Chatbots are starting to look fairly promising for businesses of all kinds. Customers today are keen to get things resolved faster than ever. Every startup out there is tempted to take the deal. But before jumping onto the bandwagon, you need to do some thinking as to what type of chatbot you must invest in. The decisive question being, which model of conversational AI perfectly aligns with the needs of your organization.
Allow us to weigh in on that one with a few suggestions that rank fairly high in the realm of AI-powered chatbots
Menu-based Chatbot
This is the simplest chatbot with a clear cut “press-button” functionality. They belong to a group of least complicated chatbots and have zero NLU involved in their design. Now, these chatbots may be no match for the nuances of the ones driven by NLU since they do not care much for processing user text. What they do have, is a set of predefined phrases. Once the user selects one of these phrases, the intent is set in motion.
Many of these chatbots don’t need any coding. Take a look at Landbot.io for a sneak peek into the capabilities of a no-code bot engine. There are tools in place to help build these bots, and designing the dialogue flow is mostly carried out using the visual editor. Understandably so, menu-based chatbots are a breeze when it comes to implementation. Except, they don’t seem so dandy on the engagement front.
A lot of businesses have had telling results with these bots. Especially for those businesses that follow the lean approach, this seems like the best bet. If all you are looking for is a customer service solution that can take care of simple queries, using a bot with predefined conversation is right on the money. Literally!
Rule-based Chatbot
As we move further on the scale of user input processing, we encounter what you call a simplistic rule-based chatbot. These chatbots are much more liberal when it comes to user behavior and give them the freedom to make queries without dictating the choice of words. What they do instead is, filter these inputs for keywords and phrase patterns and provide an output accordingly.
Although these are far less restraining in terms of input, the best they can do is barely simple text processing.
A major upside to a bot of this kind would be implementation time. A downside would be poor scalability. It is still far behind in the running when it comes to extending functions and vocabulary. The depths of Natural language still seem far beyond its reach. Chatscript is an example of one such rule-based engine that is based on dialog scripting.
NLU as a Service Chatbot
These solutions are enterprise-ready models, mostly powered by the cloud. The core idea on which these apps function is mapping relevant requests to specific intents. In other words, they ascertain user goals from the information given away during the conversation.
How do they manage that?
By improving the natural language models through active learning on the fly! Dictionaries mined from the web are used to supply a billion entries. As the entries pour in, the models grow and learn to catch valuable information from their interaction with the user.
Scalability is no issue with this type of chatbot whatsoever and the design perfectly syncs with the requisites of a commercial application. Besides, it is capable of decrypting the intent of the user, no matter how nuanced the query is.
The development however is an earnest effort and you are better off leaving it to the pros. By most accounts, an NLUaaS chatbot stands towering above the rest in its league, especially when it comes to upscaling and deployment time.
If you are thinking it couldn’t get better than this, hold on until we pitch you the jack of all trades. Now, you might say, NLUaas conveniently balances the scales of bot capability and cost of development. But there are still some aspects which are out of its reach.
And that’s when we would suggest you resort to the most adept of them all.
Chatbots with Custom NLU Stack
If you are looking to build highly contextual bots that are capable of managing complex conversations, you need to bring in a customizable NLU stack. This is something that can turn virtually any form of free text into structured data.
What this type of engine entails is an NLU model, dialogue management, and integrations, driven by open source components. A case in point would be the RASA stack that fulfills all requisites of the bot service in question.
It needs investment in additional infrastructure on your part but it sure is worth the hassle if you are looking for a high-end intent classification service.
Saas solutions are created to cater to a wide spectrum of customer requests. A bot with a custom NLU stack however can be manipulated on a micro level to competently handle requests in specific domains.
It is even possible to create a whole new bot logic with Name Entity Recognition process. Emory university’s NLP4J and CoreNLP by Stanford are classic examples of such bots.
Although these bots seem, dare we say, paragons of machine learning and dialogue management, it might not be in your best interest if you’re pressed for time and short on resources. Additionally, not many professionals out there are skilled at designing these engines. So make sure to weigh your options before you settle for a model as intricate as this.
A custom NLU stack, without a question, trumps all the others in terms of quality, flexibility, and potential. And when comes to conversational AI, nothing better than a bot that can engage users with superior interactive abilities.
Perks aside, this is still not the ultimate solution if you are looking to build a highly customized solution.
This brings us to the absolute solution!
How to Building a Custom NLU?
Naturally, there are no defined guidelines to help you through this procedure. All you need is a brain trust that masters the flair of data science and NLU. However, this option only merits attention, if you have enough time and money to spare.
In a slew of smart assistants for business, this option is only for enterprises looking to build high cognition AI-powered bots.
These happen to be our two cents to help you with the bot assistant endeavor for your business. We have tried to list the services in ascending order of ease of deployment and cost of development.
Start with a simpler solution and Test the waters for a while. If it works aligns with the needs of your business, well and good. If not, you can always jump ship and move on to an advanced option. Sooner or later every business has got to mobilize on AI innovation. Just make sure, when the time comes, you are prepared.