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      • What are voicebots?
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  • Quick guide to creating voicebots.
    • How do voicebots understand people and respond correctly?
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      • What to choose from the Blocks Menu?
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    • Find out if you want to use InteliWISE voicebot templates.
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      • What are variables?
      • How to set up the needed data?
      • How to confirm the name of the customer?
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  • What can you do with InteliWISE blocks?
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    • SAY – How to make your voicebot speak?
    • CUSTOM JS – Where to insert a custom code?
    • IF CONDITION – How to influence a voicebot behavior depending on conditions?
    • CONFIRM – How to get confirmation from a customer?
    • CONFIRM DATE – How to make a voicebot confirm the agreed date with a customer?
    • DATE – how to get a date from a customer?
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    • How to configure the telco infrastructure?
    • We recommend the InteliWISE Shared Telco Infrastructure.
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      • What are hardware requirements for the InteliWISE voicebot?
      • What hardware do is needed for automated speech recognition (ASR) system?
      • What hardware do you need for text-2-speech (TTS) system?
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    • Can voicebot be integrated with other systems?
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    • Welcome to the InteliWISE Chatbot solution!
    • Introduction | Tools and Timetable
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          • How to add questions and responses, build intent?
          • Handling exceptions (incl. ‘query miss’) and hand-overs
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  1. Chatbot
  2. AI Engineer - building and training a knowledge base
  3. STEP 3: Training AI - Chatbot understanding capabilities based on NLU / NLP
  4. CONVERSATION (FLOW)

Handling exceptions (incl. ‘query miss’) and hand-overs

PreviousHow to add questions and responses, build intent?NextTesting, Launching and post - launch optimization

Last updated 3 years ago

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The NLP system is not capable of providing automated answers to some users’ utterances. The system has different modes of how to react to requests that are not understood (i.e. ‘don’t knows’). This action is triggered when the predicted score for the answer falls below a certain threshold.

The main fall-back (escalation) options are:

Escalating to a pre-programmed response, optionally with a clarifying scenario

This type of reaction is defined in the same way as a simple response (in the Template window). The suggested topics are questions from the knowledge base entered in the desired order in the Suggested questions window.

From the user's side, it’s visible as a linear dialogue (scenario):

Escalation to a chat with Live Agent

Please see STEP : Integrations for this option

Process of supporting multiple languages

Two approaches as recommended:

  • Providing full NLP with language model, incl. application of full semantic analysis of queries, dictionaries and databases of ready-made responses in a given language This is now available for selected languages, incl English, Polish, Spanish;

  • Machine translation service

Use of a combination of automatic translators and dedicated semantics:

  • Each user input is translated into knowledge base language, e.g. from Spanish into English,

  • then user input is processed internally as usual, but all system outputs are back translated to the user language, e.g. from English into Spanish. Such solution operates only 10-15% less reliable than originally translated knowledge base. There is no time waste for translation services, no time spent on setting up the services - all works on the same knowledge base serviced in one language and serves support in multiple languages (as many as needed).