It’s a breach post-mortem.
Over the past two days, 21 companies have taken the stage at the Disrupt SF Startup Battlefield. We’ve now taken the feedback from all our expert judges and chosen five teams to compete in the finals.
These teams will all take the stage again tomorrow afternoon to present in front of a new set of judges and answer even more in-depth questions. Then one startup will be chosen as the winner of the Battlefield Cup — and they’ll also take home $100,000.
Here are the finalists. The competition will be livestreamed on TechCrunch starting at 1:35pm on Friday.
CB Therapeutics is a new biotech company that aims to change the game with cannabinoids produced cleanly and cheaply in the lab, out of sugar. What it’s done is bioengineer microorganisms — specifically yeast — to manufacture cannabinoids out of plain-old sugars.
Forethought has a modern vision for enterprise search that uses AI to surface the content that matters most in the context of work. Its first use case involves customer service, but it has a broader ambition to work across the enterprise.
Mira is a new device that aims to help women who are struggling to conceive. The Mira Fertility system offers personalized cycle prediction by measuring fertility hormone concentrations in urine samples, telling women which days they’re fertile.
Origami Labs wants to bring voice assistants right to your ear without requiring you to wear a device like a Bluetooth headset or Apple AirPods. Instead, the startup is using a ring on your finger combined with bone conduction technology to allow you to use your smartphone’s built-in assistant – whether that’s Google Assistant or Siri – in an all-new way.
Unbound makes fashion-forward vibrators, and their latest is the Palma. The new device masquerades as a ring, offers multiple speeds, and is completely waterproof. And the team plans to add accelerometer features.
A new informal study shows that 58 percent of tech employees from companies like Facebook, Amazon, Apple and Microsoft feel like frauds.
The social network says new videos prompted the move.
Knowing what’s going on in your warehouses and facilities is of course critical to many industries, but regular inspections take time, money, and personnel. Why not use drones? Vtrus uses computer vision to let a compact drone not just safely navigate indoor environments but create detailed 3D maps of them for inspectors and workers to consult, autonomously and in real time.
Vtrus showed off its hardware platform — currently a prototype — and its proprietary SLAM (simultaneous location and mapping) software at TechCrunch Disrupt SF as a Startup Battlefield Wildcard company.
There are already some drone-based services for the likes of security and exterior imaging, but Vtrus CTO Jonathan Lenoff told me that those are only practical because they operate with a large margin for error. If you’re searching for open doors or intruders beyond the fence, it doesn’t matter if you’re at 25 feet up or 26. But inside a warehouse or production line every inch counts and imaging has to be carried out at a much finer scale.
As a result, dangerous and tedious inspections, such as checking the wiring on lighting or looking for rust under an elevated walkway, have to be done by people. Vtrus wouldn’t put those people out of work, but it might take them out of danger.
The drone, called the ABI Zero for now, is equipped with a suite of sensors, from ordinary RGB cameras to 360 ones and a structured-light depth sensor. As soon as it takes off, it begins mapping its environment in great detail: it takes in 300,000 depth points 30 times per second, combining that with its other cameras to produce a detailed map of its surroundings.
It uses this information to get around, of course, but the data is also streamed over wi-fi in real time to the base station and Vtrus’s own cloud service, through which operators and inspectors can access it.
The SLAM technique they use was developed in-house; CEO Renato Moreno built and sold a company (to Facebook/Oculus) using some of the principles, but improvements to imaging and processing power have made it possible to do it faster and in greater detail than before. Not to mention on a drone that’s flying around an indoor space full of people and valuable inventory.
On a full charge, ABI can fly for about 10 minutes. That doesn’t sound very impressive, but the important thing isn’t staying aloft for a long time — few drones can do that to begin with — but how quickly it can get back up there. That’s where the special docking and charging mechanism comes in.
The Vtrus drone lives on and returns to a little box, which when a tapped-out craft touches down, sets off a patented high-speed charging process. It’s contact-based, not wireless, and happens automatically. The drone can then get back in the air perhaps half an hour or so later, meaning the craft can actually be in the air for as much as six hours a day total.
Probably anyone who has had to inspect or maintain any kind of building or space bigger than a studio apartment can see the value in getting frequent, high-precision updates on everything in that space, from storage shelving to heavy machinery. You’d put in an ABI for every X square feet depending on what you need it to do; they can access each other’s data and combine it as well.
This frequency and the detail which which the drone can inspect and navigate means maintenance can become proactive rather than reactive — you see rust on a pipe or a hot spot on a machine during the drone’s hourly pass rather than days later when the part fails. And if you don’t have an expert on site, the full 3D map and even manual drone control can be handed over to your HVAC guy or union rep.
You can see lots more examples of ABI in action at the Vtrus website. Way too many to embed here.
Lenoff, Moreno, and third co-founder Carlos Sanchez, who brings the industrial expertise to the mix, explained that their secret sauce is really the software — the drone itself is pretty much off the shelf stuff right now, tweaked to their requirements. (The base is an original creation, of course.)
But the software is all custom built to handle not just high-resolution 3D mapping in real time but the means to stream and record it as well. They’ve hired experts to build those systems as well — the 6-person team already sounds like a powerhouse.
The whole operation is self-funded right now, and the team is seeking investment. But that doesn’t mean they’re idle: they’re working with major companies already and operating a “pilotless” program (get it?). The team has been traveling the country visiting facilities, showing how the system works, and collecting feedback and requests. It’s hard to imagine they won’t have big clients soon.
Don’t call Wingly the “Uber of the Sky” — Wingly co-fonder Emeric de Waziers would like to nip that little misinterpretation in the bud as the French startup looks to expand into the U.S. If anything, the startup’s mission is more akin to carpooling for small aircrafts, helping pilots fill up empty seats in small passenger planes.
The distinction is an important one, with regard to the company’s ability to operate. After all, allowing private pilots to turn a profit changes the math significantly, both with regard to specific licenses and the company’s ability to operate inside different countries. Ninety-five percent of pilots who use the service don’t have a commercial license.
“What often happens with hobby pilots is they set a budget for the year. They’re going to fly as many times as they can with this money. If they can fly four times cheaper, they can fly four times more. We have many pilots posting what we call ‘flexible flights,’ saying, ‘hey, I’m available for a roundtrip from San Francisco to Tahoe.’ The passenger says they’re interested and they book the flight.”
Founded in July 2015, the company faced regulatory challenges early on in its native France. It was enough to cause Wingly to relocate operations, setting up shop in Germany in February of the following year. That launch was a sort of a proof of concept for the novel flight booking app. It was successful enough to convince Wingly to take on its home country again, pushing back against French regulatory bodies.
These days, it operates in Germany, France and the UK, with those markets composing 45, 30 and 20 percent of the company’s business, respectively (with the other five percent belonging to various parts of Europe). Wingly’s flight matching service currently hosts around 2,000 passengers a month, with each flight averaging about 1.8 passengers.
It’s not a huge number, but, then, these aren’t huge planes, with the prop and twin-engine crafts sporting between two and six seats each. Profitability for Wingly means pushing into high volume numbers, but the current pace has been successful enough for the startup to begin pursuing the U.S. as its next major market — a move the company plans to begin in earnest as a Battlefield contestant at Disrupt today in San Francisco.
Currently, Wingly takes a 15-percent commission on each flight, along with a €5 charge. The company has also raised €2.5 million including a €2 million seed round back in December. It’s been enough funding to help the company thrive in Europe, but coming to the States will require additional cash, particularly its current launch time frame of early 2019. From there, Wingly hopes to reach numbers comparable to the business it’s doing in Europe by August/September of next year.
Kinta AI aims to make manufacturers smarter about how they deploy their equipment and other factory resources.
The company, which is presenting today at TechCrunch’s Startup Battlefield in San Francisco, was founded by a team with plenty of experience in finance, tech and AI.
CEO Steven Glinert has held management and AI roles at fintech startups, CTO Rob Donnelly is studying the intersection of machine learning and economics as a Ph.D. candidate at Stanford and VP of Engineering Ben Zax has worked at both Facebook and Google.
Glinert told me that when factory owners are making production decisions, they’re usually relying on “dumb software” to decide which machines should be used when, which can result in machines being deployed at the wrong time or in the wrong sequence, or sitting idle when they shouldn’t be. As a result, he said that scheduling errors account for 45 percent of late manufacturing orders.
So Kinta AI tries to solve this problem with artificial intelligence, specifically reinforcement learning. Glinert said the company will run “millions and millions of factory simulations,” where the system gains “a statistical understanding of how your factory works and learning what actions you do to get what result” — and it can then use those simulations to choose the best schedule.
“We run through, not every possible scenario, but we try to go through some of those,” he said.
Glinert added that Kinta AI works with its customers to understand the nuances of each factory. He also compared the technology to Google’s AlphaGo AI and OpenAI’s Dota 2 neural networks — except that instead of using AI to play Go or Dota 2, Gilnert said Kinta AI is utilizing it “to do these detailed production planning decisions that are being made on the factory floor.”
“Not all factories are that dissimilar from each other,” he said — similar to how “if you learned how to play Go, you can easily teach that neural net how to play chess or other game of that type.”
And Kinta AI already has some customers, including chemical manufacturer BASF and a medical device manufacturer.
Ultimately, Glinert said Kinta AI could become a crucial part of the manufacturing process. He predicted that “in the factory of the future, there will be fewer people and more automation, with a vast environment of Internet of Things devices.”
In that environment, he wants Kinta AI to be “the manufacturer execution system.”