Behind each profile is DDIQ’s decision engine that executes searches, analyzes the findings and formats the output. The DDIQ decision engine interprets and processes natural language while leveraging the power of machine learning, employing the same cognitive processes that a due diligence researcher would perform without the constraints of human-based research.
DDIQ leverages continuously improving machine learning models to assess the likelihood that a piece of content relates to the input subject, elevating the world of false positive removal to a new level.
Premium And Deep Web Sources
DDIQ’s dynamic framework allows it to execute any number of queries for each profile against structured and unstructured sources including the web, deep web and premium content.
Trained by leading subject matter experts, DDIQ interprets initial search results and automatically builds subsequent queries to ensure that all relevant inputs are fed into the decision engine.
Based on the input data provided, DDIQ’s adaptable discovery plan traverses the appropriate sources to recall the most appropriate set of results.
Cutting Edge NLP
Natural language processing breaks down search results by extracting names and associated attributes, allowing DDIQ to identify the relationship between the input subject and the subjects identified in the text.
DDIQ’s risk classification models parse through content and determine the probability that the result matches the relevant scenario the tool is trained to recognize.
Complex algorithms not only match a set of keywords but contextually identify the relationship between the subject and the keywords.
DDIQ API Overview
The DDIQ API is a set of REST web services that provide access to the same data available through the DDIQ web user interface. Click here to learn more.
The best way to learn about DDIQ is to see it for yourself!
Contact us to set up a live demo tailored to your needs.