We’re pleased to announce that StartupYard will take part in Startup Safary Budapest, April 20th and 21st, 2017.
What is Startup Safary?
Budapest turns into a startup exhibition for 2 days
20/04/2017 17:30 – 18:00 – thehub.hu, 1061 Budapest, Paulay Ede utca 65.
21/04/2017 TBA Mosaik, 1136 Budapest, Pannónia utca 32.
StartupYard helps technically sophisticated developers and makers turn their ideas into real, growing businesses. In recent years, we have helped launch a series of high tech startups including TeskaLabs, Neuron Soundware, Cryptelo, and Rossum.ai. Find out how these startups went from a brilliant idea, to companies serving clients all over the world with cutting edge technologies.
20.04.2017: 13:00 – 16:00 – thehub.hu, 1061 Budapest, Paulay Ede utca 65
21.04.2017: TBA – Mosaik, 1136 Budapest, Pannónia utca 32.
This is your chance to meet the management team of Central Europe’s leading seed accelerator for tech startups, and find out how we can help you turn your experience and knowledge of AI, Machine Learning, IoT, Blockchain, or Cryptology into a globally scaleable business. Come to find out about our program, pitch us an idea, or make a connection.
How do I meet the StartupYard team in Budapest?
There will be a few opportunities. First, we warmly invite you to join our workshops at TheHub and Mosaik, where you can hear about real-life examples of startups that have been through our program, and what they have accomplished as a result.
You can also sign up for our office hours. Because this event is happening under the umbrella of Startup Safary, you should sign up directly on their platform, and you will need to purchase a ticket on their website (tickets are just 8 Euros, and go toward organizing the events).
We will update this post when we have times for our appearances at Mosaic on April 21st.
We look forward to seeing you!
On April 11-12 2017, StartupYard will be visiting Sofia to meet with Deep Tech startups, and offer two workshops – one on turning an AI idea into a global business, and the other on storytelling for Deep Tech startups.
Before our next official visit, which is to be StartupYard’s third in beautiful Sofia, I talked to Petya Lipeva, Chief Navigator of CowOrKing by Puzl, one of Sofia’s hottest tech startup spaces, about the tech scene in Sofia, how it’s changed in recent years, and what we can expect in the near future from Bulgarian startups. Here is what she had to say:
Hi Petya, first why don’t you tell us a bit about yourself, and your path to becoming Chief Navigator at Puzl?
Hey StartupYard-ers! My name is Petya and I’m the Chief Navigator at Puzl CowOrKing – a coworking space for IT professionals and IT startups in Sofia, Bulgaria. I joined the team a month after they opened the first space.
My background is in PR and marketing for a Bulgarian tech company in the CG industry. For a few years I was traveling the world and I needed to settle down, so I tried to do some freelancing work. However, being a freelancer is quite a lonely experience for a person that is used to working with big crowds.
I was considering relocating to France or Italy when I found out that two guys opened a new shared space in Sofia and they were looking for a person to run the space. I met the team for a lunch and this was the most random job interview that I ever had. It was a couple of days before the start of a big CG conference that I was organizing and my phone was ringing all the time. Meanwhile we were speaking about anything but my role in the team. It’s amazing how you click immediately when you find your people!
What makes Puzl CowOrKing, aside from any other workspace in Sofia, a special place?
First of all Puzl CowOrKing is the first industry focused coworking space in Sofia. We created the space to help IT companies and professionals to grow and develop together. The amazing industrial design is complemented with different areas to foster efficiency, collaboration, and creativity both for companies and individuals.
We started with one space in October 2015 and a few months later in May 2016 we opened a new floor that we created specially for early-stage startups. In the beginning of April 2017, we’re opening another floor with 10 dedicated offices for small startup companies. The idea is to have different areas so a team could start with some desks in the area for the early stage startups and move across the different zones as the team grows and develops.
Do you have some success stories from your own alumni you’d like to highlight?
Yes, we do have quite a lot of success stories! I’d say that the whole community of 250 professionals is one amazing success story.
We have quite a lot of starting companies that are funded by different funds and accelerators. We have a few examples of individuals who are starting alone and growing a team of 10 people. My best success stories however are the collaboration stories – I love seeing how companies and professionals in the space start working together and find it valuable to exchange experience and resources.
StartupYard is about to make our 4th visit in as many years to Sofia. What would you say have been the most profound changes about the city and the tech ecosystem there in the past decade? What can we expect when we visit next month?
I would say that about 10 years ago in Bulgaria only a few people had heard the word ‘startup’ and the entrepreneurship ecosystem basically didn’t exist. However a decade is quite a long period and the ecosystem is thriving now.
Last year Forbes Magazine named Sofia one of the top 10 destinations for starting a business. Only three years after the first venture capital funds Launchub and Eleven started; they have already about 200 start-up companies in their portfolio. Recently we also witnessed one of the largest exits in the region, with a value of 262.5 million USD after acquisition of Telerik by Progress Software.
Quite a lot of organizations are working on organizing entrepreneurship events and trainings and the country is definitely showing a great progress and could show a thriving and interesting tech ecosystem.
What are, in your view, the biggest strengths of Bulgarian tech people, engineers, and thinkers?
I’ve noticed that the engineers in Bulgaria have amazing tech skills – they are talented, dedicated and passionate about their projects. They are real problem-solvers and quite interesting people – there are some quiet ones, loud ones, crazy ones, real inventors, thinkers, and players.
To you, which have been the most interesting and inspiring successes in tech to come out of Sofia or Bulgaria in the last few years?
The most inspiring success to come out of Sofia is Chaos Group – the company that develops the rendering plugin for 3D computer graphics software applications – V-Ray. Chaos Group founder Vladimir Koylazov received an Academy Plaque at the 2017 Academy Awards. The award, presented by the Academy’s Scientific and Technical Awards Committee, recognizes V-Ray’s role in bringing realistic CGI to the big screen.
Another great success is the above mentioned largest exits in the region by Telerik. We have quite a lot of interesting starting tech companies and I’m sure that we’ll see many more great stories in the upcoming years!
What kinds of things do you think we can expect from Bulgarian startups in the near future?
I’d say that we’ll see many exciting products and services coming from the Bulgarian startups and I’m sure that we’ll see many teams growing worldwide. I’m sure that soon enough I’ll be saying proudly “he/she used to work in Puzl CowOrKing”.
Bulgaria is a relatively small economy. Does this force Bulgarian startups to think bigger? Does it have a negative impact on startups looking for their first customers and their first investor?
Yes, Bulgaria is a small economy but it’s a great floor for product testing before going outside the boundaries. The small economy is definitely pushing the startups to grow bigger and to find bigger markets. However, I don’t think it has impact on finding customers and investors, as nowadays we’re all easily connected and because of the thriving ecosystem in the region. It’s easy to access many resources outside the country.
How would you rate the local and state government’s involvement in the tech ecosystem? What are they doing right, and what are they doing wrong?
The local and the state government is not much involved in the tech ecosystem although they are trying a lot. It takes time – the administration in Bulgaria is quite slow in picking up new trends and the tech ecosystem is still something that they don’t completely understand and relate to what’s happening in a starting company. I think the administration needs to attract a new generation of people in order to start helping out the tech ecosystem.
We’re excited to announce that on the 11th and 12 of April, StartupYard visits Sofia, Bulgaria, to meet with Deep Tech startups, entrepreneurs, and others with ideas for businesses built around AI, AR/VR, cryptology, blockchain, IoT, and related technologies.
Our visit will be at Puzl CowOrKing, one of Sofia’s most exciting startup workspaces.
This is the first of 5 visits to Central European capitals this spring, with an eye to attract brand new startups to StartupYard Batch 8.
DEEP TECH FOCUS
The focus of StartupYard Batch 8 will be “Deep Tech.”
Deep Tech means companies working on technologies and products that are unique, difficult to replicate, or are exploring areas of innovation where the barrier to entry remains high, and the problems scientifically complex and difficult, such as Robots, AI, IoT, VR/AR, and Cryptography.
Today, the barrier to entry for globally scalable startups is lower than ever. However, there are still tremendously complex problems left to solve. In years past, our focus on the Data Economy has shown us that there is a growing need for novel approaches to the way people work, communicate, do business, participate in the economy, and understand the world around them.
Deep Tech solutions seek to develop never-before-possible opportunities to profoundly alter the way everyone, not just the tech industry, works, thinks, and sees the future. Deep Tech companies work at the edges of possibility for emerging technologies, and so have the potential to disrupt and change whole industries overnight.
Taking the time to apply costs you only a bit of your time, and is the first step in the StartupYard selection committee and investors getting to know you and your team. There is no risk in applying, so why not start today?
StartupYard “Training Days”
April 11th and 12th in Sofia will be StartupYard’s first visit to one of 5 cities, including Budapest, Bucharest, Vilnius, Krakow, and Sofia. Unlike a typical roadshow, where an accelerator gathers early-stage startups to show off their pitches, StartupYard will instead offer workshops for Deep Tech engineers and idea makers in these different cities, about how to turn a high tech concept into a real business.
From AI to a Real Global Business- April 11th at 16:00, Puzl CowOrKing
Do you have a Deep Tech idea that could potentially become a tech startup? This is your ideal chance to find out what it takes. StartupYard CEO Cedric Maloux will walk attendees through the process of turning AI and other Deep Tech startups into thriving businesses, from proving their concepts with real-live pilot customers, to signing their first paying clients, and gaining venture investment.
Deep Tech Positioning- April 12th, 10:00, Puzl CowOrKing
In this workshop, StartupYard’s head of communication and community Lloyd Waldo (that’s me), will show would-be entrepreneurs how early stage startups in Deep Tech can use practical storytelling skills to convince the earliest stakeholders (including cofounders, investors, customers, and employees), of the power of a new idea, by transforming it from dry description and speculation into a compelling narrative, that puts you in control of the conversation.
This workshop will include hands-on strategies for positioning that will provide entrepreneurs with the toolset necessary to construct a persuasive and powerful story about themselves, and their vision of the future.
Open Hours, April 12th, Puzl CowOrKing
Do you have a Deep Tech idea and a great team that you think is worthy of funding and acceleration at StartupYard? Are you ready to take the next step and run your own Deep Tech company? Now is your chance to meet the StartupYard management team, and tell us something about it.
It didn’t take long. Yesterday afternoon, as the StartupYard teams were relaxing and quietly gearing up to pitch at the StartupYard Batch 7 DemoDay, the Rossum team Petr Baudis, Tomas Gogar, and Tomas Tunys were signing a seed funding round.
A few hours later, live on stage at the biggest demo day in the region’s history, (and with nearly 1,000 tuned in live on Facebook), Rossum announced that the respected Czech investment firm Miton, had contributed seed capital to help propel the team toward global ambitions. This morning, CzechCrunch ran with the story on its front page as well.
About the Deal
The investment solidifies an existing relationship between the Rossum team and Miton, that began with Rossum’s founders consulting with Miton’s portfolio companies on machine learning projects. Miton Co-Founder Ondrej Raska had, according to the Rossum team, been looking for a way to enter the machine learning and AI field, but had so far not come across a project that was clear enough to dive into.
That changed when Rossum approached Raska and Miton with the idea of automating invoice management, along with a host of other challenges, using a unique approach to machine learning. Discussion quickly shifted to a strategic partnership and investment, with Miton to become an active part of the Rossum project, and Raska to play a day-to-day role in the growth of the startup. Rossum has already produced a proof of concept that they say can beat OCR technology, and is approaching human level accuracy:
— StartupYard (@startupyard) February 22, 2017
Co-Founder Tomas Gogar said of the investment and cooperation: “We think that Miton is an ideal partner for us. They are very active in the companies they invest in, helping to shape their products. Their history shows that this approach has paid off, and we believe that it will be a big help to us as well. For us, as a very technically oriented team, this is a new experience. We feel that we can help Miton push forward into the Artificial Intelligence playing field.”
Raska spoke to a similar sentiment, saying: “Cooperating with Rossum is a unique opportunity. They’ve built a great team, with big potential. Moreover, the timing is right, with deep neural net technology becoming a game changer.”
When asked about the role StartupYard has played in getting them to this point, Co-Founder Petr Baudis said last week: “StartupYard finally gave us the impulse to really focus on one single thing – we were busy people before, but now we had the reason to finally drop all the side projects for good. We thought the first mentoring month would be the most intense phase, but the pace is only picking up since, and without the “little” pushing by the StartupYard team we would be much more comfortable, getting a good eight hours of sleep a night, but still at the beginning….it surprised us how eager the core StartupYard team was to help with their experience and feedback, these few people really became an important part of Rossum’s story.”
Miton, which has backed a string of successful startup projects including food delivery startups DameJidlo (another StartupYard alum from 2012) and Rohlik, e-commerce platform Heureka, the booking platform Hotel.cz, and the popular coupon platform Slevomat, runs a portfolio of investments worth upwards of 10 Billion Czech Crowns (370m Euros).
Many of Miton’s investments are in Czech-specific consumer facing service companies, but they have lately made investments in more globally oriented projects, like innovative payment provider Twisto, the lifestyle ecommerce platforms Bonami and Biano. Rossum represents for Miton a growing interest in deep technology projects, from an investor with valuable experience in brand-building and scaling successful startups.
The feature photo for this post appeared originally on CzechCrunch
Rossum is a deep-tech startup that joined StartupYard with an understanding of the technology they were developing, but no clear use-case for that technology. The team, a group of PHD candidates in artificial intelligence and machine learning, were exploring applications for machine learning in routine tasks like image categorization.
Through the acceleration process, the Rossum team found opportunities in applying machine learning to corporate accounting, where automation technology has failed to make serious gains in productivity in recent years, due to the sheer complexity and variability of financial documentation. Even invoices alone, of which over 1 billion are sent each day worldwide, are highly resistant to automation due to technical limitations imposed due to historical factors. Rossum began to apply their technology to this problem, and have already made major breakthroughs with the help of a few early key partners.
I sat down this week with Co-Founders Tomas Gogar, and Petr Baudis to talk about Rossum:
Hi Tom, tell us a bit about Rossum. Where did the idea come from?
Tomas Gogar: Hi! The idea for Rossum dates back several years, and it started out as something very different. Our team is a group of PHD candidates who have been studying AI/Machine learning and semantic understanding for a long time.
My Co-Founder, Petr Baudis, is quite well known in AI circles for developing the leading open-source Question Answering AI, something similar to IBM’s own Watson. We actually know that some members of the IBM team periodically test his platform to see how it performs.
Petr also laid the groundwork for Google’s AlphaGo project (the AI that beat a world master in the game Go, many years before it had been predicted that a computer could beat a human). Google cited Petr in their landmark paper in the journal Nature.
I also have published groundbreaking research on the use of neural nets to parse and understand complex semantics in written text (such as semi-structured documents). It was actually this research that led us to the underlying idea behind Rossum.
Like everybody in this field, we’ve been fascinated by how neural networks and machine learning can be applied to massive volumes of data online. We started with a very popular objective, which is recognition of images and their contents, commonly called computer vision.
When we applied to StartupYard, we had the idea of providing a kind of “AI as a Service,” platform that could be used by researchers or data scientists to apply machine learning to their own problems. One of our innovations was being able to train a neural network on very limited data, which made it very useful for this purpose.
In mentoring though, we quickly discovered that there is actually a huge need for AI that understands and can parse text, in many different formats. We had assumed that problem had been solved by OCR (Optical Character Recognition), but in fact automation based on OCR alone has not really improved in effectiveness in many years. It remains a very hit and miss process.
You’re solving what seems like an unlikely problem- processing invoices. How can AI be applied to that problem?
Yes, it seemed unlikely to us as well! But what we found when we met with mentors from the big 4 accounting firms is that, even in 2017, invoice handling is largely still a manual process. This is despite OCR being around for 25 years already, and electronic invoices existing for over 50 years.
That’s mainly because despite all attempts to standardize and streamline invoicing (including newer techniques like using QR codes to allow machines to read them), it remains a problem that is really too big for any one player to solve. No government or company has been able to get enough others to agree on a single standard, and that has meant, effectively, that the complexity of processing invoices has not changed much for decades. It may actually have gotten worse, thanks to the increasing complexity of the products and services for which we receive invoices, and the increase in the total volume of invoices.
So just imagine you are an accounting company. You receive thousands of invoices a day. Maybe hundreds of thousands a year. If every invoice takes your accounting people just one minute to process and confirm, that’s hundreds of hours of work that needs to be done. And that is aside from the possibility that you are audited, and have to check all that work for a second time. The human error rate for invoice processing is low, but mistakes are very expensive. Fraud is surprisingly common as well, as bad actors take advantage of the complexities of billing to trick people into paying for non-existent things.
So we began to realize that one answer to this seemingly intractable problem was AI. The big hurdle for automating invoice processing is that no two invoices are ever exactly the same. Getting a machine to recognize one type of invoice, and correctly pull the right information out of it is non-trivial, but certainly possible with existing technology. The problem is that at any time, companies are receiving invoices from many, many parties, a company can change the way it formats invoices, or an invoice can contain a mistake or can be fraudulent.
So that means that no matter how streamlined your process may be, it still requires human-level judgement to confirm that everything is correct. That creates a bottleneck, because human-level judgement takes a long time to apply. People are very good at semantics, but we are also very slow. And you have the influence of many human factors: being tired, misreading a number, forgetting to double check.
AI that can read and understand an invoice at a human level can also be made to do the same work many, many times faster than a human. So if you train a neural network to be able to recognize and work with invoices it has never seen before (just as a human can do), then you can turn hundreds of hours of tedious work into a matter of a few seconds or minutes of computer processing time.
Who would use this technology? Why hasn’t it been developed already?
Well, neural networks are really just starting to be applied to these problems. One of the reasons this hasn’t been done already is that processing power and computer architecture hasn’t been powerful enough to make it possible. The other side of that equation is the data itself. Until recently, much of the data needed to train a neural network didn’t exist in a form that a network could actually handle. Invoices were on paper, or they were “electronic” and thus in a form that could be handled automatically by a hard-wired program.
In the past decade though, the volume of data that we can apply to machine learning has exploded. That’s why AI and machine learning have suddenly become hot topics again.
The other issue has been that the specific methodologies for training and checking the accuracy of neural networks have been evolving, and are just starting to become really useful for this kind of work. You can just show a million invoices to a neural network, but getting it to focus on what is important is not something you can just ask for. You have to be able to train the algorithms in ways that help it eliminate useless information and focus on what you want it to focus on.
The process is analogous to the way a baby learns. When a baby is born, its ability to sense information around it is very limited. It can’t see or hear very well, and it can’t process what it does see or hear. Slowly it becomes more able to sense information, and it begins to use that new information to construct an understanding of the world around it. Then comes language: the way that a human mind is able to abstract information and complexity, and imagine new things it has never seen before.
If we are to use this analogy, today neural networks are operating mostly blind, and with little understanding of language to create or understand abstract ideas. As we expose them to more information, we also have to teach them a “language” that they can use to extract something useful from that data. Rossum is a part of that language: we are helping neural networks to understand what we want them to do, and why.
The answer to who would use this technology, is, well, everyone! Human level judgement in understanding documents, even of only a very specific type, would save huge amounts of routine work for humans, who can spend that time doing things that are more natural to them. There is nothing natural for a person about spending their days processing invoices. We can learn it, but we never love to do it.
If you consider how many of these kinds of tasks exist, you realize that we spend massive amounts of time doing things that just don’t bring us anything of value, and in fact waste our time and demoralize us. That is the promise of AI in the near future: this ability to free people from having to deal with things that we just don’t get anything out of, but that just have to be done by someone.
What will be your near-term strategy for bringing Rossum’s technology to market?
As mentioned, we are already in contact with representatives of the major accounting firms. They have the biggest immediate need for Rossum, and they also have the data that Rossum needs to be able to train itself and understand invoices it has never seen before.
In the next few years, we want to have a platform that can understand and work with invoices from literally anywhere in the world, many times faster than a human can, with an error-rate lower than any human. Then the work becomes helping these companies to implement the solution, and find ways for Rossum to interact with other systems so that it can help companies streamline their document handling operations.
It is not much use to an accounting firm for an AI to understand all its invoices, if the AI doesn’t also know how to connect with their other systems, and give the outputs they need to take action. That in itself will be a challenge, and one we anticipate will take some time and development. Still, once you have the ability to process invoices with human or above-human accuracy and speed, then there is a huge incentive on the part of companies to integrate that solution into their systems.
We believe that Rossum will be a must-have for accounting firms in the very near future. And once that value has been clearly demonstrated, we can apply the technology to many other processes that are similar in nature. Auditing, analysis and processing of other documents, etc. Rossum could be a backbone for a suite of intelligent applications that takes care of a wide range of tasks that are complex, and repetitive.
We also want to open up Rossum as an online platform, to allow small and medium sized companies to find it and gain the same value from it. Currently, invoice processing services online come at a heavy cost- over a Euro per invoice in many cases. Rossum can do the same work for a tiny fraction of those costs, and it can do it instantaneously, with a very high degree of certainty.
An automatic platform solution is faster, cheaper, and safer for companies that have confidential information they don’t want others to have access to.
How has your experience been at StartupYard? What surprised you?
Petr Baudis: We were rather hesitant about joining StartupYard actually, even though we received several personal recommendations from some of the top alumni founders. We were thinking “hey, we have an office and our own good network of contracts, does StartupYard make sense for us?” and we applied at the last minute and very tentatively.
Oh boy, we were in for a real ride when we did decide to join. Mentoring sessions gave us a much wider scale of perspectives than we could ever gain from our own professional network, and a real and much needed shift of focus from the technical to business. That we expected a little – but it surprised us how eager the core StartupYard team was to help with their experience and feedback, these few people (including you, Lloyd!) really became an important part of Rossum’s story.
And most importantly, StartupYard finally gave us the impulse to really focus on one single thing – we were busy people before, but now we had the reason to finally drop all the side projects for good. We thought the first mentoring month would be the most intense phase, but the pace is only picking up since, and without the “little” pushing by the StartupYard team we would be much more comfortable, getting a good eight hours of sleep a night, but still at the beginning.
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.
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