A digital radar

How it works

Similar to how radar reveals aircraft, Semantic Radar reveals hidden data which allows an organization to uncover strategic and decisively valuable and actionable from not only Open-source intelligence (OSINT) but also proprietary and/or clandestine sources; offering high fidelity decision-making, prediction, and exposing unknowns that could be mission-critical.

World-leading solution

Semantic Radar™ is a powerful innovative way to make sense of and steer through the fog and confusion of OSINT. Semantic Radar™ beat world-class contenders like Google, Facebook Research, Baidu, Tsinghua University, MIT/Lincoln Labs in different domain leaderboard competitions without any background domain-specific coding.

Semantic Radar™ in short,

A summary of our technology

Leveraging matrix graph theory for semantic signal processing.

Matrix Graphs

Classical data is ingested and converted into a graphical format that is operated upon using linear algebra, operator theory and matrix mechanisms.

Spectral Signal Processors

Signals are generated, processed, and yield unique tracks that illuminate hidden information.

Artificial Intelligence

Machine learning and artificial intelligence methods are adapted for modeling signal Tracks.

Weak Signals Are Revealed and Analyzed

Tracks provide hindsight, insight, and foresight for prediction.

Deep Dive

A more in-depth overview of the technology

Large databases such as chemical hazards (TOX21), molecular virology (mol-HIV), REDDIT online forum Q&A chatter, IMDB/Netflix movie preference data sets, etc. contain terabytes of complex data. Most researched data in these databases have large signatures and are fairly easy to obtain as they stand out in this complexity.
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But there exists in these large databases hidden data with small, even extremely small signatures. Researching small signature data proves to be much harder as it is hidden from view by the noise and complexity of the domain. It is essentially unsearchable and therefore cannot be used. Semantic Radar™ offers a means to reveal small signature data hiding in these large databases.

Currently, ML needs vast amounts of expensive, very high-quality data to build a workable system such as those used by Twitter, the NIH data archives, the ACS chemical data, and the GDELT tools that provide diplomatic geopolitical data. These OSINT databases are comprised of many different data types generated by wide-ranging sets of teleprocessing tools that are the core of ML systems.
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In such systems, the time from data to decision, including the time to train an AI system using ML is long, arduous, domain-dependent, and most of the time, relies heavily on human Subject Matter Expertise (SME) which is hard to find and extremely costly. Adversaries use this to their advantage to create strategic gains and use of persistent vulnerabilities or threats.
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Semantic Radar™ is a new kind of tool that leverages Machine Learning and Artificial Intelligence faster, cheaper, and better than our competitors. Our approach transforms graphs into geometric tracts that signify and amplify the semantics hidden inside a domain. It is a ONE-SHOT, advanced Mathematicsapplication.

Machine learning (ML) is key to asymmetric advantages and agility in researching OSINT: being able to outthink, outwit, outlearn, outpace and outmaneuver any competitor in any domain with predictive artificial intelligence (AI). AI relies on ML to derive answers to complex questions.
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Cyber, physical, chemical, biological, environmental, and medical domains form the largest categories of societal risks. These domains are rich with vulnerabilities. Semantic Radar™ reveals key indicators from not only one network but exposes the relationships between many such complex networks. Identification of semantically related technical concepts or actual technologies that indicate threats in arrays of dislocated data is about uncovering non-obvious relationships between siloed data so that Machine Learning (ML) and Artificial Intelligence (AI) can connect the dots and predict strategic surprises.