Chatbots

Chatbots

Chatbots are supported in Trendskout within Trendskout’s connectanalyseautomate three-step flow. During the connecting stage, you can link Trendskout with the chatbot’s digital channel. This enables Trendskout’s AI to turn your chatbot into an intelligent and self-learning entity.

After connecting to input and training data, you can select the appropriate training method for your chatbot. This choice is based on the availability of training data. Trendskout can set the chatbot to work with a particular data set for self training. If no training data are available, the chatbot can be trained through interaction with your end users.

The actual chatbot training uses a combination of techniques and algorithms that Trendskout’s AI carries out independently, requiring minimal human intervention. Read on to discover a few of the underlying techniques and principles.

Practical business applications

- Customer service automation
- Intelligent website chatbots
- IT helpdesks


artificial intelligence

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Chatbots in the AI Flow

1. Connect

2. Analysis

Chatbot

Chatbot is one of the available Trendskout AI Flow analysis-functions.

3. Automate


How does this work technically?

NLP - Natural Language Processing

In order to read and understand the text entered in the chatbot, it must be processed. For this, this text is split into various parts, an internal tree structure is generated, and this is linked to other texts and question / answer combinations to discover semantic connections. Relational contexts are discovered based on this technique and the algorithm can provide meaningful answers. There are two common ways in which Trendskout can prepare this answer, Natural Language Generation (NLG) and Classification.


Natural Language Generation

NLG techniques can independently, based on training data, generate texts that resemble answers that people would draft. This is a very advanced technique based on the latest developments in Deep Learning and hybrid neural networks. NLG algorithms are very powerful but require very large amounts of training data. If the training data is not large or qualitative enough, the generated texts will be of little significance. For most organizations it is therefore recommended to work with a different, ready-to-use approach for chatbots: text and answer classification.


Text and answer Classification

This method uses a modified form of text classification. In a training phase, different question-answer combinations are analyzed by algorithms in Trendskout and a model is drawn up to provide the most suitable answer to a question. The question may differ from the form and content of the training data, NLP preprocessing extracts the semantic and contextual meaning of the question.

Chatbots + Trendskout

Via AI and Deep Learning flows in Trendskout, the model behind the intelligence in a chatbot can be created in an accessible manner. You can determine the front implementation yourself, and link it to the Trendskout AI model which controls the chatbot.
Just like with other Deep Learning functions, Trendskout automates the entire training process for chatbots including algorithm selection and hypertuning.

Ready to discover all features during a live demo - with your data?Get in touch and we will be happy to show you the direct business value of artificial intelligence for your organisation.