A Comparison of Nine Chatbot Environments

A Comparison of Nine Chatbot Environments

Chatbots have become increasingly popular in recent years, with many organizations looking to implement them for customer service, marketing, and other purposes. However, with so many chatbot environments available, it can be difficult to choose the right one for your organization. This article aims to provide a comparison of nine different chatbot environments based on various factors such as NLU capabilities, dialog development tools, scalability, and enterprise readiness. The article also discusses current trends in chatbot development and provides a rating matrix for each environment.

Current Trends in Chatbot Development

There are several current trends in chatbot development that are worth noting. These include:

• Intent Deprecation: Intent deprecation is becoming more common in chatbot development. This involves introducing greater flexibility in user input and matching these inputs with dialogue nodes.
• Entity and Intent Fusion: Entities and intents are merging and becoming more prevalent in chatbots. Composite entities are also becoming more important.
• Edge Installation: Edge installation is becoming increasingly important, with some environments allowing for installation anywhere.
• State Machine Deprecation: State machines are becoming less popular in chatbot development, with some environments introducing automation and automatic elimination of ambiguous menus.
• Vertical and Horizontal Growth: Chatbot technology is growing both vertically and horizontally. This includes the introduction of complex entities and the move away from structured pre-set menus and keyword-driven interfaces.

Comparison of Nine Chatbot Environments

Based on the above trends and other factors, the following nine chatbot environments were compared:

• IBM Watson Assistant
• Microsoft Bot Framework/Composer/LUIS/Virtual Agents
• Google DialogFlow
• AWS Lex
• Cisco Mindmeld
• RASA
• NVIDIA Jarvis
• Oracle Digital Assistant
• Industrial-Strength Natural Language Processing

Each environment was rated according to the following five elements:

• NLU Capability
• Dialog/Flow Development and Editing Tools
• Scalability and Enterprise Readiness
• GUI/Form Call Flow Development and Editing
• Cost

The rating matrix for each environment is as follows:

It is important to note that the importance of each of these factors may vary for different organizations. For example, if a company has already invested heavily in Oracle Cloud or AWS, this may be a major deciding factor for them. It is also worth noting that cost plays an important role, particularly for initial prototype designs.

Conclusion

Choosing the right chatbot environment for your organization can be a difficult decision. However, by considering the above factors and trends, as well as the specific needs and goals of your organization, you can make an informed decision and choose the environment that best fits your needs.