Proximic's core technology uses new language independent methods to reach breakthrough precision in contextual matching and targeting
Proximic goes beyond searching for single keywords. Unlike other search and information retrieval technologies, Proximic is independent of any intimate knowledge of language. It applies two fundamental steps to analyze unstructured text:
At first Proximic applies pattern recognition of symbols such as characters and character sets. Since it does not make use of words as the common denominator it therefore does not rely on any word stemming, stop words or dictionaries.
Then Proximic utilizes adaptive noise filtering algorithms to account for the variances of written texts. In essence, Proximic determines the "normality" of a language by establishing a generic baseline pattern for any given language (e.g. English, German, Chinese). When analyzing text, Proximic therefore readily can identify very subtle "specifics" to determine the context.
Proximic basically understands composition of text, not text itself. Take an example to imagine how Proximic works: how would you intuitively describe a person's face? The fact that it has eyes, nose, and mouth are normalities. But the particular size, shape, color, and location (specifics) provide the description, or context.
We've now built this core technology into a multidimensional proximity content matching platform readily available to work for you and your needs - open and in real-time.
