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
In the past few weeks, the StartupYard Batch 7 startups have been practicing their pitches for DemoDay. Tonight, they’ll be live online and in person at Kino Svetozor, at 18:30 Prague time.
The pitch gets a lot of attention in the startup world, but for good reason. Done well, it is an effective way of communicating your ideas, and also challenging yourself to define the problem (or problems), you’re really solving.
Form and Function
Though you do hear the stray complaint about the emphasis pitches receive in the tech world, I generally find most objections miss the point. Certainly there are bad pitches, and there are ways in which founders and investors are not served well by them.
A startup pitch is very predictable. You introduce the theme in an exciting way, framing the pitch in the broader context. Then you address the “problem” you are solving. Then you present your solution. You then talk about the competitive landscape, and why your competitors (or whatever solution your customers currently have), are silly and outdated, and then you talk about your business and your team, and maybe ask for money.
Pretty simple. And anywhere you go, the format stays the same. Investors from anywhere, going anywhere else, know with some certainty that they will hear those things. The discussion is then about whether they agree with the conclusions the startup is making.
But every round, our startups struggle with staying in those boundaries.
This is at least partly because pitching is quite unlike the other ways in which founders are asked to prove themselves. We expect startups to break rules. To get around the usual processes. To “disrupt,” as the jargon word of the century puts it. But pitching is just not like that.
It’s the Problem, Stupid
For starters, pitching is form over function. It’s a tool of rigid constraint, that is meant to contain “out of the box” thinking inside a narrative box. Consider that at StartupYard, we spend 3 months helping founders to see that the normal rules don’t apply to them. And then we insist that they follow a pretty strict set of rules in order to pitch.
But we do that because the format shouldn’t matter. The only thing that matters is the content. What is the problem being solved? What does that problem mean in a broader sense?
The format is standardized because formal variations on that standard tend to draw more attention than the content of them. Anyone who’s been forced to write a haiku in school will know what I’m referring to. Constraints can force us to be creative in ways that we are not used to. Attention getting can work, in some cases, but it also carries a different set of risks.
I can give you an interesting example: a startup I heard pitch back in 2014 at LeWeb, called JukeDeck. Now what’s interesting about JukeDeck is that they use neural networks to auto-generate music according to a few simple parameters.
Of course, if you had been at that pitch, you wouldn’t have known that, because almost all of the pitch was taken up with the whole team dancing and rapping over music that had been generated using their software. It was certainly entertaining.
I did understand from their pitch that companies would probably use such software to auto-generate background music for corporate videos, and maybe even retail environments. But I learned nothing about the current solutions on the market, the business case for this one, or the actual pain point it was solving.
JukeDeck won LeWeb. And they won TechCrunch Disrupt a year later. If it’s a competition, certainly breaking the rules can help you win. But did that pitch actually help their business? I’m really not sure. The panel’s questions following the pitch were revealing: “why would a business want to use this?” and “how much would a company pay for this software, when stock music is relatively cheap?”
The whole panel was taken up with answering basic questions about the business- questions that could have been answered in the pitch itself. That instantly puts the founders on the defensive. This exciting, crowd-pleasing pitch, which generates buzz, might not actually help people understand what you do. And while the panel asked basic questions about the business, they consequently had no time to ask questions about the underlying technology.
StartupYard startups have won numerous awards for pitching themselves. Not least would be companies like Gjirafa, Neuron Soundware, and Speedifly. I’ve seen our startups pitch outside our program, and heard the feedback they get. Clarity, honesty, strength. These are none of them mind-blowing, but nevertheless more important.
I can tell you one thing about all of these pitches: they are not outwardly exciting. If you were to watch them from behind a pane of glass, you’d have no idea why they win awards.
Instead, they are exciting in the sense of what they talk about, and what new ideas and thinking they illustrate. But they contain no gimmicks. There’s no real flash. The founders, too, are practiced and calm. Everything goes according to plan.
Our thesis at StartupYard, when it comes to pitching, is that your plan better be exciting and game-changing, because trying to spice it up after the fact is putting lipstick on a pig. The drama is not in the performance, but in the story itself. If you hone in on a real, emotional storyline, involving real human beings and real world customer pain, then the pitch doesn’t need bells and whistles: it’s made of gold already.
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.
Decissio is StartupYard’s second portfolio company from Kosovo, following on the success of Gjirafa, from 2014. Dite Gashi is an expert in blockchain and AI technology, who set out with a simple vision: making decisions make sense. Through the course of the SY program, Dite focused more and more on the decision making process for investors, accelerators, and portfolio managers for monetary funds, eventually identifying Decissio’s kick-off product.
I sat down with Dite this week to talk about how he got here, and why he thinks he can change the way investors and startups communicate, and make decisions together.
Hi Dite, tell us a bit about where the idea for Decissio came from? Why decisions?
I have a strong technical background in computer science and I’ve recently graduated with an MBA. I think of myself as a good bridge between technology principles and business-economics. As I was working with blockchain solutions for several years, I could not help but see emerging patterns between these fields.
My feeling is that if you take the state of any existing entity today, whether it is a business, organization, country or even an individual, their current state is defined by decisions made previously. If you look at it from this perspective, the decision making process and history can be represented as a ledger.
A topic that fascinated me since I can remember is the distinction between success and failure. How come some companies manage to grow so quickly and maintain their success? What about others, who seem to have everything in order – just to see t all crash down like a sand castle? How can we quantify these?
The history of mankind is man trying to master nature. As humans we always have been obsessed with avoiding dangers and seizing opportunities by making the right decisions. At its core that is what defines our survival and growth.
We have done that in several ways and the compounded knowledge that we have has served us very well. With big data and smart algorithms being introduced, our progress will achieve exponential growth. We want to be a part of the revolution in a narrow scale starting with investment decisions.
You joined StartupYard as a “napkin startup,” meaning you had no code written, and no finalized product idea. Now you’ve identified a market, and you’re building a product. How did you get from there to here?
As Peter Thiel says in his book, now the startup classic “From Zero to One” getting started from just an idea to actual customers is the hardest part. I am glad StartupYard was there to help us make the leap.
The evolution of Decissio has been very much a child of real-life experience. I have personally spoken to more than 20 people involved in venture capital, and Decissio as a concept was born out of these deep discussions.
When I joined StartupYard, I knew I wanted to use blockchain technology and machine learning, to help decision making processes. What I didn’t know yet was just how profound the problems with investment decision making really are. And one of the profoundest problems in investment decision making is that of information: what is relevant information and what isn’t? How do we understand our own inherent biases about information?
Human beings are good at judging other human beings, for example. We have feelings about people, and we are great at certain kinds of pattern recognition. We are good at recognizing things that are missing; things that are just sort of off. We know when we don’t like something or someone, even if we don’t know why. So many early-stage investors rightly say that the bulk of their decision making process is about people.
But that’s just a part of the story. The truth is that information bias can change the way we feel as well. Someone who we wouldn’t normally trust can trick us or mislead us by giving us indications that they are doing better than they are. We can call this “fake traction,” or “fast talking,” and it’s something investors are very familiar with. Investor, particularly in very risky and speculative industries, have to be constantly aware that they can be led down the garden path if they aren’t very aware of what has actually been proven, and what is just talk.
And that understanding slowly evolved into Decissio, which became a platform where real and verifiable information can be gathered, and more importantly, contextualized so that an investor can easily and clearly see the real situation of a startup or a company they want to invest in, or which they have in their portfolio, and use that intelligence to augment their own decision making.
What are some of the key pain points for VC investors, or accelerators, or others, and how do you solve them?
As noted above, a key point is data. There is usually not a lack of data, but a problem of scope, timeliness, accuracy, and precision. Many startup investors that I spoke with shared experiences of being unable to make decisions about investments because the data they were seeing was not clear enough, not presented in the right way, or was too broad or too specific to base decisions on.
And that problem doesn’t stop when a company gets an investment. Plenty of startup investors also complained of how difficult it really is to know the status of a startup in their portfolio, relying on the founders themselves to come forward with that info. The founders first of all pick the data they want to share themselves, and sometimes it isn’t the right data, or the data they provide isn’t helpful to the investor, or doesn’t provide the investor a chance to intervene and help the startup.
The basis of a good relationship has to be one of understanding. So Decissio is going to solve that problem by bringing a startup’s relevant data directly to the investor in a very easy to use dashboard, that provides a range of datapoints from the APIs of common platforms, like Google Analytics, MixPanel, Intercom, even Slack. The idea is to give the investor a very clear heads-up on exactly what the current status of the company is, including even clips about them from the news and social media, backed up with real live data that can’t be faked, or “massaged” or sugar coated.
On the other hand, all the great data in the world isn’t very useful if you can’t contextualize it and use it to make better choices. That’s why Decissio will be more than just a data platform- it will be a decision engine that helps investors to get actionable advice about investment decisions. We will do this by learning from the way investors use the platform, and, over time, by widening our understanding of data trends and their relationships to the industries in which our investors work.
Over time, we will be able to build an engine that deeply understands the trends in the market, and provides actionable advice to investors, and eventually to startups as well- helping them to calibrate their expectations for investors based on the real situation.
Without revealing any secrets, what was the thing that surprised you most, after talking to over 20 VC investors about their decision making processes?
The fact that many of them aren’t really as organized or as systematic as you would expect, given the power they have and the amount of money they manage. In the end, we are all just people- and investors are just like everyone else (although they may not see themselves that way).
Most of them do not have strong data solutions, and I was astonished by how much of their decisions were based on gut feelings. That serves them well and our goal is not to suplant their judgement – at the end of the day they still make the decisions. We want to enhance their gut feelings with data.
This is sort of like using AI for medical diagnostics. The AI is very good at recognizing patterns in data. But just in data. An AI can’t tell you if someone is lying by the look on their face (at least not yet), and an AI doesn’t have a lifetime of connected experiences that inform its judgement about people, the way a person obviously does.
So we don’t seek to change that. But we do seek to help in situations where judgement is clouded by our misunderstanding of data.
I will give you an example: in the 1990s, studies were conducted by researchers at Cook County Hospital in Chicago about triage decisions in cases of suspected heart attack. What the studies found was that the questions that physicians asked patients they suspected of having had a heart attack were actually making them less likely to give a proper diagnosis. For instance, asking a patient whether they have had a previous heart attack, according to the data, does not help a physician to diagnose the patient. And yet physicians always asked, and that changed their standard of care.
The result was a decision chart that was implemented around the world, in which doctors were instructed to only ask specific questions that helped them make a determination of the best care for the patient. The result was a rise in the response time, and the success rate, of treating heart attacks.
The physicians, at the end of the day, still need their judgement to understand how to treat the patient, how to talk to the patient, how to assess them, and what not to do. But it turned out that in this specific case, having more data didn’t mean making better decisions. Not all data is good data. So having AI as a backup for human decision making processes is very important in a data-filled world.
You talk in your pitch about “decision fatigue.” Can you explain why this is such a big problem, and why investors should be particularly concerned about it?
Our minds have a limited capability, and there is simply no way around that. The only way we can go about it is to acknowledge our limitations and let advanced tools help us when possible.
We make around 35,000 decisions a day. And each of those tiny choices is a mental effort. It can tire us and stress us out. Mistakes earlier in a single day can affect your thinking for the rest of the day. A bad experience 10 years ago can negatively affect your decision making today. We can all relate to that experience.You make around 35,000 decisions a day. No wonder you're tired - Sy2016/2 Startup:… Click To Tweet
Decision making stamina is of limited capability so it is important to use it wisely. Several key decision makers of our times including Obama, Zuckerberg and Steve Jobs handled this by eliminating small decisions – such as what should I wear today, by having the same type of outfits ready for them daily. As trivial as that sounds, that ensures them one decision less per day, which in annual terms compounds fairly well. Not feeling the stress of wondering if you’re dressed appropriately can be a huge relief.
Investors are prone to decision fatigue since the nature of their work involves large sets of daily decisions. In addition to that, all their investment decisions are important. Deal flow management, term sheet structuring, hiring and firing can all take a toll on the day of a investor. As these decisions may or may not break the firm, if they make bad investment decisions surely places them in a dangerous place. And the thing is, having a bad morning can cause you to make a very expensive mistake later in the day. It’s very random.
So we help them by eliminating some of that burden- at least the part that they have the least control over to begin with.
How will investors use Decissio to make better decisions?
In an investment decision there are inputs that come from the investment specification. A VC receives startup data, due diligence, market research and financials as inputs. Then there are the key performance indicators that are results coming from the operation of the company.
Decissio correlates successful outcomes with positive inputs to give investors hindsight advantage, before they decide to go with an investment. We use decision trees and self-adjusting regression algorithms to connect these factors in a way that was not possible before. That means an investor has the benefit of experiences they don’t personally have- Decissio will be able to recognize trends no human will notice.
Before the investment is made, Decissio performs a pre-screen where it looks at ratios of data provided and whether they make sense. It cannot always determine if something is wrong or right, instead it raises red flags for the investor team to go and analyze thoroughly.
Our algorithms learn from hundred of running investments therefore the network effect that they benefit from is massive. We place that massive power back in the hands of individual firms.
What about startups. Many mentors cautioned you that startups will be disinclined to use Decissio, because it seems too intrusive. What will change their minds?
Artificial Intelligence, blockchain and data driven companies are revolutionizing how business is done across industries. The revolution is already here and we have to get investors to join it. Not every investment firm has the funds to build their own data teams and solutions. We have built an easy to onboard tool that’s intuitive to use. Using Decissio saves far more time and effort, not to mention money, than the efforts it requires to onboard a company in the platform.
That being said, our belief is that startups will eventually welcome this kind of process, because it will be much more transparent, fair, and informative for them. They won’t be wasting their own time chasing investors who aren’t interested, and they’ll be able to see clearly what investors expect, and what’s wrong with their own numbers. To us, that’s a win-win situation.
What are your goals for growth in the next year or so?
We are moving in full gear towards the first MVP which will be ready in early spring for alpha testers. We want to expand our reach to 100 venture capital and private equity firms by the end of 2017. We are currently working on this and we value our connections highly. Our goal is to learn, grow and serve our investor needs the best possible way.
The MVP should be an instantly useful and actionable set of data and insights that an investor can refer to on a daily or hourly basis, that not only assures they are informed, but also keeps them on their toes, and questioning their underlying assumptions. That’s the initial success condition we want to meet.
How about long term? Where do you see Decissio as a company in 5 years?
Our goal is to scale our technology to serve hundred of thousands of investors and decision makers globally. As soon as we have a stable product in terms of revenue, we are planning to expand our reach into other industries. Learnings from early stage investing, where the data is quite loose and fluid, will help us in tackling more staid industries, in which the data are more stable and predictable.
Our goal is to saturate all fields where collective decision making occurs. Being a part of a startup you always can plan, however the environment is constantly shifting so one needs to be flexible. What we are doing within is creating scalable process and methods from the get go, that allow us to scale quickly
You’re our second startup from Kosovo. Can you talk a bit about the unique experience of starting a company in that region? What are your unique advantages and challenges?
I’m proudly the second one from many to come.
We were lucky to have [StartupYard 2014 startup] Gjirafa’s success coming before us, they really paved the way for us and other startups to start working with StartupYard. This is my second started startup in the region and I have learned so much during the process. If we are dealing with online solutions then it becomes easier because you can market your products anywhere.
On top of that the government does not interfere, so you can enjoy not having to deal with bureaucracy. Kosovo has one of the youngest populations in the European continent. However, our crumbling education system does not produce candidates that are ready to join the job market, leaving professional training to companies. In a sense that might sound like a disadvantage but we have a high employee retention rate, which is important. Young people in Kosovo highly value their work relationships.
Last but not least the standard of living is lower than in most of Europe, therefore if carefully managed, companies operating there can become cost leaders.
How has your experience with mentorship been so far? What surprised you? Which mentors had the biggest impact?
Starting off the acceleration program I was quite stubborn and persistent in my ideas, which is a trait a large amount of startup founders have. In order to go anywhere worthy, one must be persistent.
However mentoring session had a very humbling effect on me. We had the chance to speak with many well known professionals who are kings on their fields of work. Including several high level executives that manage important decisions and millions of euros. That shifted my perspective from a “I know a lot” to “listen and learn” attitude and it served me very well. I became very equipped with knowledge I would have no other way managed to get in such a short period of time.
In addition to sharing advice, mentors have very important professional networks that if you work with them, they would be happy to open up for you. Mentoring sessions in StartupYard I would say introduce an upcoming entrepreneur to the crème de la crème of Prague.
Everyone here really wants you to succeed and of course you never want to let them down.
How has your experience been with acceleration at StartupYard? Did it match your expectations?
I only heard great things about StartupYard before coming here and initially I was thinking it’s probably okay but most of it is going to end up being hype.
I was wrong, StartupYard managed to surpass all my expectations. The teams are so diverse, the staff itself is super knowledgeable and helpful. There is a no-bullshit atmosphere here and everyone is really pushing the hardest that they can. The clock on the wall has a sticker that says “Need more time? – Sleep faster!” and on the first week here I thought “That’s funny” – now I realize that it’s so true. I haven’t found a way to sleep faster yet though.
Google is betting on beacons. They aren’t the first, but this time it’s with a twist. It initially struck us as odd, that though beacons as a concept have been around for a long time (Apple launched iBeacon in 2013, and we even accelerated an iBeacon startup back then), we had very rarely seen any really practical use case for them. Apple treated iBeacon as a bit of a novelty, failing to establish an attractive platform for developers, or to educate the public on how beacons could be used.
That’s changing– beacon technology may finally be getting its day in the sun. The proliferation of bluetooth enabled smartphones, and the rapidly dropping cost of the physical hardware mean that sooner rather than later, beacons will be an integral part of our experience of the world around us. Google calls it the “Physical Web,” and evangelists inside Google see it as tomorrow’s answer to the disconnect between in-person activities, like shopping, visiting a museum, or taking public transport (or parking… yes even parking), and our lives on the web.
Meet Beeem, the StartupYard 2017 startup that is looking to bring the Physical web to your smartphone. They’re doing it with a “WordPress for the physical web,” a platform that allows pre-configured beacons to broadcast the URL of a microsite which can be launched by anyone nearby using Android or Google Chrome, on any smart device. Beeem helps businesses, event spaces, and others to create web-apps and pages that connect their in-person customers with their web presence, allowing for smart enabled storefronts, content, interactive display ads, customer retargeting online via Facebook and Google, and even payment systems.
With Beeem, a restaurant could easily allow customers to order electronically, without having to wait in line, or a museum could do away with expensive audio-guides in favor of a web-app. An electronics store could allow customers to order and pay from the aisle, rather than lugging their purchases to the counter, and cities could eliminate pay boxes for public transport or parking. To Beeem, the Physical Web means is a website for every thing- the opportunity to create a web presence for virtually any physical object or location, with a central content management system that allows a beacon’s owner to easily configure the web experience it provides.
I caught up with Ferenc Brachmann, CEO and Co-Founder of Beeem, to talk about the Physical Web, and his young company:
Hi Ferenc, how did you come up with the idea for Beeem?
I think this story is going to sound very familiar to anyone who’s ever started a startup, really. I was at a Metallica concert in 2014. I’m really into thrash metal music. Anyway, at the concert, they had a voting system, where fans could vote on their favorite songs, and the band would play them. Pretty cool.
But here’s the thing: they asked fans to vote via SMS. In 2014. To this day I remember seeing the message to vote in an SMS, and looking down at my pimped up new phone with a quad core CPU and 1 GB of memory, and realizing there was something very wrong here. And that got me thinking a bit.
Of course, the concept of using the internet to interact live and in person was not new at all. But the fact is that there were essentially two approaches, neither of which work incredibly well. Either you could create your own app or website, and populate it with interactive widgets and forms (and face all the challenges of compatibility and scalability that entails), or you could use a platform designed specifically for the kind of interactions you want, and get everyone to use that.
Metallica could do an app, but then everyone would have to download it and it would still have to connect with something on their back end. Using SMS was the platform solution. It could easily have been some other web service, like Google forms, or one of a hundred voting platforms. It works, kind of, but it leaves a lot to be desired.
For starters, Metallica can’t turn around and leverage all those people who voted via SMS to come to their next show or buy an album. What can they do? Spam them with SMS messages? That won’t work, and it will be really expensive. They don’t own the channel they’re using in that case. The telco owns SMS, and charges a lot to use it despite it being decades old. They’re a gatekeeper.
This issue bothered me for a while, until I went on a trip to South Korea, and had an eye-opening experience. In Korea, mobile internet penetration is basically 100%. It’s everywhere, and it’s fast: you can get a strong connection even in the middle of the Busan Fireworks Festival, which means that there are about 1 Million other people around you in a very small harbour also connected to and using the same networks.
And yet there too, companies like Apple, Google, and Facebook function as the “gatekeepers,” just like the telcos formerly did in the days of SMS. And like the telcos, these big tech companies aren’t necessarily the best medium through which a live venue or a business could communicate with its customers. Their value propositions are all different, and none are a good fit.
WIth Apple, you need an app. That’s a lot of time to download, install, and log-in, not to mention the limited storage space. Google has the same problem with Android apps, or even with search, which requires you to search for the business you’re looking for, only to end up on their website, which is unlikely to work well for communicating with a business then and there.
Facebook is slightly better, especially with their focus on messaging and pages but even there, you don’t have real control over your own audience as a business. And people still ultimately have to actively find you there to connect to you.
So that’s a long story to say: Beeem is the answer to all that. It’s a way for businesses to connect directly with their customers, and to have complete control, without having to rely on any one of these other platforms where they can’t be in control. And it solves the problem of scalability and repeatability that a lot of businesses have, which is: maintaining a web presence takes a big commitment of resources to stay up to date. With Beeem, that part is all taken care of – you’re left just to worry about what you show your customers, and not about which platform to use, or whether your technology is up to date. The future of apps is no apps. No downloading. No waiting. The Physical Web is there when you need it.
How is your team uniquely suited to taking on the status quo?
After my trip to Korea, I came home and shared my frustration with my friends Norbert and Peter. Norbert is the owner of a software company with a ton of experience, and Peter is the brains behind the biggest e-learning platform in Hungary. I knew they were just the right guys for the job, so we formed a team, and we’ve been working together now for over a year.
Our team is ideal, I think, because we all really love thinking about these very complex problems of how people use technology, how businesses use it, and how the system in general fails to really reconcile these two sides. Once you start looking at some of the behaviors businesses and people have adopted to communicate, it’s really bizarre.
For example, you walk into a store -let’s say an electronics store- and you find an item you think might be the right thing for you. Does this part sound familiar? You probably pull out your phone, a computer more powerful than the sum of all the world’s processing power combined 20 years ago, and you… Google it. You Google it. And Google may or may not tell you what you need to know about the product. Maybe it even offers you a better price, and it certainly shows you ads from the store’s own competitors.
Do you think that makes sense to the store, which is bearing the cost of stocking the product, that its customers can walk in, Google something, and order it for cheaper somewhere else? There have even been movements to ban use of the Amazon barcode reader in stores in the US because of this problem. People would “shop” in stores that they never bought anything from, and Amazon would benefit from the capital costs that the in-person retailer bore to keep the store open.
That’s ultimately bad for the consumer and for the business, because in the end, the platform is going to win that battle. Cheaper is cheaper. Our team is genuinely committed to reversing that problem, and giving in-person retail a new and unbeatable advantage: which is a direct line to a customer who is interested in something right here, right now.
We need in-person retail. The social aspect of shopping and dining will always be important. Perhaps now more so than ever. But these businesses need new tools to fight the battle with the big platforms. That’s what Beeem is all about.
Where do you see the biggest impact for this technology in the next 1-3 years?
I haven’t touched much on what Beeem actually is, so I’ll talk a bit about that.
Beeem is like wordpress for the physical web. The physical web is something that will continue to grow in importance in the coming years, as physical beacons (using Bluetooth), become ubiquitous and cheap. What these beacons do (at least right now), is very simple. They can broadcast a very basic URL. A physical web beacon is detectable from any smart device with Bluetooth, and will continue to be integrated deeper into our devices both on iOS and Android work.
What we do, is help businesses to leverage that technology, by sending anyone within range of that beacon to a web-app that is set up under the complete control of that business. We provide the content management system that supports that web app, and provides the business with easy drag-and-drop tools to build the app that suits their business most. We can support virtually anything a smartphone can do, including easy stuff like messaging, video and audio, and even payments, but also complex things, like live interactive programs and even games that combine the real world and the web.
Ultimately, even beacons themselves will not be that important. Anything electronic will be beacon. A phone, a watch, and soon even a clothing tag can broadcast it’s own webpage, broadcasting useful content and services tied to that object specifically. Soon, you’ll be able to “search” the physical web that is around you, and interact directly with every day objects using your phone, or your watch, or maybe VR glasses, or something else entirely.
That’s why Beeem is an App for Every Thing. Because in the Internet of Things, nothing is very useful unless you can easily and seamlessly connect to it and tell it what to do, or ask it to help you. Beeem allows a business or a venue, or anyone, to target their messaging directly to individuals around them, with customized services, made only for them, based on past shopping habits, or location, or many other factors. It could be kind of like the advertising seen in the in movie Minority Report [starring Tom Cruise].
Do you worry about what this means for privacy?
I don’t. But not because I’m not concerned about privacy or about issues of space and invasive advertising.
The truth is that visions like that in Minority Report tend to get the general idea (ads that talk to you as an individual), but they get the execution all wrong. The truth is that people’s standards shift over time, and technology and business have a way of fitting into new standards as they evolve. For example, I very much doubt that businesses would be successful if they shouted at their customers in the way that ads do in many dystopian future films. In reality, we find that as advertising becomes more targeted, it should become more useful, and the need to be aggressive or “salesy” should actually diminish.
Annoying marketing is also inefficient marketing. It spoils the customer experience rather than giving the customer something they really want. The physical web allows online interaction to be much more efficient, and much more relevant to the customer. Nothing you don’t need- everything you do.
Ultimately, the best form of marketing and advertising is the kind you don’t even consider marketing and advertising, because it gives you exactly what you need, when you need it, and never offers you things you don’t want. We don’t consider the signage inside a store to be advertising, because we choose to see it. The same will be true of the physical web: we will be asking for this information, just as when you walk into a restaurant you are, in a way “asking” to be seated and served.
Let’s talk about some of the use cases right now. What can people do with the physical web and Beeem today?
As of today, you can create a fully customized web-app that your customers can access directly from the notification screen on their Android phones, or via Google Chrome for iOS. We expect that support for the physical web will soon expand in all Chromium browsers (Chrome, Firefox, Opera) and we see hints that other browser vendors will soon join the list.
Businesses, venues, or public spaces like museums and libraries can today create pages for visitors, gather instant feedback or field questions, share content like videos and images, and also receive content from visitors, such as visitor photos or other media.
We will soon be adding e-commerce to this mix, meaning that business can direct customers, with only a single tap on their phones, directly to the portal to purchase items from the store. This could be applied in all manner of environments, from sports events to public transportation. No searching, no downloading- just tap and go.
Those are a lot of diverse and complex areas. Where do you want to focus first, and why?
We’ve been focusing on live events like trade shows, sports, and conferences, because that’s often where the early tech adopters are. There are a lot of people with open minds, and strong incentives to be unique in their fields, so we are targeting hobbyists and geeks to really get a sense of how our customers will use the platform.
In order to scale, we’ll need eventually to shift focus to businesses and use-cases that will be easier to apply to a huge number of customers. Things like retail-location web-apps that let people buy on their phones instead of waiting in line, or restaurant apps that let people order on the go. To accomplish that, we are looking at a mix of online marketing, and cooperating with resellers who already provide payment, IT, or web services to these kinds of customers.
What do you see as not really working in the current status quo?
Well, the status quo certainly works for the gatekeepers. Their ad revenues are built on the lack of penetration that businesses have among their target customers. But ultimately, it just doesn’t make sense for consumers to be directed to 3rd party platforms that aren’t controlled by the companies they are actually doing business with.
There will soon be 5 billion smartphones in the world. Having a handful of platforms funneling all the traffic and keeping all the advertising revenue just isn’t right, or fair. Facebook and Google, as they grow in dominance, can demand more control over what businesses do on their platform, when it should be the opposite.
That forces businesses to play the game of Google or Facebook, instead of focusing on what they do best. It encourages them to be more like others, and less like themselves. And that ultimately costs consumers money. If you can spend your marketing budget more efficiently, and target it at the right people, then you can provide a better service at a lower cost.
We’ve seen this all before in the history of the web. Apple and Facebook could be seen as some later version of Prodigy or AOL – closed systems that wall off huge online communities. Facebook wants people to spend time on Facebook. Not go somewhere else. They want to scoop up all the customers, and control the whole experience of the web in order to make money, just like Prodigy and AOL wanted to before Netscape. The World Wide Web, at least for a while, stopped companies like AOL from doing that. It offered a variety and authenticity a single company couldn’t.
Mobile has given companies like Facebook and Google, and especially Apple, a second shot at end-to-end control of our online experience. They want us to stay in their worlds, and not create anything outside of them. But hey, that’s just not right. I believe it isn’t the way forward for business, or a healthy open society.
Where do you see Beeem in 5 years time?
Powering this revolution of direct, instant P2P communication between everyone and every thing. We aim to be significant in 5 years time, hopefully working towards becoming a global infrastructure provider.
How has your experience with mentorship at StartupYard been? What surprised you?
The mentors have truly been amazing. I personally never thought that you can put such a strong team of mentors together in the CEE region. Out of the close to 70 meetings we had, we had over a dozen fantastic meetings.
A lot of names come to mind but what has really blew my mind is that several mentors have reached out to us afterwards to recommend us or to get more involved in what we’re doing. That really showed me they take this seriously.
I’d like to specifically mention [Former SY Mentor in Residence] Philip Staehelin, [Longtime SY mentor] Tomas Riha, [Springtide Ventures] Karel Tusek and [Axa Insurance CFO] Sebastien Guidoni who all went out of their way and did things that we really did not expect from a mentor.
How has it been for you at StartupYard?
We got accepted to another accelerator in the region, but we decided not to go. We met Lloyd Waldo in Vienna at the Pioneers festival. We had a really good talk and he kind of sold me on the idea that the program here is good. Later I met Cedric Maloux in Budapest and at that time I realized that the program is probably going to be very good for us. Especially because all of Beeem’s co-founders own companies and know business, it’s just that we’ve never owned a startup before.
The startup mindset is all about scaling. It’s about repeating something millions of times. We wanted to get out of our comfort zone, out of our country where we know the market. Even though I had high expectations I think that this program is just spot on. The depth of mentors here is surpassing even my really high expectations. I think Lloyd and Cedric and the gang have their act together. They know how to put you in difficult spots, how to challenge you without you ever knowing about it.
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