SY Alum Decissio Uses AI to Accurately Predict StartupYard Investments

You may remember Decissio, a Batch 7 StartupYard alum that has been working on the “Jarvis for Investment Decision Making.” Earlier this year, the company announced its kick-off product, an intelligent dashboard for VC investors and Accelerators to evaluate and monitor companies they invest in.

Decissio aims to go beyond a typical investment dashboard by combining up-to-date company data with complex big-data based probability models and machine learning algorithms, helping investors to continuously evaluate their investment decisions.

As Decissio and founder Dite Gashi continues to gather data and build the company’s flagship SaaS product, they have focused on piloting their approach with small controlled experiments.

One such pilot has been in partnership with StartupYard. Decissio’s Mission: to process all of StartupYard’s applications for Batch 8, our latest batch starting next week, and deliver predictions on their success based on a variety of factors, including written applications, founder profiles, founder/market fit, and the current state of the company.  

Dite Gashi

Dite Gashi: Founder and CEO at Decissio

The numbers are in on this pilot, and they’re very promising. We’re not ready to stop reading applications or doing our own research just yet, but we’re now confident that Decissio can be a big part of making our application process better, fairer, and more efficient.

The following case-study is a co-production of Decissio and StartupYard, written by Dite Gashi, and Lloyd Waldo. A more detailed write up and analysis will appear shortly after publication at Decissio.com. For more info on the technology and related work, please visit Decissio.com.

Warning: This post is long and contains big words. Skip to the bottom for a bulleted Tl;Dr 

Good Small Decisions = Big Positive Outcomes

The StartupYard application process doesn’t happen all at once. It involves a long series of smaller decisions. Does a startup have a unique idea? Does it fit into our mentor group and experience? Do the founders have enough experience? Is there strong competition in the market?

Some decisions are even more granular: did the founder answer questions thoroughly and clearly? Were they responsive in detail?

Small details often reveal big trends. But a human mind isn’t set up to think in that direction. We aren’t programmed to carefully add up small decisions to make big ones. Enter Decissio, whose mission was to apply a machine-learning approach to small decisions we make in the application process, not to override the judgement and experience of our evaluators, but rather to augment it with important insights.

StartupYard Alum Decissio.com uses #AI to accurately predict future StartupYard startup… Click To Tweet

The Framework

An application to an accelerator consists of a relatively small data set. We have a written application, founder profiles (on LinkedIn), sometimes a website, and whatever has been written about the company online.

Rarely do we have hard financial data on the companies, in some cases because there is no company in existence, and so the founding team has no financial data to look at. Nor do we have much access to the IP teams are working on. We have to rely on what founders say, and what they have done in the past.

But a bunch of small data sets together make up a bigger data set. Decissio examined over 1300 previous applications to StartupYard, along with the rankings our evaluation committee has generated, and used that data as a benchmark for incoming applications.

They found a number of statistically significant trends in that data. Startups that were successful as applicants to StartupYard could be ranked point-by-point, according to the following framework:

  • A Completeness Score: how thoroughly the application is filled in, and with how much quality information.
  • Effort Score: The quality of the writing in the application, particularly the responsiveness of answers, and the scope and variety of detail provided.
  • Relatedness Score: how closely a founder’s profile and experience matches the content of the application
  • Founder Linkedin Score: The completeness and quality of a founder’s LinkedIn profile
  • Media Mentions: The number, quality, and sources of mentions of the company or product online, along with sentiment analysis
  • Money/Work/Revenue Generated: The ratio of previous investments and time spent on the project to real revenues (if any).
  • Spell Check

Believe it or not, Spell Check is powerfully predictive of application quality. Note to founders: always use Spell Check.

The Analysis

This is where the historical data from previous StartupYard applications comes in. While it’s not very useful to directly compare older applications to newer ones, because the topics and ideas in them are often so different, it is useful to weight the importance of the different factors in the framework according to their impact on previous decisions.

Furthermore, the final analysis includes proprietary algorithms by Decissio that can dynamically weight the outcomes for individual teams, based on cross-referencing between different data sets. For example: Decissio’s AI can adjust its expectations for the Effort Score, if the founders are experienced in marketing and sales, or have no such experience. Thus each team is examined according to its own merits, and not an evaluator’s less informed expectations.

As “calibration,” or maintaining consistency and fairness of scoring across a large number of applications is a significant problem with humans, Decissio can re-calibrate an evaluator’s judgement to keep them from penalizing teams for the wrong reasons. As the standardized testing field has long known, human scoring can be so inconsistent that a significant amount of scoring time (even up to half) must be devoted to calibration in some cases.

Since our evaluations involve multiple rounds with a Pass/Fail outcome, each examining more and more detailed information, highly predictive models can be built for an application that will make it through round 1. A less predictive but still strong model can be built for round 2, and a much less accurate, but still useful model can be built for round 3, and so on.

The chart below shows overall predictiveness of the approach over multiple rounds. StartupYard uses a “first past the post” system of ranking, where the ranking cutoff for each round is smaller. This means that in round one, 70-80% of applicants are rejected. In round two, just over 50% of the remaining applicants are rejected, and in round 3 (which are day-long in person interviews), only 20-30% are rejected.

Decissio False Negatives

None of Decissio’s bottom-ranked 63 startups were ultimately selected, meaning that virtually all of the first round of evaluations could be handed over to the AI, leaving a much smaller pool of applicants to evaluate, and allowing the human evaluators to use a much lower cutoff, in a smaller, better initial pool. In this scenario, only 20% of human evaluated startups would need to be rejected in the first round.

We would expect false negatives to rise, as Decissio gets only one pass at the data, and with each round, human evaluators gather more data, which causes their behavior to diverge from the model.

For example, if use of Spell Check is 90% predictive of the Pass/Fail rate for round 1, it may be only slightly predictive of the success rate of round 2, and by round 3, it may lose its predictive power altogether. By the time an application involves a detailed look at a founder’s CV, and personal interviews with that person, other factors can arise that vastly outweigh any minor inattention to detail, like spelling.

Or the predictiveness curve can go in the other direction as well, with certain data only gaining predictive power in later rounds. Media mentions may have a low predictive power in the earlier rounds, and become more powerful later on. This can be because a company with a low early round score for Relatedness or very high Money/Work/Revenue ratios, can have many mentions in the media, but also fatal problems in their business, team, or technology. Thus, hype is not strongly predictive in Round 1, but by Round 3, it becomes a major asset to an applicant. Once all other factors are examined, media exposure becomes an affirmation of market fit, demand, or interest.

How Well Does This Work?

Decissio’s Success rate in the first round of applications (the on-paper evaluations), was 73%, far exceeding random chance. The accuracy dropped as expected in subsequent rounds where evaluations focused on personal interviews, from 50% in the 2nd round, to 20% in the final round. Still, this means that exactly half the time, a startup that passed the first interview with our selection committee was predicted to do so by Decissio, based only on their written application and profile.

There are two ways in which this kind of analysis can be useful. Either it can be used to identify applications that have a high likelihood of success, or it can be used to filter out those with the lowest likelihood of success.

Decissio Picked the Top 2 Ranking Finalists

We don’t have enough data to be able to confidently say that an application will definitely fail. However, on the opposite side of the scale, the results from Decissio’s analysis did correctly identify StartupYard’s two highest human-ranked finalists, and placed both in its own independent top ten prediction.

Decissio Picked the 100 lowest-rated applications with 89% Accuracy.

Still, the most immediate benefit of Decissio’s approach is in the earliest rounds, where pass/fail decisions are by design based on less human-focused information than the pass/fail decisions in later rounds.

This theory holds up with Decissio’s results: their bottom 100 applicants in this pool of applications (out of around 130), was 89% accurate, meaning that only 11% of the time, we determined a startup to be worth advancing, while Decissio did not. Clearly, in terms of identifying a lack of potential, Decissio’s approach is already very effective.  

Further mining of the available data could produce a much more precise prediction. For example, by analysing co-founder and founder/investor fit according to the work histories and digital footprints of both can theoretically yield very reliable predictions of compatibility, which in turn raises the chances of success or failure for a startup.

These factors would require a different kind of data to solve; a kind of data we don’t collect systematically right now. But this kind of approach, which treats people as nodes in a system that has its own features beyond those of individuals, has been deeply developed already, particularly on the level of enterprise management consulting involving things like the Meyers-Briggs Type Indicator Test.

It may prove true in the future that a set of personality tests of some kind are more predictive of success in a particular accelerator program or industry, than the content of an application, though we don’t know what that test would look like, or how it would be used.

SY Alum Decissio.com predicts first round StartupYard application decisions with 89% accuracy,… Click To Tweet

 

Potential Applications:

Time Saving

Decissio was able to predict with strong accuracy (73%), the likelihood that a startup would make it through the first round. This means that evaluator’s mental resources can be focused more on rounds in which more human-level data is being examined, particularly personal interviews and meetings.

An evaluator can spend relatively less time making early-round decisions, because Decissio can compare cursory evaluator consensus to its own scores, and “call out” the circumstances in which these do not match for further study. There is less of a chance that a good application will be “overlooked” in this way– a constant fear among startup investors dealing with many applications.

Bias Reduction

While a human with experience can “skim” an application and be able to tell it isn’t strong, that subjective evaluation is highly prone to error and internal biases. Very poor spelling could cause a human evaluator to give up on an application, whereas an algorithm might see past this issue and find more value in the startup than a person would look for.

This process could also serve as a check against more latent biases, such as gender, age, nationality, and sexual orientation. While it’s difficult for a human to differentiate between their instinctive reactions to people based on conditioning, and their objective evaluations of people in a professional context, an algorithm can demonstrate more consistency in that regard. Biases can’t be eliminated even this way, but they can be better controlled.

Thus, Decissio can be a check against the human decision making process, enhancing it without replacing it.

Fighting the “Best Horse” Problem

Decissio’s approach can also serve to fight the “best horse” problem, whereby a candidate with a strong outward appearance can advance well into the selection process without revealing sometimes severe deficiencies.

The best horse problem is one of reinforced selection bias. Imagine you have 10 horses, and you send them all running around a track. Then judging by the outcome of the test, you give special care and attention to the fastest horse, believing that it above the others has greater potential as a champion.

In this way we sometimes pick winners for all the wrong reasons. The horse to finish first can finish first for a number of reasons not having to do with potential as a racehorse. Cheating for example, or luck. Likewise, the last horse around the track can be the one with the most future potential.

In our application process, a very strong written application or interview performance can mask a basic weakness in the founding team’s experience or ability. It’s only much later that these weaknesses reveal themselves in a lack of tangible results from the company.

Startups can and do advance very far in accelerator programs while still lacking the core abilities and disposition needed to thrive. It can take a long time to recognize a fraud or a fish out of water.

Creating More Useful Feedback

Another thing this big data approach can solve is the information problem. What happens frequently with accelerator applications, as we suspect happens in many fields, is that successful written applications contain a near-perfect mix of description and data. Something like the “golden ratio” often described in mathematical analyses of artworks and natural proportionality.

The human mind likes a certain level of balance in the information it receives. When a person writes, they tend to favor either information or analysis, but only experienced writers know how to mix the two into pleasing and easy to read narratives. It’s a problem even good writers frequently struggle with. 

Too much writing about ideas, and the application seems too “light.” Too much data, and it seems too dense or too technical. In formal writing analysis, this formula is often used to describe balance between facts and ideas, where the value a is descriptive and creative writing, while b is supporting data and factual information. Those familiar with the classic “5 paragraph essay” often taught in schools, will recall the same proportionality. About 3 parts of persuasive writing, for every 1 part of factual basis. 

This type of training is not universal even among professionals, which sets up an arbitrary test of writing skill that may not be as relevant to the outcome as we tend to believe. If our job is to train people how to be better entrepreneurs, then we fail at that mission from the beginning if we can’t differentiate between someone who deserves our help, and someone who doesn’t.

By offering feedback on the strength of an application according to the above mentioned metrics (Completeness, Effort, Spelling, etc), Decissio could potentially improve the chances of failing applications where the main problem is poor writing.

An opportunity to improve an application is also an opportunity for us to see value where it is hard to spot. Telling an applicant that their application is failing because of style and substance can help those applicants to better express themselves, and thus deliver us more opportunities to find quality teams.

Conclusions

StartupYard and Decissio pilot project shows that AI assisted investing can improve results… Click To Tweet

The results of this pilot clearly show that there is great potential in enhancing our decision making process with machine learning and data analysis.

We are not at the point where we’re ready to let a machine determine our investment strategies on its own- the way machines already do some forms of investing without human inputs.

Unlike an investor in securities, or a high-frequency bond trader, an accelerator’s main advantages are as a first mover. We invest in companies that don’t exist yet, have limited information on their markets, and have a limited history, or no history. So we invest in people – and people are inherently hard to quantify.

Our anecdotal experience of meeting teams in person *before* evaluating their applications, consistently reveals that the application process cannot identify many important personality traits. For an accelerator, success comes only when we are right about a trend, and a particular person, at just the right time.

So employing an AI powered decision-making approach cannot mean abandoning the unique advantages we have: the ability to see things others don’t see. Expertise (and hard work) is still the core of sound early-stage investing, but AI can help us to focus that expertise on the “creme de la creme” of potential investments.

It can save us from becoming jaded by the junk applications that routinely swamp our inboxes.

A startup is not an individual, it’s a team. And it is not in our interest to arbitrarily eliminate applicants who are not good at writing applications, or have other deficiencies more visible on an application than in real life. However, it is in our interest to conserve and spend our resources (including our time and energy), where the potential for gain is highest. 

This approach can benefit higher-dollar investors too: later stage investors have many of the same problems accelerators have, but on a different scale. A Seed or Series A investor makes decisions involving 10-50x more money than any single investment from an accelerator, and they also receive more requests, on average, than a small accelerator does.

Currently the most obvious and most immediate advantage of using Decissio’s AI is for very early stage investors with many applicants, such as government innovation programs, and big accelerators like TechStars, Y-Combinator, and 500 Startups. 

Tl;dr:

  • StartupYard alum Decissio analyzed our past applications over a 6 year period.
  • Decissio used this data and their own AI to predict which applications to StartupYard would succeed.
  • Two of their top 10 picks were also StartupYard finalists
  • They accurately predicted the bottom ranked half of applicants.
  • This approach can be used by accelerators to:
    • Improve applications overall
    • Save time on the poorest applications
    • Reduce systemic biases
    • Get better information on applicants
  • Decissio’s AI could be applied to other early stage investors, such as Series A and Seed Investors, or to large accelerators, particularly Tech Stars, Y-C, and 500 Startups.
  • At the end of the day, AI will help early-stage investors to get better information, and spend more time focusing on the human-focused side of their work.
Central Europe Accelerator

11 Things We Say All the Time to Startups

11 Things We say ALL THE TIME to Startups

“You Just have to…”

Paul Graham has an amazing post on his blog, called “Before the Startup.” You should read it. I’ll wait.


Ok, for those who haven’t read it: he talks about how his role as a startup mentor is often just to repeat the same things. After a while, he realized that the problem wasn’t that startups didn’t know things- it was that they were asking the wrong kinds of questions. And they were doing it because they’d been trained in life and education to do it that way.

Instead of asking “what do you think about…” startup founders ask: “how do I…” They do this because the education system and tech culture itself tell them that there are “secret answers” or “key learnings” that apply to almost any situation. Like the college student who asks if a piece of information will be on the test, startups look for “tricks,” asking what they should be doing, instead of asking for mentors to react to what they’ve actually done.

We have much the same experience at StartupYard, and so we thought it would be useful to break down those things we say so often, and explain why it is we say them.

For this piece, 11 Things We Say All the Time to Startups, there will be two contributors: StartupYard Managing Director Cedric Maloux, and Community manager Lloyd Waldo. The original version of this post appeared on our blog in March, 2016.

“It’s Not About You”

Cedric: Startup founders tend to focus, particularly at an early stage, on what they want, and what kind of company they want to be, instead of the problems that they will solve for their users. When they first start talking to their customers, they will talk about “we,” and “us,” instead of “you,” and “our customers.”

So it’s almost always necessary to refocus your messaging early on to make sure that you’re focusing on your customer’s problems, and are bringing them something of value, not just attaining a goal that you have as a company. Less “we need your support,” and more: “you need this product.”

“You Are Not Your Customers.”

Lloyd: Banish this aphorism from your speaking vocabulary. You created a business and risked everything to run a startup on the strength of one idea. You are not like the people who will be your customers. You may know a lot about them, and you may even use your product, but you also created it yourself. That does not give you an excuse to not talk to users, and try to understand them better than you do.

“No One Will Believe Your Projections”

Cedric: When we talk about projections (user growth, revenue), founders can get too caught up in how to make projections that investors might believe in.

But that’s backwards. Investors will never believe in projections, because they are just that- projections. Instead, you need to develop a plan that makes those projections seem attainable. Investors don’t invest in your projections- they invest in your plan, and if that plan makes sense, it doesn’t matter whether the projections are believable or not.

“How Will This Help You Grow?”

Cedric meeting with StartupYard Startups in 2015

Lloyd: Startups come across a lot of ideas about things that might help them grow. It’s important to keep in mind the goal of doing anything connected with so called “growth hacking,” which is to actually grow.

Vanity statistics, like Facebook likes and Twitter followers, are not growth (not alone). But the logic often goes like this: Step 1: Likes on Facebook (or whatever), Step 2: …? Step 3: Growth. Focusing on how something will lead to growth is important- you can’t go from step 1 to step 3 without taking step 2. So what is step 2?

“What’s the Next Step?”

Cedric: Just like startups have to focus on how doing things will help them grow, they have to also make sure that every step they take has something after it. Everything has a desired result. You got a meeting with a potential partner? Great. What’s the next step? If there is no next step, then what will that meeting accomplish? What will that partnership accomplish without a clearly stated goal?

“Where’s the Call to Action?”

Lloyd: Simply put, you shouldn’t be communicating with your customers if you aren’t giving them something to do, or something they value.

Startup founders usually get a sense that they have to be activating their customers, but they also have to activate them to do clear and understandable things. There also has to be a clear way of measuring whether that activation is actually working. Enter the Call to Action: if you don’t have one, in an email, a post on social media, or a landing page, then you are wasting your users’ time and attention for nothing.

“You’ll have to test this and see”

Lloyd: Founders are prone to confusing advice with directions. As a mentor, I can give good and actionable advice, but just because I think it will work, doesn’t mean it works. The only way to see if the advice is sound is to try it, and pay attention to the results. Testing can’t tell you everything, but not testing tells you nothing.

“I can’t hear you.”

Cedric: When you meet with employees, with investors, or with anybody, remember that you’re the founder of a company. Your opinion matters, and you need to be heard, loud and clear. Some founders just don’t know how to make themselves heard, and make their presence felt. Instead of owning the room and controlling the conversation, they react passively, and let others lead. Instead, be the boss, and say what you think in a clear, audible voice. You’re the boss.

“It’s Your Company.”

Lloyd: Leading from that, remember that whatever you’re doing, make sure you believe in it. In an accelerator, you get a lot of advice, and a lot of direction. But it’s your company. If you aren’t happy with things a certain way, then the last word is ultimately yours. You should listen, and be open, but you shouldn’t do things just because people tell you to. If something doesn’t feel right, ask for help, but don’t “go with the flow.” It’s your company.

“Stop Selling, and Start Creating Visions”

Cedric: Selling isn’t about getting money from people. It’s about giving them something they can believe in, and are willing to pay for. To sell in the long run, you have to build a vision that people can relate to, and that people want to be a part of. If you focus on your vision, and on communicating that vision to people, then the money conversations -the selling- are just a detail. A small part of the overall experience, and not the focus.

“You Need to Control This Process”

Cedric: B2B sales are very different from B2C sales. In B2B sales, you need to remain in the driver seat, not waiting for the customer to decide that they’d like to work with you. You need to own the process: move each separate piece like a conductor, anticipate every question and issue, and close the deal. Going at the pace that the client picks is, in effect, accepting the client’s own objections and doubts as your own. If you don’t set the pace, then no one will, and many deals that could happen just won’t.

Ouibring, StartupYard

SY Batch 7 Alum Ouibring Gains Investment – With a Twist

Good news often comes all at once. Yesterday we announced that Neuron Soundware had raised €600,000, and StartupYard has raised €1 million in a record breaking investment round.  Today we’re able to announce that Ouibring, a StartupYard company (Batch 7) that helps travelers and shoppers to bring joy into each other’s lives by bringing rare items home with them from abroad, has also raised seed investment.

StartupYard, Ouibring

OuiBring Founder and CEO Joel Gordon, signing a deal with Busyman.cz

The Details

The seed investment comes from the Czech incubator Busyman.cz.

Since joining StartupYard in late 2016, Ouibring has quickly built a following of more than 60,000 Facebook fans. Filip Major, the founder of Busyman commented: “Ouibring has the potential to change the global consumer goods logistic system as UBER is changing the way people move”.

The investment will power global expansion, as Ouibring connects more of the 30 million flights carrying almost 1 billion travelers each year with shoppers all around the world.

Ouibring connects shoppers who need help sourcing hard to find products, and travelers with spare luggage capacity to create a win-win situation. On Ouibring’s platform it’s possible to order hard-to-find goods from your home country, or discover new items that travelers can then bring with them when they visit a city near you.

Ouibring, Startupyard

The Twist

Busyman.cz has acquired a minority stake in Ouibring using a digital commodity, “Crown,” which is a “non-pre-mined” digital currency.

Crown has a market cap of more than $13m USD, processes hundreds of transactions per day on its blockchain and provides powerful security features. As part of this deal, Ouibring will move its client-to-client transaction settlement onto the Crown blockchain, making every transaction easily trackable, efficient and transparent. Ouibring also aims to emit its own token of exchange on the Crown blockchain.

“Our customers care about security and compliance. Using the Crown blockchain to create unique new features will help make Ouibring even more reliable and easy to use for our customers” says Joel Gordon, CEO and founder of Ouibring.

About Ouibring:

In our interview with him earlier this year, Joel told us the story behind Ouibring as a new online shopping experience:

 

” The idea for Ouibring came from experiences gained living and working abroad for the last 15 years. The fun and excitement when a special package delivered by a friend arrives is the inspiration for Ouibring’s tagline – ‘Bring a little happiness’.

As any expatriate knows, living abroad can give you a special appreciation for things that those at home just take for granted. You look forward to that time when a friend will bring a special something you’ve requested from your home. That’s a magical feeling, as if you’re the only person in the world that has what you have. We wanted to capture that feeling, and make it something anyone could enjoy. A special moment of joy only for them; an experience no one else is having.

At the same time, we can give others the chance to make a bit of money, and reduce waste by sharing their spare luggage capacity.

One story I really like is how even a small, generic item that is plentiful in one location can provide a whole lot of pleasure and luxury when it appears in an unexpected context. When a Ouibringer arrived with three massive bags of Monster Munch Pickled Onion and delivered them to a travel blogger living in Bangkok. They really made her day.” 

Joel Gordon
Joel GordonCEO, Ouibring

 

What’s Next for OuiBring

CEO Joel Gordon moonlights as a user of his own product: here he delivers some treats to customers in Thailand.

Ouibring has already attracted hundreds of shoppers and travelers from around the world. The company is continuing to explore alternative approaches to shopping and fulfillment for adventurous people everywhere.

So, to celebrate this big step for the young company, why not jump over and order something for yourself, or sign up to bring a little happiness into someone else’s life?

You can now apply for StartupYard Batch #8.

  • Robots
  • Artificial Intelligence
  • VR/AR
  • IoT
  • Cryptography
  • Blockchain
Applications Open: Now
Applications Close: June 30th, 2017
Program starts: September 4th, 2017
Program ends: December 1st, 2017
Pavel Konecny, Neuron Soundware, StartupYard

SY Alum Neuron Soundware Closes €600K Investment from J&T Ventures

We are very pleased to announce that Neuron Soundware, or 2016 Alum, and winner of “Vodafone Idea of the Year 2016” has closed an investment from Prague-based J&T Ventures, of €600,000 to grow their team and expand their sales to capitalize on early traction with clients like Siemens.

The story broke first on Euro.cz this morning.

Congratulations @startupyard alum @NeuronSW on exciting progress, and fundraising €600K to expand operations! Click To Tweet

The Details

Pavel Konecny, Co-founder and CEO of Neuron Soundware, made the announcement today in Mlada Fronta, together with Adam Kocik, Managing Director of J&T Ventures The investment will help Neuron Soundware to beef up its team, refine its technology, and expand its customer reach to include aerospace manufacturers, rail operators, and automotive companies.

Neuron Soundware, founded in 2016, joined StartupYard the same year. There the founding team, a group of AI experts led by Konecny, conceived of a device which can listen to heavy machinery, and over time, learn to recognize mechanical issues and predict when the machinery is likely to fail. Since attending StartupYard, they have developed a device employing high-end sensors used in aerospace, and audio processing software that can be plugged directly into heavy machinery and can warn of future mechanical problems. The company announced a cooperation with Siemens in 2016, and was invited to join the Airbus Innovation Lab the same year.

“We are continually impressed by the Neuron Soundware team’s technical prowess and ability to attack very complex problem sets with novel approaches and technology,” Kocik commented on the investment, “this technology is going to be even more essential as the IoT [Internet of Things] matures. Neuron Soundware will help to make machines safer, more efficient, and longer lasting.” The investment, a cooperation between J&T Ventures and a private investor, will be used to refine the engineering of Neuron Soundware’s physical devices and software, and to support its outreach to large industrial machinery firms, where demand for the technology is already growing.

Neuron Soundware, StartupYard Accelerator

According to Konecny, the technology, based on “deep neural networks,” learns from the sounds machinery produces, and can detect patterns too faint or complex for a human to hear, diagnosing issues with machinery well before they become catastrophic. Konecny says of the technology: “Sound is a rich source of data, and also quite universal, which is why mechanics and engineers rely on it so much. But a human cannot listen to 100 airplane or diesel engines for 1000 hours each, and make sense of it all. A machine can do this, and when one engine fails, it can apply that learning to all it has already heard, thus greatly enhancing our ability to detect and prevent future problems.”

“When Neuron Soundware joined us for our 6th program [out of 8], their approach to understanding sound had never really been tried before,” commented Cedric Maloux, our CEO, “leveraging StartupYard’s mentor network, locally and abroad, they were able to very quickly prove that there was a huge need for this kind of technology.” The company notes that future applications for machine learning and sound reach beyond machine maintenance, to product testing, autonomous navigation, green energy solutions, and even security. “Sound is everywhere,” remarks Konecny, “and we’ve just started to see how we can use it to understand more of how everything works.”

About Neuron Soundware:

Neuron Soundware is a deep tech startup, exploring the use of self-teaching, constantly learning neural networks in a wide range of audio analysis and audio manipulation applications. Since 2016, Neuron Soundware has focused on technology to monitor and diagnose industrial equipment to predict failures and increase efficiency. They include Siemens and a number of other leading industrial and transportation equipment manufacturers among their clients.

About J&T Ventures:

J&T Ventures is a Venture Capital fund based in Prague. The fund invests up to €500 000 in technology firms at the seed stage in CEE region. Since 2014, J&T Ventures has been invested in 11 growing and promising innovative startups with the goal to contribute to their dynamic growth and value creation. The fund focuses mainly on B2B sector with a particular interest in FinTech, IT (Big Data Analytics), IoT/IoE & Smart City IoT and Retail.

 

You can now apply for StartupYard Batch #8.

  • Robots
  • Artificial Intelligence
  • VR/AR
  • IoT
  • Cryptography
  • Blockchain
Applications Open: Now
Applications Close: June 30th, 2017
Program starts: September 4th, 2017
Program ends: December 1st, 2017
Rossum, Startupyard

Exclusive Interview: Rossum – AI for Documents

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.

The Rossum Team: Petr Baudis (left), and Tomas Gogar (middle), with Tomas Tunys (right)

The Rossum Team: Petr Baudis (left), and Tomas Gogar (middle), with Tomas Tunys (right)

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.

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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.

Rossum: Human level judgement is slow. And most AI is dumb. Read the exclusive interview: Click To Tweet

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.

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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.  

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Tomas Gogar (right), works with another co-founder at the StartupYard voice workshop

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.

Chatler: AI for Conversations at Scale

The past year has seen a boom in chatbots, which have become a buzzword in the tech industry, most particularly with retailers and big brands. StartupYard this year handled dozens of applications for chatbot startups, but despite the buzz, none of these seemed to us to have really discovered the inherent value of automating customer interaction on social media, and in customer care. Chatbots are not a new idea, after all, and much of the recent hype has come thanks to Facebook opening its platform for 3rd party developers, which has spurred renewed interest in new applications for chat.

Facebook’s strategy is bold, and we believe that it may yet yield positive net benefits for end-users and customers, but the chatbot arena is still very immature. And so Janos Szabo gained our attention immediately when we met him and his team during our roadshow, in Budapest last summer. Janos convinced us that the future of chatbots, at least for now, is not in full automation, but in AI integration. He founded Chatler late last year with a team of friends, including strong experience in brand management, to prove that the way forward for chat with big companies is in helping human beings to do what they do best:  be human,

I caught up with Janos this week to talk about his vision for Chatler. Here is our discussion:

Hi Janos, tell us a bit about Chatler. How did you come up with the idea?

I’ve worked a bit with chatbots. I was actually on a team that was creating them. But it quickly became apparent that chatbots aren’t the massive game-changer they’ve been billed as. They aren’t a “fire and forget” sort of thing. They need a lot of care, and a lot of time, and as a result they never end up being as good or as efficient as we hope they will be.

There’s a cycle to AI products in general. It starts with hype: a new way of doing something has its day in the sun, like chatbots, or language translation, and early demonstrations are incredibly promising. But when they’re released into the wild, these things just never work as well as we hope they will. They always have their limitations, and those limitations become apparent relatively fast.

Janos Szabo, CEO and Co-Founder of Chatler

Janos Szabo, CEO and Co-Founder of Chatler

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.

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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?”

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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.

A messenger code. Scan it to talk to Chatler.

A Facebook messenger code. Scan it to talk to Chatler.

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.

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Chatler understands, customer interactions,and recommends effective answers and solutions.

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. 

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The chatler team: Andras, Andris, and Janos

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.

The Chatler team during mentoring.

The Chatler team during mentoring.

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.

 

Meet Decissio: Your Jarvis for Investment Decisions

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.

Decissio, StartupYard

Dite Gashi, left, participating in StartupYard’s voice training workshop

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.

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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.

Dashboard

An early prototype of a Decissio dashboard

 

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.

Beeem, StartupYard

Meet Beeem, A Website for Every Thing

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.

Beeem.co, StartupYard, beacons

 

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.

Beeem, StartupYard, Central Europe Accelerator

Co-Founder CEO of Beeem, Ferenc Brachmann

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.

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The Future of Apps is... No Apps. via @beeemapp – a Website for Every Thing. Click To Tweet

Or…

 

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.

Beeem Teeem

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.

Beeem, Product

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.

Beeem, StartupYard

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.

Meet Cryptelo: The Unbreakable Dropbox

Cryptelo joins StartupYard as few companies do, with a fully launched product and existing customers. Founded in 2014, Cryptelo is an end-to-end secure file storage and messaging platform, offering a measure of protection unparalleled by the major file storage, transfer, and communication platforms.

Cryptelo, originally targeted at security conscious consumers, has shifted its focus toward organizations with highly sensitive data, and a need to make controlled access to that data readily available, and totally safe. Already becoming a favorite among the Czech legal community, Cryptelo is poised to challenge big storage providers by offering first-in-class protection against all manner of cyber-attacks, including physical penetration. To do this, they’ve recruited one of the world’s leading cryptologists, Vlastimil Klima, who was among the first to crack the SSL protocol, the security relied upon by the world’s banks. I caught up with Founder and CEO Martin Baros, to talk about his technology, and his vision for Cryptelo.

Hi Martin, Cryptelo is a very ambitious project; solving cloud storage security is something the biggest players haven’t really tackled. What made you want to do it?

Martin Baros, Cryptelo, StartupYard

Martin Baros, Founder and CEO of Cryptelo

My personal experience has taught me how important security really is. Years ago, I was hacked, and my intellectual property was stolen. That cost my company over 2 million CZK (about 100,000 Euros).

It was not fair. It felt like a violation- and that’s a common feeling for victims of theft. I blamed myself, but in time, I came to see that people are really being set up to fail when it comes to digital security. Someone, somewhere, decided that security doesn’t sell, and that’s not right. I set out to change it.

So I decided to create my own solution. This was the start of Cryptelo. I believe that no matter how big your company is, you should have an accessible tool for great security to keep your documents yours.

There was a time in which company security was easy: someone just could not read your documents and communications from the other side of the world, much less the other side of the room. But no more. Most information today is created digital. I’m convinced that we must have a full right to decide who can read our documents. This sense that we now have, that nothing we say will stay private, is chilling. It tells us that we cannot be candid and we cannot take intellectual risks and speak our minds. That’s not right at all.

That’s why I like cryptography – it can bring freedom and real security in the current digital era.

Let’s talk a bit about your team. You have some of the best cryptography talent in the world. What makes your team better than any other?

Vlastimil_Klima_Cryptologist

Vlastimil Klima, world leading cryptologist, and the mind behind Cryptelo security.

I have a strong technology background based on studying at MFF UK and 10 years of professional experience as a software developer, team leader and key account manager in projects with Accenture, Wüstenrot and AirBank.

When we developed the technical proof of concept of Cryptelo, I decided to approach Dr. Vlastimil Klíma – one of the best cryptographers in the world. After just an hour’s discussion where I described our vision, he decided to join us and has became part of our team. He created the cryptographical basis of Cryptelo

Then we needed superior implementation. Just imagine a product, which could encrypt all of your data, but wouldn’t be able to decrypt it. It would be secure, for sure, but not that useful.

That’s why I set out to build our team from the most talented programmers I have met during my career. Together we have over 40 years of experience in enterprise development. With this knowledge we started building the best software of our careers.

During development we have applied modern methodologies for software development and created amazing infrastructure, which enabled us to deliver new features almost immediately after they passed through testing. From the beginning we focused on automated testing – the underlying cryptographic elements are tested cross platform, to find incompatibilities which exist between different implementations on different platforms. Each change is built, packaged and is required to pass through wide range of UI tests, where an automated process simulates a user clicking in our application, trying to verify, that everything works as expected. We manage our fleet of servers remotely using SaltStack and monitor a wide range of properties of each host. We have also been running all of our services on docker from the beginning, which allowed us to offer on-premise solution early on.

You’ve experimented with B2C and B2B business models. What are you focusing on now, and why?

We started the service Screesh.com, which is similar to uschovna.cz (a file storage solution), but with strong encryption in the background. We also allowed users to encrypt files with a password directly in the browser without any extension. We believed that this would be much easier than usual way – using winrar with a password and sending documents as attachments.

We observed that even though screesh.com is so easy to use, the number of users was growing slowly. We found out that people individually don’t really understand how to  price their own security. It makes it very difficult to sell a totally secure solution.

We began to realize that a better way is to go for institutions that you trust, and put great security there. We all rely on banks, telco operators and even small businesses on a daily basis. Why should you take sole care of your personal security if big companies aren’t doing it themselves?

Currently, selling digital security to individuals is like selling crash helmets to pedestrians. It doesn’t do much good if the corporations are driving rally cars on the sidewalks.

 

Individual digital security is like crash helmets for pedestrians while companies drive rally cars… Click To Tweet

 

Why do you think it is that in 2017, security discipline is still generally so poor in many companies?

Imagine that you built a city with parks, family houses and skyscrapers. And when everything is ready you find out that you built it in an earthquake zone. But your houses are not ready for circumstances like this. What would you do? Would you demolish the whole city and build it from the scratch?

Cyber attacks are quite similar. Most companies didn’t know they should implement security and they built their businesses without it. And now there are 130 000 cyber attacks every single minute. That’s like 130,000 tiny little Earthquakes, and you’re just praying it doesn’t happen to you.

There is a significant trend to move data to the cloud. Cloud is connected with a lot of risks – you lose physical control of your data. End-to-end encryption is one answer for that. With E2E encryption your data are locked in the black box and travel like this securely over internet and are stored on the server. Only authorized people have the right key to open it on their computers.

All well and good, but the problem is that the most effective way how to implement this level of security is start from scratch. Especially big companies cannot demolish houses in their cities, because there are people already. But the truth is that the infrastructure of many big data companies just wasn’t designed properly. They are built for speed, for flexibility, and for accessibility. You can’t do that and expect unbreakable security at the same time, unless you build something secure from the ground up.

What are, to you, the 2 or 3 biggest mistakes most people make when it comes to their digital security? How can they fix these mistakes?

Cyber security risks are invisible to most people. That’s why they aren’t mindful.

We wouldn’t walk in a bad neighborhood in the night with money in your hand. But we pay online with our credit cards through unknown web pages using unsecured wifi. That’s pretty much the same thing. You won’t automatically get robbed, but if you knew how dangerous it was, you might not do it.

 

You don't walk around with your money out in public. But you do the same online every day.… Click To Tweet

 

We wouldn’t use a postcard even for love letter, but we send our personal information and details of million dollar contracts by email. That’s a serious dissonance in our sense of what is secure and what is not.

Worst is that the big players don’t want you to care about security, they want you to use their service and share there as much as possible about your likes, plans, dreams and your friends. This data is gold in the e-commerce business and many businesses are based on it these days. That’s why Facebook will never bring real security to their products. It would kill its business. They will always be playing catch-up with cyber-security because anything more proactive would only slow them down.

It’s also much cheaper if you don’t care about security too much. Have you ever tried to upload a well known movie on a file-storage platform? It’s uploaded in a few seconds. How is that possible? The reason is that users data are shared between accounts. That means, in effect, that the platform is scanning and analyzing everything you upload, and that data is all going somewhere out of your control.

Tell me a bit about your technology: how does Cryptelo work, and why is it unique? What can customers do with the platform?

CrypteloID_Preview_ENG

Cryptelo is a virtual encrypted drive. It has the basic functionalities of a Dropbox or a Google Drive – you can use your web browser to access files from any computer.

Even though Cryptelo is as easy to use as Dropbox, it brings end-to-end encryption and a zero-knowledge server concept. We have a totally different approach to security than Dropbox or Google drive. The standard approach is to create a service, put it on the physical server, and build barriers – spread data into more datacenters, put this servers behind a firewall, keep servers in the datacenter located in an anti-nuclear shield, restrict people who can access it.

But even with top-notch data center security, a “mission impossible” type attack could breach these barriers and gain physical access to the server. That’s about as secure as a bank vault- and bank vaults get robbed all the time.

Our approach is that we also have all these barriers, but when Tom Cruise steals the server, there is nothing useful on it. All data are encrypted and the keys for opening it are not there. The data is useless.

But Cryptelo is not just virtual encrypted drive. Drive is just one of the uses, and a first step toward what we are building with our secure platform. The technology we’ve built is able to secure chat, email, and provide strong authentication based on cryptography.

Just out of interest, why do you think it is that Czech engineers have gained such a strong reputation for security and cryptology prowess? Does something in the culture or history of Czechia make them particularly suited to the task?

It’s probably a combination of talent and environment. Slavic people are known for their strategic, probing thinking, and it’s a bit of justified stereotype that we produce chess masters and rocket scientists faster than we produce world renowned writers and artists. We have these  too, but to Czech people, there is art in working with your hands, and solving puzzles.

If someone describes the rules of a game – law, technical environment – we start to think: Is it bullet proof? Could I bypass it? It’s natural. We just like puzzles and smart solutions. And that’s exactly what maths and cryptography is.

We call it the “Zlate Ceske rucicky,” or “Golden Czech Hands.” Czech people just like to fix things, and to squeeze the tiniest efficiencies out of their materials. Sometimes we say this in a joking way, as a Czech would rather fix something old than buy something new. But it is deep in our culture that we build things that will last a lifetime. Just look at our cities: we have trams that have been running continuously for over 60 years, bridges and towers that have stood for centuries. We build for endurance.

And I think Czech technology proves out that trend as well.  We have had 40 years of communism behind us. Times when we had to find ways to create and fix things with limited resources. Look at our arms industry, or automotive- we produce robust products at low prices.

Combine these things and superior programmers and security experts are born.

And you can really see this trend in Czech: Avast (now together with AVG), TCP Cloud (acquired by Mirantis), TeskaLabs, Apiary (acquired by Oracle).

Before Google built sales offices in Europe, they built a development center in the Czech Republic. No coincidence.

What is the biggest difficulty you have in selling Cryptelo as a solution for your core customers, like law firms or consultancies?

Cryptelo, StartupYard

A look at Cryptelo Drives UI

We are currently targeting trusted institutions that need to set a high bar for their security with client communications, as well as internal communication. That means law firms, tax and finance companies, even banks. And one of the challenges here is that, again, people do not want to think about security. We find, for example, that potential customers often want to buy our solution because of its features, like storage and sharing, and not because it is secure. To them, security is seen as an add-on, and not the core value.

That takes some adjusting, and we need to meet our customers somewhere in the middle. They need to see the value in security, and paying more to have it. But that the same time, they need to feel that they are doing something that will not create an undue burden on them. People don’t want to “buy security.” They want to buy secure solutions- and that means selling both security and the solutions together, and they need to be educated to measure their value appropriately.

That has been a learning process for us, and one we have been applying successfully in our talks with law firms in the Czech Republic. Finding out what is most important to these law firms is key to helping them see the benefits of using Cryptelo- so we have learned more and more to focus on what the customer sees in the solution, not just what we see as its core value.

How has your experience been at StartupYard? What surprised you? Which of the mentors had the biggest impact, and why?

In StartupYard I fully realized that there are two different tracks in building a real company: the hard part of creating a product, and then the even harder part of selling it. It’s crucial to get advice from someone who’s been in your shoes. Thanks to SY we got the opportunity to talk with scores of experienced mentors and entrepreneurs who have all been there, and understand our struggles, and how to get past them. You can’t read this kind of thing in books.

Would you recommend that other startups apply to an accelerator?

100% SY is like a First Aid Kit for most of your business troubles. Imagine that you decide to build a company to fulfill your vision. How will you incorporate, get first money to build MVP? How would you know it wasn’t just a terrible idea, or completely the wrong direction to take?  Where will you meet tens of your potential customer to verify your market fit? How will you create and learn how to perform the perfect pitch, that you need for getting customers and bigger investors?