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Chatbots currently represent the top use of AI in enterprises, and their adoption rates are expected to almost double over the next two to five years. Chatbots are growing more powerful each day, and conversational AI chatbots are among the most sophisticated.
In this article, we’ll review everything you need to know to build a conversational AI chatbot; what they are, what makes them “smarter” than your average chatbot, and how to build one.
A conversational AI chatbot uses natural language processing and/or machine learning to simulate human conversation. This is different than other types of chatbots that use more basic automation.
Conversational AI chatbots are more sophisticated in that they can be trained to understand context and intent. Let’s take a look at how they compare to “regular” chatbots—or those that run on rule-based automation.
A “regular” chatbot usually follows pre-programmed rules. That’s why they’re often called rule-based chatbots. They follow IF/THEN logic, or conditional logic.
For example, if X happens, then the chatbot does Y.
Artificial Intelligence (AI) chatbots, on the other hand, are designed to simulate human thinking. They are trained to interpret and understand the conversation on a broader level.
To summarize: Bots with automation follow pre-set rules. Bots with AI mimic human thinking.
Learn more about the differences here: Rule-Based Chatbots vs. AI Chatbots: Key Differences
Conversational AI chatbots use natural language understanding (NLU) to mimic human thinking. NLU lets you capture user inputs by intent. This is different than rule-based automation, which works by capturing user inputs, using regular expressions, and parsing raw text.
NLU helps you capture all of the different ways users can express a unit of meaning that is valid for your bot. You can then map that intent to an action using a route.
Although they take longer to build initially, AI conversational AI chatbots can save time in the long run. They are able to explore patterns, learn from experience, and get “smarter” the more they interact with people.
Another benefit of conversational AI chatbots is how they can connect customers’ previous questions to new ones. They use real-time analytics and data to make contextual decisions.
These abilities are important for conversations that are unstructured and unpredictable. Again, if conversations are predictable and structured, they are easier to automate with rule-based logic.
In short, conversational AI chatbots can:
Disclaimer: It is important to note that for most businesses today, rule-based automation is sufficient to automate most use cases. The majority of our customers, for example, find that rule-based automation is enough to achieve their goals.
Rule-based can use very simple or complicated rules. Even though the technology is considered less sophisticated, it can be programmed to handle complex use cases.
Though, for companies that need their chatbots to understand a wide range of inputs and languages, it may make sense to build a conversational AI chatbot.
There are many ways to build a conversational AI chatbot. You could hire a chatbot development company to build you a custom chatbot from scratch, or you could go with a code-based chatbot framework.
We’re going to skip right past no-code chatbot builders. If you’re interested in conversational AI, a no-code chatbot builder is not the right solution for you. A no-code approach will not be flexible enough for your use case—it’s more suitable for basic automation and simple use cases.
For our purposes today we’re going to focus on how to build a conversational AI chatbot with a code-based framework. This will give you control over the development without having to start from scratch.
Code-based chatbot frameworks are a good choice for businesses that want an efficient way to build a conversational AI chatbot in-house. They serve as a foundation, giving you the building blocks to create solutions that work for your unique needs.
Code-based chatbot frameworks are best for:
With a chatbot framework like Hubtype’s Botonic framework, you can create a custom chatbot or use a pre-made template to speed up the process. This gives you freedom, flexibility, and speed to create custom solutions for your unique needs.
When you’re choosing an AI chatbot framework, make sure it has all of the building blocks you need. For example, Hubtype’s Botonic framework has the tools you need to build and iterate fast.
These conversational building blocks include:
The plugin library will save you a lot of time when adding conversational AI functionality. More details on this below.
An excellent conversational AI framework will have NLU plugins (or functionalities) that can be injected into any bot. These plugins allow you to create different intents and assign keywords or phrases. These will match with the intent and direct to the corresponding route.
With our framework, you can connect to powerful third-party NLU services like:
We also have our own powerful NLU plugins that you can choose from, too.
For tips on how to train your NLU chatbot, read: How to Train a Chatbot: 8 Effective Tips for Training AI
Hubtype provides the conversational infrastructure you need to deploy your conversational AI chatbot. You can follow our step-by-step guide to deploy your bot here. This will allow you to get started with our free plan that allows up to 500 monthly active users.
You’ll also be able to integrate with messaging apps and bypass the verification process. You can easily manage the chatbot/human handoff using our CRM or your own.
With Hubtype, you won’t need to worry about maintaining infrastructure and its scalability. We do all of that for you. We also give you all the tools you need to make your conversational AI chatbot privacy and GDPR compliant.
Contact our sales team to discover how you can use Hubtype’s framework and infrastructure to build a conversational AI chatbot.
Find out how global brands are scaling their customer communication strategies with conversational apps.
