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What you can build with Rasa

High-trust, fluent AI assistants​

Rasa assistants are great at handling tasks that require back-and-forth with a user. When real users interact with a chatbot or voice assistant, they rarely provide all the information you need in one go. Assistants have to ask clarifying questions, gather more information, query APIs, and follow dynamic branching logic to complete a task.

You can trust a Rasa assistant to handle that back and forth. Out of the box, Rasa already knows a lot about how conversations work: it automatically handles disambiguation, topic changes, clarification, corrections, implicatures, negation, interjections, and more. And while LLMs help power that out-of-the-box fluency, Rasa executes the steps in your task determinstically. So no matter how complex and high-stakes your business logic might be, Rasa will follow it faithfully.

Here are some of the conversation patterns that Rasa handles automatically:

Bot:

Would you like to add anything else to your order?

User:

I've spent too much already!

💡 Set continue_shopping to False and continue with the checkout process

Bot:

Can I interest you in next-day shipping for $4.99?

Real-time voice assistants that take action​

Rasa handles the core elements of spoken conversations. These include natural conversation elements like pauses and turn-taking, as well as phone-specific capabilities such as DTMF processing and call control. The platform integrates with speech recognition services and contact center systems for automating call center operations.

Rasa's dialogue understanding approach allows small LLMs (under 10B parameters) to match the accuracy of much larger models. These models can be self-hosted and respond quickly enough for real-time voice conversations. Here's a demonstration using a fine-tuned Llama 8B model:

Multilingual, personalized support assistants that handle hundreds of tasks​

Each customer interaction is different, shaped by language, location, and context. With Rasa you can build personalized experiences using real-time contextual data about the user.

user profile loaded: language preference, subscription tier, recent activity

User:

you messed up my order

Bot:

Hi, Anisha! I'm sorry to hear that. I can see two recent orders - was the problem with the umbrella or with the kitchenware?

Rasa is especially helpful as you scale across domains and departments to cover the full breadth of support requests, with:

  • A centralized content hub for translation & localization
  • Reusable dialog components across languages
  • Collaborative workflows to review and update assistant versions.
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Custom, controllable conversational AI​

Rasa lets you build assistants where you need:

  • Complete data control and privacy, with all processing happening in your infrastructure
  • Full visibility into decision-making, with structured, traceable paths from input to output
  • The ability to override & cutomize the AI engine of your assistant
  • Deep integration with your existing systems
  • A CI/CD driven approach to deploying your assistant

With Rasa you can:

  • Self-host fine-tuned language models optimized for your domain
  • Add custom components to modify any part of the conversation pipeline
  • Build native integrations with your mobile apps and enterprise systems

Ready to start?​

Check out the Platform at a Glance

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