What are Chatbots?

Chatbots are conversational interfaces for individual conversations with computer systems using messages.

Chatbots allow users to write or to say messages (sometimes only questions) in “normal” (human-understandable) form and receive same-formed answers. 

The core of each chatbot is Natural Language Processing techniques, which allows not only ”understand” the written/spoken message, but mimic human conversation.

The variety of chatbots is huge: from simple ones, like FAQ answer assistants, to complicated ones such as Siri or Alexa.

The benefits of chatbots are 24/7 service, improvement of customer satisfaction, increment of revenue and reduction of costs.

Our Chatbots Projects

FAQ ChatBot (NLU)

FAQ ChatBot is a conversational interface for individual conversations with computer systems using messages.

Chatbot “asks” predefined questions and analyzes the answers from the user using RNN techniques (LSTM in particular).

The responses from FAQ CB could be different: message, link to the article, internal object, external source, etc.

It also “learns” from the user’s answers and asks if the answer was relevant or not. 

The usage of FAQ CB is wide: in any field, which requires a conversation with agents e.g.: sells, services, customer support, etc.

Although all the benefits of FAQ chatbot, complete human intelligence is unreachable so far.


A Context Based Artificial Intelligence ChatBot is a conversational interface for individual conversations with computer systems using voice commands, very rarely messages. A few prime examples of those chatbots are Apple Siri, Amazon Alexa, Google Assistant, etc.

CBAI CB receives from the user a vocal command/commands analyzes it and responds respectively.

The algorithm of CBAI CB is complicated and consists of a few steps. The first one is to translate a vocal signal (a command) to text using an Automatic Text Recognition (ASR) technique. An algorithm also eliminates noises (unrecognized sounds), and long pauses and divides given vocal input to separated parts according to recognized words. In case that word is unknown (can not be found in the bag-of-word: algorithm’s word dictionary), it will be added and remembered. Thus, the algorithm teaches itself. After that, during the next step, the algorithm classifies converted to text in the previous step command, using Machine Learning classification techniques (such as Hidden Markov Model, or SVM with/without Naїve Bayes features).

Lastly, a Text-To-Speech (TTS) unit delivers an algorithm’s response to a user or reports about completion of the current command in a normal (human-understandable) way.

CBAI CB is not only used in IoT systems, but also as a personal assistant in everyday life.

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