We have developed a way of allowing automatic analysis of any language structure using graphs. The solution automatically detects all direct and indirect dependencies between words and phrases, identifying recognizable and hidden sources of meanings at the data recording stage of the system.
This makes the process of learning and understanding the various meanings of NLP very fast and does not require a lot of computing resources. Menerva records the text data in its original form, retaining all potential links so you do not lose the key relationships that allow you to efficiently and effectively read all potential meanings, as in the case of neural networks.
They allow you to improve the efficiency of associative (data association) processing to a new level of performance.
Data structure based on graph engrams simplifies the statistical and symbolic analysis of data.
They allow detection of hard to find dependencies in unstructured data.
They save all potential data dependencies in their repositories, so the system automatically chooses the most suitable model without having to design another.
When customers expect answers to queries in less and less time, it is worth thinking about artificial intelligence which will take care of many of those repeated questions. Bots on livechat or social media, reading e-mails and suggests answers, FAQ search engines, smart search, are just a few examples to help you increase customer satisfaction by giving them the option of self-service. It’s worth it!
Tell us about your business’ needs, and we will find a solution