Dialog systems, like chatbots, are all about context. A lot of chatbot interfaces fall short on understanding human conversational context, thus failing miserably when trying to interface with your leads or customers. The Rasa Core framework seeks to alleviate this shortcoming by using an interactive learning approach, where you can train & update your models by conversing with your bots prior to releasing them.
As Core states in the product page, Rasa Core is focused on helping developers by guiding conversations “taking the history & external context of a conversation into account. Instead of 1000s of rules, Rasa picks up patterns from real conversations.”
Alan Nichol, co-founder of Berlin, Germany-based Rasa, states that perhaps the greatest challenge faced by bot developers is making a useful AI-based bot with an initial lack of training data. Rasa’s interactive learning approach lets you train your system by talking to it & providing feedback. Once you’ve trained the dialog system with enough real conversations (a dozen or so, according to Rasa) you’ve already scaled it to the point where it’s ready for release.
Core’s advanced machine learning technology is derived from Rasa’s long time collaboration with leading research in Neuro-linguistic programming (NLP) & Dialog systems, that’s gotten them a leg up on bringing important research findings into production. The platform gets models bootstrapped really quickly & dialog systems, especially for non AI savvy development shops, are practicably useful from day 1.
The Rasa Core framework is Python-based & facilitates the building of chatbots on Messenger, Slack bots, Alexa Skills, Google Home Actions & more.
Since Core is built on open source software & sports an Apache license, you don’t risk vendor lock-in & you can use it free of charge on commercial projects. You can download Core for free from its GitHub page here.
Rasa also offers paid subscriptions with 24/7 support, & offers the NLU intent classification & entity extraction system. This is a sort of API for building your own language parser with existing NLP & ML libraries.
– This is a startup profile based on publicly available material & not a review –
Image Credit: Rasa
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