Rasa

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Rasa
Score 8.4 out of 10
Enterprise companies (1,001+ employees)
Rasa is a conversational AI platform from the company of the same name headquartered in San Francisco, enabling enterprises to build customer experiences. Rasa’s platform was built to create enterprise-grade virtual assistants, allowing personalized conversations with customers - at scale. Rasa’s conversational AI platform allows companies to build better customer experiences by lowering costs through automation, improving customer satisfaction, and providing a scalable way to gather customer…
$0
Pricing
Rasa
Editions & Modules
Developer Edition
$0
Growth
starting at $35k
Enterprise
Contact Sales
Offerings
Pricing Offerings
Rasa
Free Trial
Yes
Free/Freemium Version
Yes
Premium Consulting/Integration Services
Yes
Entry-level Setup FeeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Rasa
Considered Both Products
Rasa
Chose Rasa
The NLU algorithms are more efficient in Rasa. Creating conversations is much easier. In IBM, the more use cases we created, the more complicated it was to up date the entire model. It was quite common to mess up what had already been done.Rasa has greater scope for use with …
Chose Rasa
Glean - proprietary semantic search algorithms, no backend actions integration
IBM Watsonx - complicated dialogue builder, poor separation of no-code and pro-code interfaces
ELMOS (agent based) - all logic in code, no dialogue logic in no-code interface possible
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Rasa
Small Businesses
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Score 8.8 out of 10
Medium-sized Companies
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Score 8.9 out of 10
Enterprises
Conversica
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Score 4.1 out of 10
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User Ratings
Rasa
Likelihood to Recommend
8.4
(4 ratings)
Usability
7.0
(4 ratings)
User Testimonials
Rasa
Likelihood to Recommend
Rasa Technologies Inc
Rasa Pro is well suited for corporate use and for chatbots which require backend connections. Smaller chatbots with a few flows might be better served with a simple dialogue engine and custom AI agents, or Rasa Open Source. Rasa does not come with its own complex vector database, just in-memory FAISS and connectors to external vector DB's such as Milvus and Qdrant. It provides only a basic document parser and embedder for FAISS. If you need to build a RAG focused chatbot around a large knowledge base with complex documents, e.g. lots of MS Word or PDF files, you'll have to build a separate document parser and embedder, as well as your own semantic search engine
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Pros
Rasa Technologies Inc
  • Rasa team has Top notch AI knowledge
  • Greate customer support, by listening towards the clients needs.
  • And building future proof solutions around client Business Requirements within dazzling timeframes
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Cons
Rasa Technologies Inc
  • No-code apps could be improved
  • Online docs can be messy
  • Steep learning curve
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Usability
Rasa Technologies Inc
With the help of dedicated team - documentation and video resources it is relatively easier to build. We prioritized pro-code usage to begin with launch.
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Support Rating
Rasa Technologies Inc
Rasa support has been very responsive, trying to fix any reported issues ASAP. They've also listened to many requests for improvement. The Rasa features and changelog are well documented
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Alternatives Considered
Rasa Technologies Inc
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Return on Investment
Rasa Technologies Inc
  • Cost Savings & Efficiency
  • Increased Conversion Rates
  • Improved Customer Satisfaction
  • Operational ROI
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ScreenShots

Rasa Screenshots

Screenshot of the Studio interface, where a new Flow can be tried out. The user can trace the flow of conversation through the AI Assistant to test and debug new developments.Screenshot of the extensible generative conversational AI framework in a no-code user interface, which enables business users to drag and drop dialogue components for easier AI assistant development.Screenshot of central content management to curate the AI Assistant training data. Users can repurpose and reuse assistant data: search, add, edit, and update assistant data directly in Studio.Screenshot of where analysts, testers, and builders can review user conversations to optimize the AI assistant performance and improve the user experience. Filter and tag key conversations for review, and share within a team for increased collaboration and efficiency.Screenshot of the fully transparent conversational AI enables deep customization and explainability enabling a high-performance architecture.