It's been said that nostalgia isn't what it used to be, and in the world of technology there's a lot to be nostalgic about! Just over twenty years ago client-server was all the rage, and then the internet arrived and suddenly browser-based systems became the new way to do everything. Then in the mid-2000's Apple coined the phrase, "there's an app for that", which then begat a mad rush to build phone-based apps for everything under the sun. And now, in 2018, all of those things have been usurped by a new UI that will change the technology landscape yet again: chatbots. And, for generation Z or millennials, this is the way they expect to be able to interact with everything.

What do we do when we need any information? Simple: "We Ask, and Google Tells". But if the answer depends on multiple variables, then the existing Ask-Tell model tends to sputter. State of the art search engines usually cannot handle such requests. We would have to search for information available in bits and pieces and then try to filter and assemble relevant parts together.

In Universal Chat, we've implemented a chatbot which can be trained by anyone effortlessly. The logic is simple; just give it your questions and answers, and the bot will reply to any customer who asks something similar. In this paper, I'll show you how we've done it so you can do the same. To start, I've tested many different Machine Learning frameworks out there -- Wit.AI, Microsoft Cognitive services, I had a peek at Luis… for me, it turned out that API.AI was the correct choice. It was totally free with a good response rate, it had a great user interface for development, and it was easy to setup, get started with and expand.