Rules-based Chatbots vs. AI Chatbots

Traditionally chatbot solutions rely on rule-based and decision tree solutions. This approach is fragile and frustrating for customers because all answers customers ask need to be anticipated. This renders rule-based chatbots more expensive to develop and maintain.

In contrast, data-driven machine learning conversational AI modes deliver more powerful chatbot solutions. Intelligence is obtained by integrating these models with business applications like CRM, Logistics etc. Once up and running these model use real AI to learn from experience.

Unlike decision-tree bots, conversational AI models use a range of other consumer data to facilitate self-learning. For example at Rayon AI we use data available on an organization's website and other social channels so that models can be deployed in a matter of days, which will be able to answer a wide range of customer queries immediately.

And Rayon AI uses the apparatus an organization’s CSRs use when dealing with customers. So an organization can deploy powerful Rayon AI Synthetic Service Agents in the same way as they allocate human resources but for a fraction of the cost.

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