Proactive. Predictive. Preventative.
Protecting People. Saving Lives.
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Behavioral Affinity Models. Data Noise Solved.
Data noise is a major obstacle for most commercial off the shelf open source social listening tools. Our proprietary and configurable BAM data filters solve this problem. Each BAM is a collection of selectors – terms, phrases, and expressions – that are representative of a specific area of risk or interest. Keywords are run through OS-INT and filtered by BAMs. BAMs are created through subject matter expertise and leveraging supervised machine learning.
Our Deep Web Listening is Like No Other.
Conventional technologies rely on a single platform or social media API. Not Radiance. It uses continuous deep-web extraction to ingest all open source data. The volumes of publicly available electronic information are cleaned and prioritized, yielding relevant insights into high-risk individuals, entities, events, or sources by aggregating all the data scattered across the Internet and measuring it against configurable BAMs.
Proprietary Risk Databases. 11 ecosystems.
Beyond the BAM search, Radiance cross-references user-entered names with its proprietary, dynamic risk databases. It executes more than 1 million queries across 11 ecosystems, providing additional insight into risk-related behaviors.
Name Extraction. Relationship Mapping.
OS-INT’s proprietary name extraction algorithms identify first, second, and third order relationships of apparently disconnected individuals, entities, and events. It measures network centrality to highlight importance and priority of relationships.