Computer Vision
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Mobius Labs nabs $6M to help more sectors tap into computer vision
Berlin-based Mobius Labs has closed a €5.2 million (~$6.1M) funding round off the back of increased demand for its computer vision training platform. The Series A investment is led by Ventech VC, along with Atlantic Labs, APEX Ventures, Space Capital, Lunar Ventures plus some additional angel investors.
The startup offers an SDK that lets the user create custom computer vision models fed with a little of their own training data — as an alternative to off-the-shelf tools which may not have the required specificity for a particular use-case.
It also flags a ‘no code’ focus, saying its tech has been designed with a non-technical user in mind.
As it’s an SDK, Mobius Labs’ platform can also be deployed on premise and/or on device — rather than the customer needing to connect to a cloud service to tap into the AI tool’s utility.
“Our custom training user interface is very simple to work with, and requires no prior technical knowledge on any level,” claims Appu Shaji, CEO and chief scientist.
“Over the years, a trend we have observed is that often the people who get the maximum value from AI are non technical personas like a content manager in a press and creative agency, or an application manager in the space sector. Our no-code AI allows anyone to build their own applications, thus enabling these users to get close to their vision without having to wait for AI experts or developer teams to help them.”
Mobius Labs — which was founded back in 2018 — now has 30 customers using its tools for a range of use cases.
Uses include categorisation, recommendation, prediction, reducing operational expense, and/or “generally connecting users and audiences to visual content that is most relevant to their needs”. (Press and broadcasting and the stock photography sector have unsurprisingly been big focuses to date.)
But it reckons there’s wider utility for its tech and is gearing up for growth.
It caters to businesses of various sizes, from startups to SMEs, but says it mainly targets global enterprises with major content challenges — hence its historical focus on the media sector and video use cases.
Now, though, it’s also targeting geospatial and earth observation applications as it seeks to expand its customer base.
The 30-strong startup has more than doubled in size over the last 18 months. With the new funding it’s planning to double its headcount again over the next 12 months as it looks to expand its geographical footprint — focusing on Europe and the US.
Year-on-year growth has also been 2x but it believes it can dial that up by tapping into other sectors.
“We are working with industries that are rich in visual data,” says Shaji. “The geospatial sector is something that we are focussing on currently as we have a strong belief that vast amounts of visual data is being produced by them. However, these huge archives of raw pixel data are useless on their own.
“For instance, if we want to track how river fronts are expanding, we have to look at data collected by satellites, sort and tag them in order to analyse them. Currently this is being done manually. The technology we are creating comes in a lightweight SDK, and can be deployed directly into these satellites so that the raw data can be detected and then analysed by machine learning algorithms. We are currently working with satellite companies in this sector.”
On the competitive front, Shaji names Clarifai and Google Cloud Vision as the main rivals it has in its sights.
“We realise these are the big players but at the same time believe that we have something unique to offer, which these players cannot: Unlike their solutions, our platform users can be outside the field of computer vision. By democratising the training of machine learning models beyond simply the technical crowd, we are making computer vision accessible and understandable by anyone, regardless of their job titles,” he argues.
“Another core value that differentiates us is the way we treat client data. Our solutions are delivered in the form of a Software Development Kit (SDK), which runs on-premise, completely locally on clients’ systems. No data is ever sent back to us. Our role is to empower people to build applications, and make them their own.”
Computer vision startups have been a hot acquisition target in recent years and some earlier startups offering ‘computer vision as a service’ got acquired by IT services firms to beef up their existing offerings, while tech giants like Amazon and (the aforementioned) Google offer their own computer vision services too.
But Shaji suggests the tech is now at a different stage of development — and primed for “mass adoption”.
“We’re talking about providing solutions that empower clients to build their own applications,” he says, summing up the competitive play. “And that [do that] with complete data privacy, where our solutions run on-premise, and we don’t see our clients data. Coupled with that is the ease of use that our technology offers: It is a lightweight solution that can be deployed on many ‘edge’ devices like smartphones, laptops, and even on satellites.”
Commenting on the funding in a statement, Stephan Wirries, partner at Ventech VC, added: “Appu and the team at Mobius Labs have developed an unparalleled offering in the computer vision space. Superhuman Vision is impressively innovative with its high degree of accuracy despite very limited required training to recognise new objects at excellent computational efficiency. We believe industries will be transformed through AI, and Mobius Labs is the European Deep Tech innovator teaching machines to see.”
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VOCHI raises additional $2.4 million for its computer vision-powered video editing app
VOCHI, a Belarus-based startup behind a clever computer vision-based video editing app used by online creators, has raised an additional $2.4 million in a “late-seed” round that follows the company’s initial $1.5 million round led by Ukraine-based Genesis Investments last year. The new funds follow a period of significant growth for the mobile tool, which is now used by more than 500,000 people per month and has achieved a $4 million-plus annual run rate in a year’s time.
Investors in the most recent round include TA Ventures, Angelsdeck, A.Partners, Startup Wise Guys, Kolos VC and angels from companies like Belarus-based Verv and Estonian unicorn Bolt. Along with the fundraise, VOCHI is elevating the company’s first employee, Anna Buglakova, who began as head of marketing, to the position of co-founder and chief product officer.
According to VOCHI co-founder and CEO Ilya Lesun, the company’s idea was to provide an easy way for people to create professional edits that could help them produce unique and trendy content for social media that could help them stand out and become more popular. To do so, VOCHI leverages a proprietary computer vision-based video segmentation algorithm that applies various effects to specific moving objects in a video or to images in static photos.
“To get this result, there are two trained [convolutional neural networks] to perform semi-supervised Video Object Segmentation and Instance Segmentation,” explains Lesun, of VOCHI’s technology. “Our team also developed a custom rendering engine for video effects that enables instant application in 4K on mobile devices. And it works perfectly without quality loss,” he adds. It works pretty fast, too — effects are applied in just seconds.
The company used the initial seed funding to invest in marketing and product development, growing its catalog to over 80 unique effects and more than 30 filters.
Image Credits: VOCHI
Today, the app offers a number of tools that let you give a video a particular aesthetic (like a dreamy vibe, artistic feel, or 8-bit look, for example). It also can highlight the moving content with glowing lines, add blurs or motion, apply different filters, insert 3D objects into the video, add glitter or sparkles and much more.
In addition to editing their content directly, users can swipe through a vertical home feed in the app where they can view the video edits others have applied to their own content for inspiration. When they see something they like, they can then tap a button to use the same effect on their own video. The finished results can then be shared out to other platforms, like Instagram, Snapchat and TikTok.
Though based in Belarus, most of VOCHI’s users are young adults from the U.S. Others hail from Russia, Saudi Arabia, Brazil and parts of Europe, Lesun says.
Unlike some of its video editor rivals, VOCHI offers a robust free experience where around 60% of the effects and filters are available without having to pay, along with other basic editing tools and content. More advanced features, like effect settings, unique presents and various special effects, require a subscription. This subscription, however, isn’t cheap — it’s either $7.99 per week or $39.99 for 12 weeks. This seemingly aims the subscription more at professional content creators rather than a casual user just looking to have fun with their videos from time to time. (A one-time purchase of $150 is also available, if you prefer.)
To date, around 20,000 of VOCHI’s 500,000 monthly active users have committed to a paid subscription, and that number is growing at a rate of 20% month-over-month, the company says.
Image Credits: VOCHI
The numbers VOCHI has delivered, however, aren’t as important as what the startup has been through to get there.
The company has been growing its business at a time when a dictatorial regime has been cracking down on opposition, leading to arrests and violence in the country. Last year, employees from U.S.-headquartered enterprise startup PandaDoc were arrested in Minsk by the Belarus police, in an act of state-led retaliation for their protests against President Alexander Lukashenko. In April, Imaguru, the country’s main startup hub, event and co-working space in Minsk — and birthplace of a number of startups, including MSQRD, which was acquired by Facebook — was also shut down by the Lukashenko regime.
Meanwhile, VOCHI was being featured as App of the Day in the App Store across 126 countries worldwide, and growing revenues to around $300,000 per month.
“Personal videos take an increasingly important place in our lives and for many has become a method of self-expression. VOCHI helps to follow the path of inspiration, education and provides tools for creativity through video,” said Andrei Avsievich, general partner at Bulba Ventures, where VOCHI was incubated. “I am happy that users and investors love VOCHI, which is reflected both in the revenue and the oversubscribed round.”
The additional funds will put VOCHI on the path to a Series A as it continues to work to attract more creators, improve user engagement and add more tools to the app, says Lesun.
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Snap acquires AR startup WaveOptics, which provides tech for Spectacles, for over $500M
Snap yesterday announced the latest iteration of its Spectacles augmented reality glasses, and today the company revealed a bit more news: it is also acquiring the startup that supplied the technology that helps power them. The Snapchat parent is snapping up WaveOptics, an AR startup that makes the waveguides and projectors used in AR glasses. These overlay virtual images on top of the views of the real world someone wearing the glasses can see, and Snap worked with WaveOptics to build its latest version of Spectacles.
The deal was first reported by The Verge, and a spokesperson for Snap directly confirmed the details to TechCrunch. Snap is paying over $500 million for the startup, in a cash-and-stock deal. The first half of that will be coming in the form of stock when the deal officially closes, and the remainder will be payable in cash or stock in two years.
This is a big leap for WaveOptics, which had raised around $65 million in funding from investors that included Bosch, Octopus Ventures and a host of individuals, from Stan Boland (veteran entrepreneur in the UK, most recently at FiveAI) and Ambarish Mitra (the co-founder of early AR startup Blippar). PitchBook estimates that its most recent valuation was only around $105 million.
WaveOptics was founded in Oxford, and from what we know it will continue to be based in the UK.
We have been covering the company since its earliest days, when it displayed some very interesting, early, and ahead-of-its-time technology: waveguides based on hologram physics and photonic crystals. The important and key thing is that its tech drastically compresses size and load of the hardware needed to process and display images, meaning a much wider and more flexible range of form factors for AR hardware based on WaveOptics tech.
It’s not clear whether WaveOptics will continue to work with other parties post-deal, but it seems that one obvious advantage for Snap would be making the startup’s technology exclusive to itself.
Snap has been on something of an acquisition march in recent times — it’s made at least three other purchases of startups since January, including Fit Analytics for an AR-fuelled move into e-commerce, as well as Pixel8Earth and StreetCred for its mapping tools.
This deal, however, marks Snap’s biggest acquisition to date in terms of valuation. That is not only a mark of the premium price that foundational artificial intelligence tech continues to command — in addition to the team of scientists that built WaveOptics, it also has 12 filed and in-progress patents — but also Snap’s financial and, frankly, existential commitment to having a seat at the table when it comes not just to social apps that use AR, but hardware, and being at the centre of not just using the tech, but setting the pace and agenda for how and where that will play out.
That’s been a tenacious and not always rewarding place for it to be, but the company — which has long described itself as a “camera company” — has kept hardware in the mix as an essential component for its future strategy.
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Veo raises $25M for AI-based cameras that record and analyze football and other team sports
Sports have been among some of the most popular and lucrative media plays in the world, luring broadcasters, advertisers and consumers to fork out huge sums to secure the chance to watch (and sponsor) their favorite teams and athletes.
That content, unsurprisingly, also typically costs a ton of money to produce, narrowing the production and distribution funnel even more. But today, a startup that’s cracked open that model with an autonomous, AI -based camera that lets any team record, edit and distribute their games, is announcing a round of funding to build out its business targeting the long tail of sporting teams and fixtures.
Veo Technologies, a Copenhagen startup that has designed a video camera and cloud-based subscription service to record and then automatically pick out highlights of games, which it then hosts on a platform for its customers to access and share that video content, has picked up €20 million (around $24.5 million) in a Series B round of funding.
The funding is being led by Danish investor Chr. Augustinus Fabrikker, with participation from U.S.-based Courtside VC, France’s Ventech and Denmark’s SEED Capital. Veo’s CEO and co-founder Henrik Teisbæk said in an interview that the startup is not disclosing its valuation, but a source close to funding tells me that it’s well over $100 million.
Teisbæk said that the plan will be to use the funds to continue expanding the company’s business on two levels. First, Veo will be digging into expanding its U.S. operations, with an office in Miami.
Second, it plans to continue enhancing the scope of its technology: The company started out optimising its computer vision software to record and track the matches for the most popular team sport in the world, football (soccer to U.S. readers), with customers buying the cameras — which retail for $800 — and the corresponding (mandatory) subscriptions — $1,200 annually — both to record games for spectators, as well as to use the footage for all kinds of practical purposes like training and recruitment videos. The key is that the cameras can be set up and left to run on their own. Once they are in place, they can record using wide-angles the majority of a soccer field (or whatever playing space is being used) and then zoom and edit down based on that.
Image Credits: Veo Technologies
Now, Veo is building the computer vision algorithms to expand that proposition into a plethora of other team-based sports, including rugby, basketball and hockey, and it is ramping up the kinds of analytics that it can provide around the clips that it generates, as well as the wider match itself.
Even with the slowdown in a lot of sporting activity this year due to COVID — in the U.K. for example, we’re in a lockdown again where team sports below professional leagues, excepting teams for disabled people, have been prohibited — Veo has seen a lot of growth.
The startup currently works with some 5,000 clubs globally ranging from professional sports teams through to amateur clubs for children, and it has recorded and tracked 200,000 games since opening for business in 2018, with a large proportion of that volume in the last year and in the U.S.
For a point of reference, in 2019, when we covered a $6 million round for Veo, the startup had racked up 1,000 clubs and 25,000 games, pointing to customer growth of 400% in that period.
The COVID-19 pandemic has indeed altered the playing field — literally and figuratively — for sports in the past year. Spectators, athletes and supporting staff need to be just as mindful as anyone else when it comes to spreading the coronavirus.
That’s not just led to a change in how many games are being played, but also for attendance: witness the huge lengths that the NBA went to last year to create an extensive isolation bubble in Orlando, Florida, to play out the season, with no actual fans in physical seats watching games, but all games and fans virtually streamed into the events as they happened.
That NBA effort, needless to say, came at a huge financial cost, one that any lesser league would never be able to carry, and so that predicament has led to an interesting use case for Veo.
Pre-pandemic, the Danish startup was quietly building its business around catering to the long tail of sporting organizations which — even in the best of times — would be hard-pressed to find the funds to buy cameras and/or hire videographers to record games, not just an essential part of how people can enjoy a sporting event, but useful for helping with team development.
“There is a perception that football is already being recorded and broadcast, but in the U.K. (for example) it’s only the Premier League,” Teisbæk said. “If you go down one or two steps from that, nothing is being recorded.” Before Veo, to record a football game, he added, “you need a guy sitting on a scaffold, and time and money to then cut that down to highlights. It’s just too cumbersome. But video is the best tool there is to develop talent. Kids are visual learners. And it’s a great way to get recruited, sending videos to colleges.”
Those use cases then expanded with the pandemic, he said. “Under coronavirus rules, parents cannot go out and watch their kids, and so video becomes a tool to follow those matches.”
‘We’re a Shopify, not an Amazon’
The business model for Veo up to now has largely been around what Teisbæk described as “the long tail theory”, which in the case of sports works out, he said, as “There won’t be many viewers for each match, but there are millions of matches out there.” But if you consider how a lot of high school sports will attract locals beyond those currently attached to a school — you have alumni supporters and fans, as well as local businesses and neighborhoods — even that long tail audience might be bigger than one might imagine.
Veo’s long-tail focus has inevitably meant that its target users are in the wide array of amateur or semi-pro clubs and the people associated with them, but interestingly it has also spilled into big names, too.
Veo’s cameras are being used by professional soccer clubs in the Premier League, Spain’s La Liga, Italy’s Serie A and France’s Ligue 1, as well as several clubs in the MLS such as Inter Miami, Austin FC, Atlanta United and FC Cincinnati. Teisbæk noted that while this might never be for primary coverage, it’s there to supplement for training and also be used in the academies attached to those organizations.
The plan longer term, he said, is not to build its own media empire with the trove of content that it has amassed, but to be an enabler for creating that content for its customers, who can in turn use it as they wish. It’s a “Shopify, not an Amazon,” said Teisbæk.
“We are not building the next ESPN, but we are helping the clubs unlock these connections that are already in place by way of our technology,” he said. “We want to help them capture and stream their matches and their play for the audience that is there today.”
That may be how he views the opportunity, but some investors are already eyeing up the bigger picture.
Vasu Kulkarni, a partner at Courtside VC — a firm that has focused (as its name might imply) on backing a lot of different sports-related businesses, with The Athletic, Beam (acquired by Microsoft) and many others in its portfolio — said that he’d been looking to back a company like Veo, building a smart, tech-enabled way to record and parse sports in a more cost-effective way.
“I spent close to four years trying to find a company trying to do that,” he said.
“I’ve always been a believer in sports content captured at the long tail,” he said. Coincidentally, he himself started a company called Krossover in his dorm room to help somewhat with tracking and recording sports training. Krossover eventually was acquired by Hudl, a competitor to Veo.
“You’ll never have the NBA finals recorded on Veo, there is just too much at stake, but when you start to look at all the areas where there isn’t enough mass media value to hire people, to produce and livestream, you get to the point where computer vision and AI are going to be doing the filming to get rid of the cost.”
He said that the economics are important here: the camera needs to be less than $1,000 (which it is) and able to produce something demonstrably better than “a parent with a Best Buy camcorder that was picked up for $100.”
Kulkarni thinks that longer term there could definitely be an opportunity to consider how to help clubs bring that content to a wider audience, especially using highlights and focusing on the best of the best in amateur games — which of course are the precursors to some of those players one day being world-famous elite athletes. (Think of how exciting it is to see the footage of Michael Jordan playing as a young student for some context here.) “AI will be able to pull out the best 10-15 plays and stitch them together for highlight reels,” he said, something that could feasibly find a market with sports fans wider than just the parents of the actual players.
All of that then feeds a bigger market for what has started to feel like an insatiable appetite for sports, one that, if anything, has found even more audience at a time when many are spending more time at home and watching video overall. “The more video you get from the sport, the better the sport gets, for players and fans,” Teisbæk said.
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Xesto is a foot scanning app that simplifies shoe gifting
You wait ages for foot scanning startups to help with the tricky fit issue that troubles online shoe shopping and then two come along at once: Launching today in time for Black Friday sprees is Xesto — which like Neatsy, which we wrote about earlier today, also makes use of the iPhone’s TrueDepth camera to generate individual 3D foot models for shoe size recommendations.
The Canadian startup hasn’t always been focused on feet. It has a long-standing research collaboration with the University of Toronto, alma mater of its CEO and co-founder Sophie Howe (its other co-founder and chief scientist, Afiny Akdemir, is also pursuing a Math PhD there) — and was actually founded back in 2015 to explore business ideas in human computer interaction.
But Howe tells us it moved into mobile sizing shortly after the 2017 launch of the iPhone X — which added a 3D depth camera to Apple’s smartphone. Since then Apple has added the sensor to additional iPhone models, pushing it within reach of a larger swathe of iOS users. So you can see why startups are spying a virtual fit opportunity here.
“This summer I had an aha! moment when my boyfriend saw a pair of fancy shoes on a deep discount online and thought they would be a great gift. He couldn’t remember my foot length at the time, and knew I didn’t own that brand so he couldn’t have gone through my closet to find my size,” says Howe. “I realized in that moment shoes as gifts are uncommon because they’re so hard to get correct because of size, and no one likes returning and exchanging gifts. When I’ve bought shoes for him in the past, I’ve had to ruin the surprise by calling him – and I’m not the only one. I realized in talking with friends this was a feature they all wanted without even knowing it… Shoes have such a cult status in wardrobes and it is time to unlock their gifting potential!”
Howe slid into this TechCrunch writer’s DMs with the eye-catching claim that Xesto’s foot-scanning technology is more accurate than Neatsy’s — sending a Xesto scan of her foot compared to Neatsy’s measure of it to back up the boast. (Aka: “We are under 1.5 mm accuracy. We compared against Neatsy right now and they are about 1.5 cm off of the true size of the app,” as she put it.)
Another big difference is Xesto isn’t selling any shoes itself. Nor is it interested in just sneakers; its shoe-type agnostic. If you can put it on your feet it wants to help you find the right fit, is the idea.
Right now the app is focused on the foot scanning process and the resulting 3D foot models — showing shoppers their feet in a 3D point cloud view, another photorealistic view as well as providing granular foot measurements.
There’s also a neat feature that lets you share your foot scans so, for example, a person who doesn’t have their own depth sensing iPhone could ask to borrow a friend’s to capture and takeaway scans of their own feet.
Helping people who want to be bought (correctly fitting) shoes as gifts is the main reason they’ve added foot scan sharing, per Howe — who notes shoppers can create and store multiple foot profiles on an account “for ease of group shopping”.
“Xesto is solving two problems: Buying shoes [online] for yourself, and buying shoes for someone else,” she tells TechCrunch. “Problem 1: When you buy shoes online, you might be unfamiliar with your size in the brand or model. If you’ve never bought from a brand before, it is very risky to make a purchase because there is very limited context in selecting your size. With many brands you translate your size yourself.
“Problem 2: People don’t only buy shoes for themselves. We enable gift and family purchasing (within a household or remote!) by sharing profiles.”
Xesto is doing its size predictions based on comparing a user’s (<1.5mm accurate) foot measurements to brands’ official sizing guidelines — with more than 150 shoe brands currently supported.
Howe says it plans to incorporate customer feedback into these predictions — including by analyzing online reviews where people tend to specify if a particular shoe sizes larger or smaller than expected. So it’s hoping to be able to keep honing the model’s accuracy.
“What we do is remove the uncertainty of finding your size by taking your 3D foot dimensions and correlate that to the brands sizes (or shoe model, if we have them),” she says. “We use the brands size guides and customer feedback to make the size recommendations. We have over 150 brands currently supported and are continuously adding more brands and models. We also recommend if you have extra wide feet you read reviews to see if you need to size up (until we have all that data robustly gathered).”
Asked about the competitive landscape, given all this foot scanning action, Howe admits there’s a number of approaches trying to help with virtual shoe fit — such as comparative brand sizing recommendations or even foot scanning with pieces of paper. But she argues Xesto has an edge because of the high level of detail of its 3D scans — and on account of its social sharing feature. Aka this is an app to make foot scans you can send your bestie for shopping keepsies.
“What we do that is unique is only use 3D depth data and computer vision to create a 3D scan of the foot with under 1.5mm accuracy (unmatched as far as we’ve seen) in only a few minutes,” she argues. “We don’t ask you any information about your feet, or to use a reference object. We make size recommendations based on your feet alone, then let you share them seamlessly with loved ones. Size sharing is a unique feature we haven’t seen in the sizing space that we’re incredibly excited about (not only because we will get more shoes as gifts :D).”
Xesto’s iOS app is free for shoppers to download. It’s also entirely free to create and share your foot scan in glorious 3D point cloud — and will remain so according to Howe. The team’s monetization plan is focused on building out partnerships with retailers, which is on the slate for 2021.
“Right now we’re not taking any revenue but next year we will be announcing partnerships where we work directly within brands ecosystems,” she says, adding: “[We wanted to offer] the app to customers in time for Black Friday and the holiday shopping season. In 2021, we are launching some exciting initiatives in partnership with brands. But the app will always be free for shoppers!”
Since being founded around five years ago, Howe says Xesto has raised a pre-seed round from angel investors and secured national advanced research grants, as well as taking in some revenue over its lifetime. The team has one patent granted and one pending for their technologies, she adds.
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Computer vision startup Chooch.ai scores $20M Series A
Chooch.ai, a startup that hopes to bring computer vision more broadly to companies to help them identify and tag elements at high speed, announced a $20 million Series A today.
Vickers Venture Partners led the round with participation from 212, Streamlined Ventures, Alumni Ventures Group, Waterman Ventures and several other unnamed investors. Today’s investment brings the total raised to $25.8 million, according to the company.
“Basically we set out to copy human visual intelligence in machines. That’s really what this whole journey is about,” CEO and co-founder Emrah Gultekin explained. As the company describes it, “Chooch Al can rapidly ingest and process visual data from any spectrum, generating AI models in hours that can detect objects, actions, processes, coordinates, states, and more.”
Chooch is trying to differentiate itself from other AI startups by taking a broader approach that could work in any setting, rather than concentrating on specific vertical applications. Using the pandemic as an example, Gultekin says you could use his company’s software to identify everyone who is not wearing a mask in the building or everyone who is not wearing a hard hat at a construction site.
With 22 employees spread across the U.S., India and Turkey, Chooch is building a diverse company just by virtue of its geography, but as it doubles the workforce in the coming year, it wants to continue to build on that.
“We’re immigrants. We’ve been through a lot of different things, and we recognize some of the issues and are very sensitive to them. One of our senior members is a person of color and we are very cognizant of the fact that we need to develop that part of our company,” he said. At a recent company meeting, he said that they were discussing how to build diversity into the policies and values of the company as they move forward.
The company currently has 18 enterprise clients and hopes to use the money to add engineers, data scientists and begin to build out a worldwide sales team to continue to build the product and expand its go-to-market effort.
Gultekin says that the company’s unusual name comes from a mix of the words choose and search. He says that it is also an old Italian insult. “It means dummy or idiot, which is what artificial intelligence is today. It’s a poor reflection of humanity or human intelligence in humans,” he said. His startup aims to change that.
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mmhmm, Phil Libin’s new startup, acquires Memix to add enhanced filters to its video presentation toolkit
Virtual meetings are a fundamental part of how we interact with each other these days, but even when (if!?) we find better ways to mitigate the effects of COVID-19, many think that they will be here to stay. That means there is an opportunity out there to improve how they work — because let’s face it, Zoom Fatigue is real and I for one am not super excited anymore to be a part of your Team.
Mmhmm, the video presentation startup from former Evernote CEO Phil Libin with ambitions to change the conversation (literally and figuratively) about what we can do with the medium — its first efforts have included things like the ability to manipulate presentation material around your video in real time to mimic newscasts — is today announcing an acquisition as it continues to home in on a wider launch of its product, currently in a closed beta.
It has acquired Memix, an outfit out of San Francisco that has built a series of filters you can apply to videos — either pre-recorded or streaming — to change the lighting, details in the background, or across the whole of the screen, and an app that works across various video platforms to apply those filters.
Like mmhmm, Memix is today focused on building tools that you use on existing video platforms — not building a video player itself. Memix today comes in the form of a virtual camera, accessible via Windows apps for Zoom, WebEx and Microsoft Teams; or web apps like Facebook Messenger, Houseparty and others that run on Chrome, Edge and Firefox.
Libin said in an interview that the plan will be to keep that virtual camera operating as is while it works on integrating the filters and Memix’s technology into mmhmm, while also laying the groundwork for building more on top of the platform.
Libin’s view is that while there are already a lot of video products and users in the market today, we are just at the start of it all, with technology and our expectations changing rapidly. We are shifting, he said, from wanting to reproduce existing experiences (like meetings) to creating completely new ones that might actually be better.
“There is a profound change in the world that we are just at the beginning of,” he said in an interview. “The main thing is that everything is hybrid. If you imagine all the experiences we can have, from in-person to online, or recorded to live, up to now almost everything in life fit neatly into one of those quadrants. The boundaries were fixed. Now all these boundaries have melted away we can rebuild every experience to be natively hybrid. This is a monumental change.”
That is a concept that the Memix founders have not just been thinking about, but also building the software to make it a reality.
“There is a lot to do,” said Pol Jeremias-Vila, one of the co-founders. “One of our ideas was to try to provide people who do streaming professionally an alternative to the really complicated set-ups you currently use,” which can involve expensive cameras, lights, microphones, stands and more. “Can we bring that to a user just with a couple of clicks? What can be done to put the same kind of tech you get with all that hardware into the hands of a massive audience?”
Memix’s team of two — co-founders Inigo Quilez and Pol Jeremias-Vila, Spaniards who met not in Spain but the Bay Area — are not coming on board full-time, but they will be helping with the transition and integration of the tech.
Libin said that he first became aware of Quilez from a YouTube video he’d posted on “The principles of painting with maths”, but that doesn’t give a lot away about the two co-founders. They are in reality graphic engineering whizzes, with Jeremias-Vila currently the lead graphics software engineer at Pixar, and Quilez until last year a product manager and lead engineer at Facebook, where he created, among other things, the Quill VR animation and production tool for Oculus.
Because working the kind of hours that people put in at tech companies wasn’t quite enough time to work on graphics applications, the pair started another effort called Beauty Pi (not to be confused with Beauty Pie), which has become a home for various collaborations between the two that had nothing to do with their day jobs. Memix had been bootstrapped by the pair as a project built out of that. Other efforts have included Shadertoy, a community and platform for creating Shaders (a computer program created to shade in 3D scenes).
That background of Memix points to an interesting opportunity in the world of video right now. In part because of all the focus (sorry not sorry!) on video right now as a medium because of our current pandemic circumstances, but also because of the advances in broadband, devices, apps and video technology, we’re seeing a huge proliferation of startups building interesting variations and improvements on the basic concept of video streaming.
Just in the area of videoconferencing alone, some of the hopefuls have included Headroom, which launched the other week with a really interesting AI-based approach to helping its users get more meaningful notes from meetings, and using computer vision to help presenters “read the room” better by detecting if people are getting bored, annoyed and more.
Vowel is also bringing a new set of tools not just to annotate meetings and their corresponding transcriptions in a better way, but to then be able to search across all your sessions to follow up items and dig into what people said over multiple events.
And Descript, which originally built a tool to edit audio tracks, earlier this week launched a video component, letting users edit visuals and what you say in those moving pictures, by cutting, pasting and rewriting a word-based document transcribing the sound from that video. All of these have obvious B2B angles, like mmhmm, and they are just the tip of the iceberg.
Indeed, the huge amount of IP out there is interesting in itself. Yet the jury is still out on where all of it would best live and thrive as the space continues to evolve, with more defined business models (and leading companies) only now emerging.
That presents an interesting opportunity not just for the biggies like Zoom, Google and Microsoft, but also players who are building entirely new platforms from the ground up.
Mmhmm is a notable company in that context. Not only does it have the reputation and inspiration of Libin behind it — a force powerful enough that even his foray into the ill-fated world of chatbots got headlines — but it’s also backed by the likes of Sequoia, which led a $31 million round earlier this month.
Libin said he doesn’t like to think of his startup as a consolidator, or the industry in a consolidation play, as that implies a degree of maturity in an area that he still feels is just getting started.
“We’re looking at this not so much as consolidation, which to me means market share,” he said. “Our main criteria is that we wanted to work with teams that we are in love with.”
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Headroom, which uses AI to supercharge videoconferencing, raises $5M
Videoconferencing has become a cornerstone of how many of us work these days — so much so that one leading service, Zoom, has graduated into verb status because of how much it’s getting used.
But does that mean videoconferencing works as well as it should? Today, a new startup called Headroom is coming out of stealth, tapping into a battery of AI tools — computer vision, natural language processing and more — on the belief that the answer to that question is a clear — no bad Wi-Fi interruption here — “no.”
Headroom not only hosts videoconferences, but then provides transcripts, summaries with highlights, gesture recognition, optimised video quality and more, and today it’s announcing that it has raised a seed round of $5 million as it gears up to launch its freemium service into the world.
You can sign up to the waitlist to pilot it, and get other updates here.
The funding is coming from Anna Patterson of Gradient Ventures (Google’s AI venture fund); Evan Nisselson of LDV Capital (a specialist VC backing companies building visual technologies); Yahoo founder Jerry Yang, now of AME Cloud Ventures; Ash Patel of Morado Ventures; Anthony Goldbloom, the co-founder and CEO of Kaggle.com; and Serge Belongie, Cornell Tech associate dean and professor of Computer Vision and Machine Learning.
It’s an interesting group of backers, but that might be because the founders themselves have a pretty illustrious background with years of experience using some of the most cutting-edge visual technologies to build other consumer and enterprise services.
Julian Green — a British transplant — was most recently at Google, where he ran the company’s computer vision products, including the Cloud Vision API that was launched under his watch. He came to Google by way of its acquisition of his previous startup Jetpac, which used deep learning and other AI tools to analyze photos to make travel recommendations. In a previous life, he was one of the co-founders of Houzz, another kind of platform that hinges on visual interactivity.
Russian-born Andrew Rabinovich, meanwhile, spent the last five years at Magic Leap, where he was the head of AI, and before that, the director of deep learning and the head of engineering. Before that, he too was at Google, as a software engineer specializing in computer vision and machine learning.
You might think that leaving their jobs to build an improved videoconferencing service was an opportunistic move, given the huge surge of use that the medium has had this year. Green, however, tells me that they came up with the idea and started building it at the end of 2019, when the term “COVID-19” didn’t even exist.
“But it certainly has made this a more interesting area,” he quipped, adding that it did make raising money significantly easier, too. (The round closed in July, he said.)
Given that Magic Leap had long been in limbo — AR and VR have proven to be incredibly tough to build businesses around, especially in the short to medium-term, even for a startup with hundreds of millions of dollars in VC backing — and could have probably used some more interesting ideas to pivot to; and that Google is Google, with everything tech having an endpoint in Mountain View, it’s also curious that the pair decided to strike out on their own to build Headroom rather than pitch building the tech at their respective previous employers.
Green said the reasons were two-fold. The first has to do with the efficiency of building something when you are small. “I enjoy moving at startup speed,” he said.
And the second has to do with the challenges of building things on legacy platforms versus fresh, from the ground up.
“Google can do anything it wants,” he replied when I asked why he didn’t think of bringing these ideas to the team working on Meet (or Hangouts if you’re a non-business user). “But to run real-time AI on video conferencing, you need to build for that from the start. We started with that assumption,” he said.
All the same, the reasons why Headroom are interesting are also likely going to be the ones that will pose big challenges for it. The new ubiquity (and our present lives working at home) might make us more open to using video calling, but for better or worse, we’re all also now pretty used to what we already use. And for many companies, they’ve now paid up as premium users to one service or another, so they may be reluctant to try out new and less-tested platforms.
But as we’ve seen in tech so many times, sometimes it pays to be a late mover, and the early movers are not always the winners.
The first iteration of Headroom will include features that will automatically take transcripts of the whole conversation, with the ability to use the video replay to edit the transcript if something has gone awry; offer a summary of the key points that are made during the call; and identify gestures to help shift the conversation.
And Green tells me that they are already also working on features that will be added into future iterations. When the videoconference uses supplementary presentation materials, those can also be processed by the engine for highlights and transcription too.
And another feature will optimize the pixels that you see for much better video quality, which should come in especially handy when you or the person/people you are talking to are on poor connections.
“You can understand where and what the pixels are in a video conference and send the right ones,” he explained. “Most of what you see of me and my background is not changing, so those don’t need to be sent all the time.”
All of this taps into some of the more interesting aspects of sophisticated computer vision and natural language algorithms. Creating a summary, for example, relies on technology that is able to suss out not just what you are saying, but what are the most important parts of what you or someone else is saying.
And if you’ve ever been on a videocall and found it hard to make it clear you’ve wanted to say something, without straight-out interrupting the speaker, you’ll understand why gestures might be very useful.
But they can also come in handy if a speaker wants to know if he or she is losing the attention of the audience: The same tech that Headroom is using to detect gestures for people keen to speak up can also be used to detect when they are getting bored or annoyed and pass that information on to the person doing the talking.
“It’s about helping with EQ,” he said, with what I’m sure was a little bit of his tongue in his cheek, but then again we were on a Google Meet, and I may have misread that.
And that brings us to why Headroom is tapping into an interesting opportunity. At their best, when they work, tools like these not only supercharge videoconferences, but they have the potential to solve some of the problems you may have come up against in face-to-face meetings, too. Building software that actually might be better than the “real thing” is one way of making sure that it can have staying power beyond the demands of our current circumstances (which hopefully won’t be permanent circumstances).
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Report: Apple quietly acquired Israel’s Camerai, formerly Tipit, a specialist in AR and camera tech
Apple is well known for picking up smaller startups on the hush-hush to augment its business, and today news leaked out about the latest of these… nearly two years after the fact. Sometime between 2018 and 2019, the iPhone giant reportedly acquired and shut down Camerai, an augmented reality and computer vision company based out of Israel, which used to be called Tipit.
The news was first reported earlier today by Israeli newspaper Calcalist, and we have reached out to ask Apple directly about it. In the meantime, Jonathan (Yehonatan) Rimon, who had been Camerai’s CEO and co-founded the company with Moty Kosharovsky, Erez Tal and Aaron Wetzler, declined to comment one way or the other on the report when we contacted him directly about it. A separate source confirmed the story to us. We’ll update as we learn more.
Calcalist said that the startup sold for several tens of millions of dollars. From being founded in 2015, Camerai had raised around $5 million — including a $2.5 million round in 2017 and another unreported $2.5 million in 2018 — with investors including the Atooro Fund and another called the SKO Fund.
It seems that the acquisition came on the heels of multiple approaches from a number of companies at a time when AR was arguably at a peak of hype and many big tech companies wanted a piece of the action. (Recall that 2018 was the year when Magic Leap raised nearly $1 billion in a single round of funding.) Back in 2018, we heard rumors that those approaching and looking at the startup included Apple, Samsung and Alibaba.
The Calcalist report said that Camerai employees joined Apple’s computer vision team, and that the company’s technology has been incorporated into Apple products already. It’s not clear specifically where and when, but recall that both iOS 13 and iOS 14 have featured big software updates to the camera.
Camerai had built an SDK and specifically a range of software-based AR tools to help edit and use camera-made images in more sophisticated ways,
Its tech included the ability to detect different objects in the picture, and outline them with precision to alter them cosmetically; the ability to outline and apply filters across the whole image; a “skeleton tracking” neural network API that could detect and draw body joints in real time overlaid on a picture of a human; and its own version of selective focus for enhanced portrait modes (remember this was 2018 and this was not standard on phones at the time). Camerai’s site is shut down, but here are some screenshots of how it all looked, pulled from the Internet Archive:
Camerai’s acquisition underscores a couple of interesting, and ongoing, trends.
The first of these is in the development of smartphone technology, particularly around cameras. Some of the more interesting innovations in smartphone camera technology have come not out of improvements in hardware, but software, where the application of breakthroughs in artificial intelligence can mean that an existing combination of sensor, lens and on-phone and cloud processors produce a better and more technically dynamic picture than before.
At a time when smartphone replacement cycles have really slowed down and we are seeing also slower innovation on hardware, bolting on talent and tech created outside the phone companies is one way to gain a competitive edge.
(Separately, I wonder if making cutting-edge technology software-based also means that there could be scope in the future for paid updates to older phone models, which could mean more incremental revenues from consumers that don’t want to invest incompletely new devices.)
The second trend that this deal underscores is how Israel remains fertile ground for bigger companies on the hunt to pick up and bolt on technology, and that the secretive approach is likely to remain for some time to come.
“In Israel there are over 350 global corporate companies, from 30 countries, who search for local innovation. Some of them like Apple, MS, Google, even have local R&D [operations],” said Avihai Michaeli, a Tel Aviv-based senior investment banker and startup advisor. “Those global companies look mainly for tech which could serve as its competitive edge. It is not the first time that an acquired startup is asked not to publish it was acquired, nor talk about it.”
Other acquisitions that Apple has made in Israel have included camera module maker LinX, semiconductor startup Anobit and 3D sensor company PrimeSense.
We’ll update this post as we learn more.
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Stockwell, the AI-vending machine startup formerly known as Bodega, is shutting down July 1
Stockwell AI entered the world with a bang but it is leaving with a whimper. Founded in 2017 by ex-Googlers, the AI vending machine startup formerly known as Bodega first raised blood pressures — people hated how it referenced and poorly ‘disrupted’ mom-and-pop shops in one fell swoop — and then raised a lot of money. But ultimately, it was no match for COVID-19 and the hit it has had on how we live.
TechCrunch has learned and confirmed that Stockwell will be shutting down at the end of this month, after it was unable find a viable business for its in-building app-controlled “smart” vending machines stocked with convenience store items.
“Regretfully, the current landscape has created a situation in which we can no longer continue our operations and will be winding down the company on July 1st,” co-founder and CEO Paul McDonald wrote in an email to TechCrunch. “We are deeply grateful to our talented team, incredible partners and investors, and our amazing shoppers that made this possible. While this wasn’t the way we wanted to end this journey, we are confident that our vision of bringing the store to where people live, work and play will live on through other amazing companies, products and services.”
We originally reached out after we were tipped off by someone who had received an email about the closure. Stockwell’s vending boxes were distributed primarily in apartment and office buildings, and it has been contacting those customers for the past week to break the news.
For what it’s worth, the building operator that was using Stockwell vending machines said it is actively in search of a replacement provider, so it seems it did get some use, but more pointedly it’s been very hard for the vending machine industry, where some distributors have seen business losses of up to 90%.
Stockwell’s closure is notable because it underscores how in the current climate, having a strong list of backers and a very decent amount of funding cannot always guarantee insulation for everyone.
As of last September, Stockwell had raised at least $45 million in funding from investors that included NEA, GV, DCM Ventures, Forerunner, First Round, and Homebrew. Its network had grown to 1,000 “stores”, smart vending machines that work a little like advanced hotel minibars: sensors detect and charge you for what you take out, and you use a smartphone app both to track what you buy and to pay for it.
As of last autumn, the company appeared to be gearing up for a widening of its business model, allowing its customers (building, office and apartment managers) to have a bigger say in what got stocked beyond the items Stockwell itself put into its machines, which included water and other beverages, savoury and sweet snacks, and a few home basics like laundry detergent and pain killers.
By December, it seems that McDonald’s co-founder, Ashwath Rajan, had quietly left the startup, and then as 2020 kicked into gear, COVID-19 took its toll.
First, consumers found themselves spending much more time working and simply being at home, going out less and bulk buying to minimise shopping efforts. That, in turn, had a big impact on the sustainability of business models based on casual, small purchases, such as the kind that one would typically make from vending machines like Stockwell’s.
Second, at a time when many are trying to minimise the spread of infection by wearing face masks, washing hands and minimising touching random objects, a big question mark hangs over the whole concept of unattended vending machines, and whether they can ever be properly sanitised. That’s impacted not only people buying items, but the workforce that’s meant to help stock and maintain these kiosks.
There have been some interesting twists in how the vending industry has handled COVID-19. Some are swapping out pretzels and Snickers and replacing them with PPE equipment, and others are finding opportunity in stocking them with healthy food specifically for front-line workers who have no other options and need quick but nutritious fixes during critical times.
But more generally, the vending machine industry has been hit hard by the pandemic.
The wider market in a normal year is estimated to be worth some $30 billion annually — one reason why Stockwell nee Bodega likely caught the eye of investors — but business has fallen off a cliff for many key operators.
The president of the European Vending Association, in an appeal in April to government leaders for financial assistance, said that business had dropped off by 90% and described COVID-19 as having a “devastating effect” on the sector. Difficult numbers for the Pepsi’s and Mondelez’s (nee Kraft) of the world, but surely the nail in the coffin for a young, promising AI-based vending machine startup that nonetheless some doubted from the word go.
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