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Conversations with patients are rarely just one question and one answer.

Build contextual AI assistants that can handle back-and-forth, and host them securely on your HIPAA-compliant infrastructure.

AI assistants can support your members throughout the whole lifecycle. Examples of conversational use cases:

  • Choose the right health plan
  • Change contact details like address
  • Check the status of claims
  • Answer questions about billing

HIPAA-compliant with full control over data and IP

Keep your customer data secure with HIPAA-compliance, maximum data privacy and security measures.

Advanced NLU and dialogue management

Customize and train language models specifically for healthcare and medical terms. Manage back-and-forth dialogues with machine learning.

Integrate & securely deploy anywhere

Integrate Rasa into existing backend systems and data sources. Deploy Rasa on-premises, or on your private cloud.

Case Study

Automating the onboarding of mental health patients with a privacy-first, HIPAA-BAA compliant AI assistant

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. Meeting these requirements could have required Tia to build their own natural language understanding AI. Developing from scratch takes months and requires specific AI and machine learning expertise.

The Solution

Tia based their app’s natural language understanding on Rasa. Tia’s product team consists of engineers, data scientists, product managers, and designers, some of whom don’t have Python coding experience. Rasa Enterprise's intuitive user interface enables product managers to label training data, providing faster iteration cycles and ease-of-use.

The Results

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, Tia is able to continuously add new training data and learn from real user interactions, which continually improves accuracy.