How it Works
BRAINDOCS™ A.I. RECOGNIZE THE RELEVANCE IN TEXT BASED LANGUAGE INFORMATION
BrainDocs™ A.I. intelligent agents enable you to codify and automate your domain expertise so you can use your expertise on the important issues.
Problem: The amount of information available is increasing incredibly fast, our ability to process all of it accurately cannot keep pace. It is often nearly impossible to quickly find the most relevant information in the sheer amount of irrelevant data noise there is. The time wasted by searching for the correct knowledge can become very expensive for employers.
State of the art BrainDocs™ A.I. intelligent agents are based on ai-one™ Inc. Technology. ai-one’s core technology for language applications. BrainDocs™ is a daughter company of ai-one Inc. and specialized to sell the BrainDocs™ A.I. solution.
ai-one’s technology is a new form of biologically inspired computing that processes information in the same way as the brain. Unlike other approaches, this API enables machines to learn with or without human supervision. The technology automatically generates a lightweight ontology that detects all relationships among data elements. Learning occurs at the time data is ingested therefore, it is very fast compared to other approaches.
BrainDocs™ A.I. leverages this core technology using both static and dynamic fingerprint techniques to deliver a set of tools for the analyst working with free text or unstructured data to classify, organize, filter, search and explore in ways not possible with keyword search, natural language processing (NLP), Latent Semantic Indexing (LSI) or other statistical/mathematical tools. Furthermore, the extraction of concepts expressed in documents into a fingerprint graph enables experts with programs such as SPSS, R and Tableau to include unstructured data in their analytics and visualizations.
The intelligent agents that are deployed by BrainDocs™ A.I. are trained by the user and the list of relevant results are returned in their ranked order to review and mark as relevant or not relevant. This process saves hours of review time, which is a tremendous value proposition to analysts confronted with ever increasing quantities of information.