Rasa automates conversational customer experience at scale.
Top Enterprises Trust Rasa
181%
ROI. Less than a year before you see returns on your investment, according to an independent study.
60%
Containment Rates. Attentive interactions across all touchpoints - allowing employees to focus on higher value tasks while automating the rest.
10M
Downloads. Infrastructure foundation used by hundreds of organizations, and core developer workflows used by millions
Analyst Report
The Gartner Cool Vendor report identifies innovative products and rising trends in the conversational AI industry. Learn why Rasa was selected and get recommendations for evaluating your conversational AI stack.
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Customer Story
T-Mobile decreased wait times and time to resolution, with a customer-centric approach to self-service support.
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Whitepaper
Discover how to take a proof of concept to a production-ready, scalable virtual assistant. We share strategies for overcoming some of the most common challenges faced when taking enterprise assistants to market.r overcoming some of the most common challenges faced when taking enterprise assistants to market.
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If you're out to build serious conversational applications—not just dabble—Rasa is the platform you do it with. The upfront investment in the right platform will yield benefits in shorter time-to-market and lower overall total cost of ownership.
Our team loves working on the Rasa platform. The flexibility has produced many creative ideas, and we already have teams outside of the Care department that want to use our virtual assistant
We built our assistant using Rasa - which was the only solution and fit for us at Lemonade. Using Rasa’s machine learning framework, we’re able to hire smart humans who create real impact while automating everything else.
The Rasa Community is a diverse group of developers, data scientists, designers, and conversational AI enthusiasts.
A few weeks ago we released Rasa Open Source 3.0, a new major version. This major update contains new features, quality of life improvements as well as big architecture updates. To make it easier for developers to get started with we’ve created new learning material and updated a big chunk of our existing content.
Angus and his team developed textual VQA components to accurately understand what a user is asking, extract relevant text from images in an intelligent manner, and to provide natural language answers that build upon the context of previous questions. As their system is created to be assistive, they designed it with a particular focus on privacy, transparency, and controllability.
In this post, we’ll review a method to automate testing of your Rasa custom actions and forms using Postman.
