Chatler: AI for Conversations at Scale
The past year has seen a boom in chatbots, which have become a buzzword in the tech industry, most particularly with retailers and big brands. StartupYard this year handled dozens of applications for chatbot startups, but despite the buzz, none of these seemed to us to have really discovered the inherent value of automating customer interaction on social media, and in customer care. Chatbots are not a new idea, after all, and much of the recent hype has come thanks to Facebook opening its platform for 3rd party developers, which has spurred renewed interest in new applications for chat.
Facebook’s strategy is bold, and we believe that it may yet yield positive net benefits for end-users and customers, but the chatbot arena is still very immature. And so Janos Szabo gained our attention immediately when we met him and his team during our roadshow, in Budapest last summer. Janos convinced us that the future of chatbots, at least for now, is not in full automation, but in AI integration. He founded Chatler late last year with a team of friends, including strong experience in brand management, to prove that the way forward for chat with big companies is in helping human beings to do what they do best: be human,
I caught up with Janos this week to talk about his vision for Chatler. Here is our discussion:
Hi Janos, tell us a bit about Chatler. How did you come up with the idea?
I’ve worked a bit with chatbots. I was actually on a team that was creating them. But it quickly became apparent that chatbots aren’t the massive game-changer they’ve been billed as. They aren’t a “fire and forget” sort of thing. They need a lot of care, and a lot of time, and as a result they never end up being as good or as efficient as we hope they will be.
There’s a cycle to AI products in general. It starts with hype: a new way of doing something has its day in the sun, like chatbots, or language translation, and early demonstrations are incredibly promising. But when they’re released into the wild, these things just never work as well as we hope they will. They always have their limitations, and those limitations become apparent relatively fast.
In something closed and strictly defined like Chess, or Go, or even driving a car or landing a rocket on Mars, the rules are clear and the inputs are fairly simple to deal with. But humans are organic, and human conversation is chaotic. Subtlety and context that is easy for a human is incredibly difficult for a machine. I couldn’t land a rocket on Mars, but I can probably handle a customer interaction better than a bot that cost a billion dollars to make. So we have a long way to go.
The problem with chatbots today is the same as it was 20 years ago, when chatbots first were popularized. Some may remember Aimbots, that could answer simple questions according to a hardwired answer-tree. Essentially, modern chatbots work the same way, but pull information from more sources, and understand the intent of questions, perhaps a little better than they used to, when the only thing they really understood were keywords.
Still, 20 plus years, and chatbots can’t do basic things. They can’t judge a person’s reactions to what they say very well. They can’t tell if a person is annoyed, or is making a joke. They can’t think like we do. And they can’t improvise the way a person can.
You wouldn’t trust one to handle your high-value customers, and you might not even trust one to run your Twitter feed. We’ve seen from live experiments from companies like Microsoft that chatbots are still poorly understood. They created a Twitterbot that quickly became an anti-semitic Nazi sympathizer. Of course, it was just doing what it was programmed to do, but that was part of the problem. A chatbot, at the basic level, doesn’t have any sense of morality or of right and wrong. Even backed up by fairly sophisticated AI, it doesn’t have a conscience to guide its actions. It just reacts to inputs.
Now, AI is about learning. We can teach bots how to simulate our own sense of right and wrong. But that’s still on the horizon for modern technology. Today, we still very much need humans to be at the center of communications with other humans. So I started to realize that what we needed to do was, for now, let go of the idea of building bots that can be fully automated.
We need bots that can learn from us, by showing them how we interact with other people. In time, they can begin to take over tasks that are relatively simple. For example, answering a common question, or accessing an internal system to check a customer’s account information, and other routine tasks that bots should theoretically be very good for.
AI can do a lot of things better than a human can. Particularly routine tasks, but also more complex ones, if there is enough opportunity for the AI to learn them. That’s the assumption that Chatler is built upon. But humans aren’t going away. Until we build an AI that can really feel as we do, there will always be a need for real people.
You’ve actually been selected for StartupYard once before (but didn’t attend), as part of a now defunct startup. Tell us a bit about that experience, and what you learned from it.
Yes, I was one of the founders of ClipDis, which was a tool for message platforms. It was a really catchy product. You could type something in, and it would construct a short video of the sentence using clips from TV and movies. People loved it, and it was fun.
It was a great opportunity for us, but we didn’t fully understand our market, or how to create a real business out of the idea. You have to establish your fundamentals earlier, and spend time on coding later. We were a bit naive, and a bit arrogant, because we had such a cool product, and we thought we could just worry about getting lots of users, and take care of the business part later.
But, especially in Europe, that just isn’t something investors want to be involved with. A hard lesson for us, and something I’ve taken with me to Chatler. I’m still deeply interested in the way people message each other, and how we can understand and enhance our ability to effectively communicate. That has not changed.
What is the market getting wrong about chat, and how does Chatler get it right?
The market right now knows that chat, in some form, is a huge part of the future when it comes not only to customer care, but also to sales and even business development, research, and other fields. So there has been a rush into this space, starting all the way back with Facebook’s acquisition of WhatsApp 4 years ago.
Chatbots seem like an elegant solution to the problem of chatting with your customers at a large scale, but as I’ve noted, they have some really basic limitations. They are good at tasks, but in authentic conversations, they are totally lost. That’s why I see Human/AI collaboration as a key step forward in chat technology. If chat is indeed going to dominate as a future medium for customer communication, then AI is going to play a big part. We just don’t believe it will be the driving force.
Chatler helps big companies and brands make their chat channels more efficient, more responsive, and easier for their customer care and sales agents to use. It does this by learning from the way real people talk to customers, and teaching itself to do routine tasks that real people do. For example, Chatler will quickly learn the best answers to common questions: “What time are you open?” It will then be able to progress to more complex topics: “How do I get to your offices from my home?”
Chatler will be there suggesting answers for a human to decide upon and use. But eventually, Chatler is going to make that human better at giving answers. Humans make dumb mistakes, like typing a wrong letter, or misreading a map. An AI doesn’t make those kinds of mistakes, so the two working together create a better overall customer experience. Fewer mistakes, faster responses, and better information.
There are a few paths we can take from there. We can more fully develop a recommendation engine that can be applied horizontally to different modes of communication, and/or, we can integrate Chatler more deeply with a company’s operations, enabling it to help a chat agent to accomplish tasks that would take a person a lot of time. Things like looking through databases to find one particular item, or checking multiple sources for one key piece of information. An AI can do in a moment what it can take a person hours to do on their own.
For example, an AI could search your entire communication history with a company instantly, and find out exactly what problems you have had in the past, while a human would take a long time to find and understand all that information. We think humans should be left to do what they are best at- which is caring about other people.
Humans have emotional intelligence. And machines don’t. Certainly not yet, anyway. So the goal shouldn’t be to fake emotional experiences, but to enhance a human’s ability to focus on what they’re best at.Humans should focus on what they are best at: emotional intelligence. @ChatlerAI Click To Tweet
Why are big brands so interested in moving their customer care operations to a chat format?
A few reasons. FIrst of all, chat is where people are now, particularly younger people. People under 30 now rarely talk on the phone, and school-aged kids today essentially never do, unless forced to. They now see voice-calling as invasive. Some people don’t answer voice calls anymore, directing people to message them instead.
Messaging has become a very visual and creative medium thanks to innovations from Snap, Facebook, and others. It has become a prefered way of communicating between friends and family, and even with some businesses. It’s convenient, and flexible, not requiring two people to pay complete attention to a conversation at one time, and creating an easy record of what’s been said.
And for those who remember the early days of ubiquitous mobile phones, chat is more private, and it is also less intrusive to people around you.
Secondly, it’s cheap. This is partly why chat became so popular to begin with, because sending electronic messages uses less data or “calling minutes” than making a call or using VOIP. A customer service person can also service multiple inquiries at once using chat- something that a voice-call center can simply not do. That means that fewer overall people are needed, because the attention of a chat agent is more divisible.
It’s much more important to have more agents available when there are a lot of customers contacting you. And it’s much simpler technologically- without the need for expensive equipment, phone switches, and complex phone-trees and recording equipment.
There is a universe of Customer Relationship Management (CRM) tools and platforms out there. How does Chatler fit into the broader market for these products?
Chatler is focused on chat-based CRM. It will take the form of an end-user solution, but we also plan to offer it as an API for existing CRM providers. That way, if a company has invested deeply in a system that closely matches their needs, Chatler will be easy to integrate in order to make that system more effective and efficient.
Chatler is data driven. Many CRM systems, even those designed for chat, are driven mainly by solutions to logistical issues. We don’t plan to disrupt that, and we don’t pretend to be able to completely replace existing platforms. Instead, we’ll bring a data focus that will help companies in any stage of their chat development. In many cases, our aim is for Chatler shorten a 3-step process into a one-click process, or a 10 step process into a 3 step one, Moreover, Chatler will learn continuously, meaning that what it can’t do today, it can learn to do tomorrow.
For companies that have never used chat, they can start with Chatler. For companies that are deeply invested in chat already, even better. Chatler will seamlessly enter the flow of existing systems, and save chat agents time and stress throughout the day.
You’ve recently worked on a set of trials with real brands. What did you learn from this experience?
Well, I will be diplomatic and not describe the processes that we’ve observed. We can say that some of them are archaic. Copy paste and spreadsheets are not uncommon. In many ways, the problem is typically that big companies haven’t taken chat seriously from the beginning, and now demand has seriously outweighed their ability to leverage chat. This keeps many brands from even using chat, and it keeps others from using it effectively, or investing in it further.
Companies are just coming around to the reality that chat isn’t going away, and that it will have some fundamental effects on the way they do business. But that’s just starting. As they did when social media first appeared and changed the way businesses communicate, big companies are constantly weighing the risk and the reward of diving into a new form of communication.
That has helped us to understand what companies need from us. And often what they need is someone to tell them that there is a place to start- a way to make chat an organic part of your business, just as telephones became a part of business a century ago, and the internet became a part of it 30 years ago.
How do you see Chatler growing within the next year? Who are the obvious first customers?
As a standalone platform, SMEs can use Chatler as their primary solution for chat. It will always be better than a native chat platform, because it will be dedicated to learning exactly how your business, and only your business runs, and what kinds of things you communicate, and how you do it. This will extend to mobile as well, as more and more, customers expect to be services on mobile chat.
Large enterprises will integrate Chatler with their existing CRM platforms, and for that we’ll need a global sales team, and a richer, fuller analytics product that helps companies to understand the value that chat is bringing them, and gain actionable insights on their whole chat-oriented customer care operation.
How do you think Chatler will play a role in the further future, 3-5 years from now?
Eventually, consumers will come to expect a chat experience from businesses that mirrors the experience we currently have of the web. Chat will be highly automated, and “explorable,” working together with the customer to solve their needs and find new opportunities for them.
We won’t get to that state by building chatbots. No amount of planning can really tell you how people will use chat. Instead, it will be based on an accumulation of experience, reflected in the complexity of the machine learning algorithms behind Chatler. Over time, Chatler’s AI will become adept at learning about a new company, and finding ways to help take over tasks that it can automate, and interactions that it can handle on its own, then only referring to humans when it is unsure of how to proceed.
This has deeper implications than just making businesses better at chat. Websites and social media both also transformed the activities that businesses actually engage in. It affects the products they create, and the way they do business. Chat has that same potential. The data and experience that Chatler’s AI will gain from conversing with real people will translate to actionable business data, helping companies make better decisions with products, services, customer relations, and more.
Chat will become an opportunity, rather than a liability.
Tell us a bit about your team. How did you all start working together?
We know each other from our previous startup projects, years spent at multinational companies, agencies, and several meetups.
András Reder has 11 years of experience at Coca-Cola, leading cross functional teams in a matrix organization. He launched over 17 products/brands into international markets with Coke. Having seen the industry from the inside, András wanted to challenge himself in new projects where A multinational background doesn’t necessarily mean success. He brings the business oriented mindset into our team and makes sure that we all line up from operational POV.
Andris is a freshly graduated coder and bot enthusiast. He organizes the Chatbot meetup in Budapest, supported by Microsoft Hungary. We met at one of his meetups and we immediately jumped into a passionate discussion about chatbots and the future of chat communication. It was an obvious fit for the team. Beyond coding he is also contributing heavily to conversational UX related topics.
With Gábor, Bence and János, we created one of the first chatbots for KIK Messenger’s global bot shop launch (other launch partners were Vine, H&M, WeatherApp). This was the time we started to experiment with recommendation engines, and we realized that even the best algorithm is useless if it doesn’t starts with a clearly defined use/business case.
You’ve known about StartupYard a long time. How has the experience been now that you’ve finally joined us for acceleration?
First I wanted to stay that I should have gone to Startupyard with my previous project. But now I feel lucky that we joined with this project. Having learned the lesson the hard way helps me to appreciate and value the feedback even more. Maybe I would have been more stubborn without that experience behind me.
StartupYard really helped us explore many possible futures, and grow confidence in some of our ideas, while letting others go for now. Instead of sitting in classrooms we were pushed to go out to the jungle and meet possible clients, and investors. This helped us to shape and sharpen our vision through real life feedback. I’m very excited about the progress we’ve made, and where we’re going.