Chatbot platforms are essential tools if you need to build and run a chatbot.
There are many available on the market, big and small, popular and not so much.
Here are some useful thoughts that should help you navigate the complex world of chatbots and conversational AI solutions.
All chatbot platforms can be split into two categories: those that let you create chatbots without any programming, and those that require programming. Now, the idea that you don’t need to possess technical knowledge to build a chatbot seems appealing but the reality is not so rosy. In fact, I have yet to see a professional chatbot created without coding.
Chatbots rely on sophisticated algorithms and advanced knowledge of linguistics. These technologies are so complex that at the moment there are no plug-and-play solutions available. The companies like Chatfuel, Manychat, Flow XO and many others are attempting to fill that void and offer chatbot platforms that are simple in use. The best way to make the chatbot creation simpler is by dropping the need to code them. However this simplicity comes at a price: chatbots made without coding are limited, rigid and in general, primitive.
So to summarize: if you want to impress your girlfriend use Chatfuel. If you need a professional chatbot that delivers on your business goals and provides customer satisfaction use advanced chatbot platforms with programming capabilities.
One of the main, if not the main, tasks of the chatbot platforms is to connect your chatbot to the user interfaces. There are many ways for your chatbot to interface with the world: on Facebook messenger, on the website, on the mobile app, via SMS, on Twitter , on Skype, on Slack, on Telegram, and more. A good chatbot platform should seamlessly connect the chatbot to most of these channels. Chatbot platforms do not make your chatbot smarter. For this you need AI Engines (brief disucssion on AI Engines: http://nmodes.com/entry/2018/03/29/what-are-ai-engines-and-how-choose-one/).
For best results create your chatbot on a chatbot platform, then connect it to AI engine.
One of the top chatbot platforms on the market is Microsoft Bot Framework. It is robust, powerful, with a wide variety of useful functionality built-in. Another good chatbot platform is DialogFlow. DialogFlow has a slightly different architecture in the sense that it is a chatbot platform and an AI Engine all in one interface.
Chatbot platforms can be used to create conversation flow for your chatbot. There are several schools of thought here: some prefer to delegate conversation flow to AI engines. Chatfuel and other tools with the emphasis on simplicity (build your chatbot in minutes, no coding necessary) offer easy graphical interfaces for conversation flow creation. And there is always a reliable option to create conversation flow in an old-fashioned way, programmatically.
Which option to choose? Depends on your chatbot requirements and the business needs the chatbot is expected to address.And if you have questions feel free to ask: http://http://nmodes.com/contact-us/
CHATBOT PLATFORMS. How to choose the right one?
Microsoft AI products
Microsoft product strategy has always been and still remains that of ‘zero alternative’. Their ultimate policy is for their customers to have no choice but to embrace only Microsoft products. Consequently they created and are offering products and solutions in (almost) every segment of IT enterprise and consumer market, including, but certainly not limited to, their own data base, their own cloud services, operating system, office tools, programming language, and many more.
Not only do Microsoft offer wide variety of products, they tie them up together in a unified ecosystem that makes it easy for components to connect and interact. At the same time, this ecosystem is hostile to non-Microsoft products.
Microsoft strategy for the burgeoning, fast growing AI segment is similar:
Create products to address all parts of the AI market, add them to the ecosystem to ensure easy compatibility from within and difficulty of use from outside.
Currently the products on offer are:
- Microsoft AI engine, called LUIS. It is supposed to compete with other major industrial AI systems such as IBM Watson, and has similar training methodology. It offers webhook interfacing via endpoints.
- Microsoft chatbot building platform, called, surprisingly, Microsoft Bot Platform. It addresses the popular demand for easy chatbot design and provides seamless connectivity with main user interfaces, such as web interface, SMS, mobile, and messaging platforms.
- In addition Microsoft offers their own messaging platform in Skype.
The main advantage of using Microsoft AI products is the built-in connectivity with user interfaces.
The main disadvantage is in their ‘zero alternative’ policy - once you’ve chosen a Microsoft product you are likely will be forced to choose only Microsoft products for the duration of your project.
When Big Data is not so big anymore
We are inundated with information. There is so much information around us they coined a special term - Big Data. To emphasize the sheer size of it.
It is, of course, a problem - to deal with a large amount of data. Various solutions have been created to address it efficiently.
At nmodes we developed a semantic technology that accurately filters relevant conversations. We applied it to social networks, particularly Twitter. Twitter is a poster child of Big Data. They have 500 million conversations every day. A staggering number. And yet, we found that for many topics, when they are narrowed down and accurately filtered, there are not that many relevant conversations after all.
No more than 5 people are looking for CRM solutions on an average day on Twitter. Even less - two per day on average - are asking for new web hosting providers explicitly, although many more are complaining about their existing providers (which might or might not suggest they are ready to switch or looking for a new option).
We often have businesses coming to us asking to find relevant conversations and expecting a large number of results. This is what Big Data is supposed to deliver, they assume. Such expectation is likely a product of our ‘keyword search dependency’. Indeed, when we run a keyword search on Twitter, or search engines, or anywhere we get a long list of results. The fact that most of them (up to 98% in many cases) are irrelevant is often lost in the visual illusion of having this long, seemingly endless, list in front of our eyes.
With the quality solutions that accurately deliver only relevant results we experience, for the first time, a situation when there are no longer big lists of random results. Only several relevant ones.
This is so much more efficient. It saves time, increases productivity, clarifies the picture, and makes Big Data manageable.
Time for businesses to embrace the new approach.