Rasa: Empowering Global Enterprises with Conversational AI

September 28, 2018 0 comments

Built by machine learning experts, Rasa has developed an open source Artificial Intelligence (AI) assistant framework and platform that supports full and nuanced conversation to effectively engage with the customers. This framework uses natural language processing, machine learning, and deep learning for handling truly advanced contexts and complex multi-turn conversations.

The company empowers makers to build AI assistants that everyone can use. Rasa’s founders started out building their own AI assistant for Slack. It quickly became obvious that available tools lacked capabilities to customize the solution and generally were insufficient for common needs. Talking to other developers, the founders quickly realized a large group of people faced the same problem. As a result, Rasa was born: a framework with the goal to enable everybody to build AI assistants. The company works closely with its research team and the open source community for contributions to the industry.


The Trailblazers

The company is headed by Alexander Weidauer, Co-founder & CEO; and Dr. Alan Nichol, Co-founder & CTO. Alan and Alex perfectly live Rasa’s mission to enable makers to build AI assistants by making bold decisions every day. This includes open sourcing Rasa Stack tools as well as approaching dialogue management from a machine and deep learning perspective. Because Rasa Stack is open source, it fosters innovation in the industry of conversational AI.

From the very beginning, Rasa focused on larger product teams building advanced bots. As a result, the company is now working closely with a lot of Fortune 500 companies, many already using Rasa in production. Working with these influential companies using its technology, and partnering with Rasa, serves as strong feedback that the company is developing a great product. Further, both the founders have been honored with this year’s Forbes 30 Under 30 list.


Delivering Solutions to Handle Complex Conversations

While many companies claim that the whole topic of AI assistants is solved and working perfectly, Rasa believes that there is a long way to go. It is still very common to use drag and drop solutions to mimic conversations. Human conversations are very complex, hitting the limits of rule-based systems quite easily. Rasa’s dialogue manager Rasa Core, however, enables a more realistic approach to human conversations by learning from real conversation data.

Further, Rasa pursues an open source approach enabling all makers to build AI assistants. The company believes this to be really important not only from a customization perspective but also because most cloud solutions act as black boxes making it hard for companies to understand why something works and why not.


Growth through Strategic Partnerships

Rasa is collaborating closely with many research institutes around the world including University of Cambridge, Heriot-Watt, and Technical University Munich. “Because we are completely open source many researchers use Rasa for their projects. This enables us to follow relevant research closely and implement the latest developments in NLP and AI. For this process, we work closely with our customers to identify real business problems. Furthermore, our world-class team rigorously examine new additions to ensure Rasa only implements cutting-edge research-based updates that solve key problems,” said the founders.


The Future

Innovation moves much faster now than ever before. Technologies like Big Data or AI play a major role in this development. Once it took years to develop new innovations and thoroughly test new ideas. With the help of large data sets and automated processes, this time is dramatically decreased. As a consequence, companies can shorten their adaption cycles as well. It will be interesting to see which companies succeed at this and which won’t.

Conversational AI is still in its early stages. Only a small fraction of companies are already working with it. This will change over the next couple of months and years. One aspect that contributes to that development is new tools and tech getting introduced ultimately improving the general product environment of conversational AI.

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