BrainDocs – powering intelligent enterprise
Simply put, BrainDocs will make your enterprise smarter and faster.
Problem: You are looking for something, a brilliant idea that needs supporting research before you can present it, but it could be worded in a variety of ways and is buried among thousands of pages on your server. Although you may know a few keywords, or tags, to help search for documents; you still end up reading through hundreds of irrelevant documents trying locate that needle in the haystack. You need your own AI, your own agent to do this for you.
Solution: Our intelligent agents enable you to codify and automate your domain expertise so you can use your expertise on the important stuff. State of the art, all of the agents in BrainDocs are based on the NathanICE API, ai-one’s core technology for language applications. Nathan is a new form of biologically inspired neural computing that processes information in the same way as the brain. Unlike other approaches, our API enables machines to learn with or without human supervision. Our technology automatically generates a lightweight ontology that detects all relationships among data elements. Learning occurs at the time data is ingested — so it is very fast compared to other approaches.
At the core is our new intelligent classification engine, BrainDocsICE.
BrainDocsICE – Intelligent Classification Engine
BrainDocsICE 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 are trained by you and the list of relevant results are returned in your ranked order to review and mark as relevant or not relevant. This process saves hours of review time – a tremendous value proposition to analysts confronted with ever increasing quantities of information.