Userbot

@ryanchaliff In Userbot, research and development of new algorithms are focused both on finding new, fast and reliable solutions for our customers, and optimizing the overall AI pipeline. The AI pipeline is composed of several neural components that can be customized by our users to fit their language domain, such as the intent classifier and the entity extractor, and each of these components is continuously under research. To better improve the user experience, we try to provide machine learning algorithms that are able to generalize and fit the user conversational domain with just a few textual inputs from the customer. So the overall intent classification architecture is based on two neural networks: the first one is a language model that outputs the similarity between an input question (from online chat) and a training question; the second one is a neural network that is trained to map several variations of the same question to an intent.

@ryanchaliff thanks for your question, I think that @ricardo_piana or @cappiello_an may give you an exhaustive answer 🙂

@fabiobiccari thanks Fabio! 🙂

@andrea_manfre thanks Andrea! 😉

@ricardo_piana teamwork makes the dream work! 😇

@giovanni_giberti hey Giovanni, thanks! 😉

@simonegizzi85 glad to have you on board! 😎

@carlo_rossotto thanks Carlo, we’re doing our best! 🙂