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How to Build Your Own Chatbot With Dialogflow

High-quality conversational interfaces (chatbots and voice assistants) have traditionally been difficult and expensive to build. An effective chatbot requires Natural Language Processing/Understanding (NLP, NLU) and other Deep Learning techniques to understand the underlying intent of human language.

These techniques require skills that are difficult for individuals to acquire and expensive for organizations to hire. Even if you have the skillset at hand, the amount of conversation data required to build natural interactions is labor intensive and expensive to collect. Given these facts, building a chat interface for your application or product likely does not offer enough value for the cost. Until now.

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How Neural Networks Recognize Speech-to-Text

Speech to text

Gartner experts say that by 2020, businesses will automate conversations with their customers. According to statistics, companies lost up to 30% of incoming calls because call center employees either missed calls or didn’t have enough competence to communicate effectively.

To quickly and efficiently process incoming requests, modern businesses use chatbots. Conversational AI assistants are replacing standard chatbots and IVR. They are especially in demand among B2C companies. They use websites and mobile apps to stay competitive. Convolutional neural networks are trained to recognize human speech and automate call processing. They help to keep in touch with customers 24/7 and simplify the typical request processing.