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Rule-based dialogue systems are hard to maintain and can easily break. Rasa uses machine learning instead so you can learn from real user conversations.

Learn more about it here in our blog post →

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Case Study

Incorporate HIPAA-compliant natural language understanding for better women’s health

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The Challenge

Tia is a San Francisco-based women’s health startup. Their natural language app allows users to ask health questions and has been downloaded thousands of times.

In the healthcare space HIPAA compliance is crucial, meaning Tia would have had to build their own natural language understanding AI. Developing this from scratch takes months and requires specific AI and machine learning expertise.

The Solution

Tia built their conversational natural language understanding on the Rasa Stack. Tia’s product team consists of engineers, data scientists, product managers and designers, some of whom don’t have Python coding experience. For instance, Rasa Platform’s intuitive user interface enables product managers to label training data, providing faster iteration cycles and ease-of-use.

Next Step

Tia’s system was established in just a few days and without a research team. Using Rasa, the company could focus primarily on designing a compelling user experience. Now, the company is able to continuously add new training data and learn from real user interactions, which continually improves accuracy.