Leave the talking to machines

Understanding conversations using machine learning is one of the most difficult yet exciting areas in AI. Not surprisingly, in 2020 alone companies spent about $42 billion on conversational AI technology and will continue to do so with an compound annual growth rate of 37% (IDC, 2021).
The applications for organizations of this technology are endless, but most interest is found in the customer service domain. By automating your customer service department you are able to serve your customers better and faster.

The key challenge

Making machines able to accurately understand intertwined contexts of messages, reasoning and being able to reproduce conversations.

Reasoning with NLP & NLI

On a high level, Natural Language Processing (NLP) covers everything related to language understanding. It covers topics from speech recognition to language generation. Common NLP algorithms are often manifested in applications like chatbots, translation services, and sentiment detectors etc.
When it comes to understanding conversations, Natural Language Inference (NLI) is key. It is all about understanding the deeper contexts and being able to learn from a story.
PremiseQuestionModel Prediction
Leo is outside, Daniel walks to himIs Daniel outside?True
A mom and her daughter are smilingTwo ladies are laughing because the cat is playingUnclear
A man runs on the soccer fieldIs he sitting on the couch?False
NLU model in action

Conversation summarizing

Using AI summarizing techniques you are able to process large amounts information in a shorter amount of time. Especially when time is a constraint, like in customer service, where you want to quickly look back what has been agreed with the customer in previous conversations.
Current summary models can be both extractive and abstractive, meaning that they can pick only the most important sentence, or write an entire new summary.

Want to be part of something big?