Cyfuture AI vs. Rasa

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Cyfuture AI
Score 0.0 out of 10
N/A
Cyfuture AI provides an enterprise platform for building and deploying applications using secure cloud infrastructure and GPU computing. The system supports workloads including Generative AI, voice agents, Large Language Model (LLM) deployment, Computer Vision, and Data Analytics. The vendor offers GPU as a Service (GPUaaS) utilizing NVIDIA GPU infrastructure for model training, inference, and fine-tuning. Deployment options include Public Cloud, Private Cloud, and…N/A
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
Cyfuture AIRasa
Editions & Modules
No answers on this topic
Developer Edition
$0
Growth
starting at $35k
Enterprise
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Offerings
Pricing Offerings
Cyfuture AIRasa
Free Trial
NoYes
Free/Freemium Version
NoYes
Premium Consulting/Integration Services
NoYes
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
User Ratings
Cyfuture AIRasa
Likelihood to Recommend
-
(0 ratings)
8.4
(4 ratings)
Usability
-
(0 ratings)
7.0
(4 ratings)
User Testimonials
Cyfuture AIRasa
Likelihood to Recommend
Cyfuture AI
No answers on this topic
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
Cyfuture AI
No answers on this topic
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
Cyfuture AI
No answers on this topic
Rasa Technologies Inc
  • No-code apps could be improved
  • Online docs can be messy
  • Steep learning curve
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Usability
Cyfuture AI
No answers on this topic
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
Cyfuture AI
No answers on this topic
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
Cyfuture AI
No answers on this topic
Rasa Technologies Inc
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Return on Investment
Cyfuture AI
No answers on this topic
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.