I'm one of the data scientist at Talla working on NLP and ML problems. One of the challenges of selling an "AI product" is that users assume the AI is monolithic. With Talla, the user interacts with the product through either through the chat bot (via slack or ms teams) or our web application to create/manage knowledge content, import content, and manage users. So they (fairly) assume that the AI is the bot itself, which is their primary point of engagement.
In reality, many of the "AI" technologies we have developed are behind the scenes and rather invisible. We have deployed various machine learning models and nlp techniques across the product to make it more intelligent. We are working on models to extract context and entities from user questions, support natural language querying of tabular data, extract "useful" user questions from slack channels, and automatically generate diagnostic workflows from user conversations. So ML/AI is more than the bot itself.
In reality, many of the "AI" technologies we have developed are behind the scenes and rather invisible. We have deployed various machine learning models and nlp techniques across the product to make it more intelligent. We are working on models to extract context and entities from user questions, support natural language querying of tabular data, extract "useful" user questions from slack channels, and automatically generate diagnostic workflows from user conversations. So ML/AI is more than the bot itself.