TP on Rasa
This story is a rough-draft. Check back later for the fully-polished story.
I have used Rasa a little bit. I will add more quotes and thoughts about Rasa to this story as time goes on.
Motivating Quotes and Numbers about Rasa
The Ambitious Goal of Rasa Core
The libraries available to bot developers today rely on hand-crafted rules. With Rasa Core we set ourselves the challenge of building a machine learning-based dialogue framework that’s ready for production, flexible enough to support research and experimentation, and accessible to non-specialists.
https://medium.com/rasa-blog/a-new-approach-to-conversational-software-2e64a5d05f2a
And it’s open sourced!
Rasa Fills a Valuable Niche in CAI
Enterprises of all sizes are looking to move text and voice conversations from agents to conversational AI. However, reliably automating text or voice-based conversations is extremely difficult. Traditionally, developers either use third party cloud APIs that are hard to customize or build their own tools on top of general purpose machine learning frameworks, which usually requires a big research team.
Rasa was founded to provide developers with the machine learning tools necessary to build great AI assistants, regardless of team size, with no additional expertise required, and while still allowing companies to control automation.
Developers can build custom AI environments while owning their own data, deploying on premise or with their cloud provider of choice. The company’s ML-based dialogue allows enterprises to expand assistants beyond simple FAQs to automate full conversations in sales and marketing, internal processes, and advanced customer service which results in clear ROI. For example, Helvetia, a Swiss insurance company, achieved a conversion rate of over 30% selling insurance policies over text.
“Automation is the next battleground for the enterprise, and while this is a very difficult space to win, especially for unstructured information like text and voice, we are confident Rasa has what it takes given their impressive adoption by developers,” said Andrei Brasoveanu, Partner at Accel. “Existing solutions don’t let in-house developer teams control their own automation destiny. Rasa is applying commercial open source software solutions for AI environments similarly to what open source leaders such as Cloudera, Mulesoft, and Hashicorp have done for others.”
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Rasa is used by some of the world’s largest companies across all industries including healthcare, insurance, telecom, and banking. Five of the ten largest U.S. banks use Rasa, as well as companies such as Parallon and TalkSpace, Zurich and Allianz, Telekom, and UBS use Rasa.
“Finding the right photo on Adobe Stock can prove time consuming, with over 100 million images alone. We wanted to give our users an AI assistant that lets them search with natural language commands,” said Brett Butterfield, Director of Software Development of Adobe. “We looked at several online services, and, in the end, Rasa was the clear choice because we were able to host our own servers & protect our user’s data privacy. Being able to automate full conversations and the fact it is open source were key elements for us. It only took a few weeks to build the first version with our team of developers, data scientists and designers. We were impressed by how easy it was to customize the platform to our needs, we can all develop, build & deploy our models directly from our laptops, not something you’ll find with the online services.”
Rasa’s Big Bold Vision for Autonomous Agent-base (Insurance) Companies
Level 5: Autonomous Organization of Assistants
Eventually, there will be a group of AI assistants that know every customer personally and eventually run large parts of company operations — from lead generation over marketing, sales, HR, or finance. This is a vision we see as reality, even if it is as far as a decade away.
Example: To complete the renters insurance scenario at Level 5, enter the world of an autonomous insurance company. In all examples before, most probably humans were still involved in the backend process with constant checks and balances. Now the end to end is done by AI — anything from underwriting to claims to discounts. AI also judges if you might be able to take advantage of a different policy based on data points from your life, freeing you from a decision.
https://blog.rasa.com/conversational-ai-your-guide-to-five-levels-of-ai-assistants-in-enterprise/
https://blog.rasa.com/level-3-contextual-assistants-beyond-answering-simple-questions/
https://www.lemonade.com/blog/rise-autonomous-organization/
I personally think it will take longer than a decade to reach this point for any business with much data-sensitivity or financial power like insurance.
