Without effective data processing mechanisms in place, there is significant risk of misinformation hindering outcomes.
MDAA is a scalable AI-based, hardware-agnostic software suite built on a modular technology stack that integrates multi-INT data for analysts and warfighters. It integrates with and enhances existing systems and user interfaces while operating across disparate data types, including textual data sources in multiple languages, multi-physics sensor networks, and multi-domain manned and unmanned platforms.
MDAA is a scalable AI-based, hardware-agnostic software suite built on a modular technology stack that integrates multi-INT data for analysts and warfighters. It integrates with and enhances existing systems and user interfaces while operating across disparate data types, including textual data sources in multiple languages, multi-physics sensor networks, and multi-domain manned and unmanned platforms.
Using normal behavior modeling, MDAA helps operators achieve decision dominance by shortening OODA loops and/or Find, Fix, Finish, Exploit, Assess, Disseminate (F3EAD) cycles. It can manage or enhance deployed AI models using machine learning based on battlefield conditions and real-time sensor data backhaul and synthesis while integrating with existing systems.
- Distributed anomaly detection.
- Agile software architecture facilitates integration across systems and domains.
- Compounded impact of orchestrated AI-classes processing across varied data-sets.
- Relevant alerts indicated to command and control watch-standers.
- Enhanced domain awareness across echelons in shortened timescales.
Proven For High-value Mission Sets
Avathon’s MDAA is a scalable AI-based, hardware-agnostic software suite built on a modular technology stack that integrates multi-INT data for use by analysts and warfighters.
In USMC Maritime Recon/Counter Recon exercise deployments, Avathon MDAA analyzed 40K+ Automated Identification Systems (AIS) observations, resulting in the identification of 19 vessels that were engaging in AIS spoofing.
In USMC Maritime Recon/Counter Recon exercise deployments, Avathon MDAA analyzed 40K+ Automated Identification Systems (AIS) observations, resulting in the identification of 19 vessels that were engaging in AIS spoofing.
