All these tools/APIs offer a great way to build your chatbots rapidly by allowing us to define the intents/entities and flow of intents graphically.
The initial step is to collect the requirements on how customer interaction happens today. They might have a script, coded procedures, or other resources.
Behavioral targeting is necessary so that the customers can interact in a personal way. Hence, defining a persona for your chatbot agent is a vital step while creating chatbot.
The diagram describes the interactions among the different elements in the model. It defines the dynamic behavior of the system. Hence, the next step is to build a System Interaction Diagram.
The next step is to extract the potential list of intents and compile possible sentences that lead to those intents.
Create an instance of your Chatbot using your favorite API (dialogflow/wit/watson/botframework).
Define the intent of your Chatbot by using the conversation builder tool. Also, set the entities.
Use their testing agent interface to check the interaction with the bot.
Develop the web app or microservice that is responsible to interact with conversation and implement some of the business logic to handle the conversation context.
Integrate external APIs to deliver the required data to the end user. You can use their one-click integration service to integrate with multiple channels such as Facebook, slack, webapp etc. You can even integrate it with the mobile apps using their SDK.
Add other components to complement the business requirements such as Retrieve and Rank, build recommendation engine etc.
The best way is to extend prebuilt intelligence. To extend the intelligence of your chatbot you can add an intermediate layer between your chatbot engine and customer query. You can either add any NLP(Natural Language Processing) APIs given below or prepare your own tensorflow based model and pass the query to this model. This intermediate layer will further extend the capability of our chatbot by analyzing the customer query and take appropriate action based on insight delivered by the NLP API.
What’s the best feature of our model? The very clear answer is “Flexibility.” To have the greater flexibility in overall architecture we will add our custom chatbot engine in between customer interaction channels and Third Party chatbot frameworks. Our chatbot framework has the capability of routing the end user to a live chat support person depending upon the satisfaction score.
Our chatbots can offer interactive communication where they ask questions to understand the real problem rather than just saying anything. Bots can be programmed to give automated answers to repetitive questions immediately according to your stored database. Also, they forward the request to a human agent when a more complicated action is needed.
The limited functionality offered by the tools has been a big barrier for chatbots and Machine Learning is an attempt to target this. Our model is completely based on Machine Learning. With experience, our chatbot learns responses to various queries and thus increases the functionality. Hence, the use of machine learning seems very natural for chatbots.
Sometimes, a bot can't just handle the mood of the customer alone. Customer may get irritated or angry and would not want to stick around talking to a bot and want the real deal. With sentiment analysis, the chatbot can auto-transfer the ticket to a human and the agent can handle the customer in a more professional way.
Our model is entirely pluggable and we create our own framework. Hence, if there is any recorded database you can integrate it in your chatbot. It will make your chatbot more intelligent and flexible.
In general, Chatbot is used to avoid the dependency on agents which can handle only few conversations at the same time and also proves to be very helpful for businesses that receive lot of enquiries on daily basis.
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