Reinforce conversational platform design and empower it with machine learning algorithms for improving users experience.
Complex conversational systems may be challenging to build. To help with that, we designed a visual tool to collaborate and create a chatbots ecosystem with minimal to zero knowledge of coding.
Our solution visually processes the bot logic and helps define the general flow of the conversation, both from the user and administration side.
|Client||AI-powered Chatbot Platform|
|Business need||no-code chatbot design platform|
|Scope||design, UI/UX, front end, backend|
Any conversational platform requires chatbot functionality. Here’s a bot diagram for flows’ visualization to enable a full view of the flow structure. The user can follow the possible missing flow elements and correct any issues. The user-friendly interface integrates available tools, turning it into a virtual assistant for business and technical users.
This depicts the processes to document, study, plan, improve or communicate the operations in clear, easy-to-understand diagrams. While representing the configuration of the conversation between the end-user and the chatbot, the flow diagram provides comprehensive information for each step of the conversation flow.
A comprehensive set of information for each step of the conversation flow, including contextual data and integration calls, provide a better understanding of the flow diagram’s operation and easy conversation flow & update. The expandable chat details allow the user to follow the actual conversation.
A style guide optimizes the development and unifies all interface spaces. It delivers UI solutions as a set of guidelines, parameters, controls, and components that make the user interface intuitive and consistent.
A challenge to build complex conversational systems is common for companies delivering chatbots. The presented visual tool enabling creation and managing the chatbot ecosystem has been built with minimal to zero coding knowledge.
The low-code solution is tailored to process the bot logic visually and helps define the conversation flow. Both users and admin can operate the tool.
Automated chatbot operations
Drag&drop builder for smooth diagrams’ creation
Supervision and detection of possible issues in real-time
Building a chatbot platform involves several key steps. First, define the purpose and objectives of the chatbot to determine its functionalities and target audience. Then, choose a suitable platform or framework for building the chatbot. Design the conversation flow and dialogues, considering user inputs and potential responses. Develop the chatbot using programming languages or visual development tools, integrating it with appropriate APIs or databases. Test and refine the chatbot, ensuring it provides accurate and relevant responses. Finally, deploy the chatbot on the desired channels, such as websites, messaging apps, or voice assistants, and continually monitor and update it based on user feedback and performance analytics.
Designing a chatbot involves several key considerations. Begin by defining the chatbot’s purpose, target audience, and primary use cases. Identify the expected user inputs and plan appropriate responses and interactions. Determine the chatbot’s personality and tone, ensuring it aligns with the brand or purpose it serves. Design a conversational flowchart or storyboard to visualize the user journey and possible paths. Create a database of frequently asked questions and relevant information to support the chatbot’s knowledge base. Iterate and refine the design based on user testing and feedback, continuously improving the chatbot’s user experience.
Several methods can be used to design chatbots, depending on the complexity and requirements of the chatbot. Some common approaches include rule-based design, where predefined rules and patterns dictate the chatbot’s responses; machine learning-based design, which involves training the chatbot on large datasets to improve its understanding and responses; and a hybrid approach that combines rule-based and machine learning techniques to achieve a balance between flexibility and accuracy. User-centered design principles, such as conducting user research, usability testing, and iterative design, can also be applied to ensure the chatbot meets user needs and expectations.
The platform of a chatbot refers to the underlying software or framework on which the chatbot is built and deployed. It provides the necessary tools, libraries, and infrastructure to create, manage, and host the chatbot. A chatbot platform may include features such as natural language processing (NLP) capabilities, dialog management, integration with various messaging channels, analytics and reporting, and user management. Examples of chatbot platforms include Dialogflow, Microsoft Bot Framework, IBM Watson Assistant, and Amazon Lex. These platforms offer developers the resources and functionality to design, build, and deploy chatbots across different platforms and channels.