Aug
15
2018
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To fight the scourge of open offices, ROOM sells rooms

Noisy open offices don’t foster collaboration, they kill it, according to a Harvard study that found the less-private floor plan led to a 73 percent drop in face-to-face interaction between employees and a rise in emailing. The problem is plenty of young companies and big corporations have already bought into the open office fad. But a new startup called ROOM is building a prefabricated, self-assembled solution. It’s the IKEA of office phone booths.

The $3,495 ROOM One is a sound-proofed, ventilated, powered booth that can be built in new or existing offices to give employees a place to take a video call or get some uninterrupted time to focus on work. For comparison, ROOM co-founder Morten Meisner-Jensen says, “Most phone booths are $8,000 to $12,000. The cheapest competitor to us is $6,000 — almost twice as much.” Though booths start at $4,500 from TalkBox and $3,995 from Zenbooth, they tack on $1,250 and $1,650 for shipping, while ROOM ships for free. They’re all dividing the market of dividing offices.

The idea might seem simple, but the booths could save businesses a ton of money on lost productivity, recruitment and retention if it keeps employees from going crazy amidst sales call cacophony. Less than a year after launch, ROOM has hit a $10 million revenue run rate thanks to 200 clients ranging from startups to Salesforce, Nike, NASA and JP Morgan. That’s attracted a $2 million seed round from Slow Ventures that adds to angel funding from Flexport CEO Ryan Petersen. “I am really excited about it since it is probably the largest revenue-generating company Slow has seen at the time of our initial Seed stage investment,” says partner Kevin Colleran.

“It’s not called ROOM because we build rooms,” Meisner-Jensen tells me. “It’s called ROOM because we want to make room for people, make room for privacy and make room for a better work environment.”

Phone booths, not sweatboxes

You might be asking yourself, enterprising reader, why you couldn’t just go to Home Depot, buy some supplies and build your own in-office phone booth for way less than $3,500. Well, ROOM’s co-founders tried that. The result was… moist.

Meisner-Jensen has design experience from the Danish digital agency Revolt that he started before co-founding digital book service Mofibo and selling it to Storytel. “In my old job we had to go outside and take the call, and I’m from Copenhagen, so that’s a pretty cold experience half the year.” His co-founder Brian Chen started Y Combinator-backed smart suitcase company Bluesmart, where he was VP of operations. They figured they could attack the office layout issue with hammers and saws. I mean, they do look like superhero alter-egos.

Room co-founders (from left): Brian Chen and Morten Meisner-Jensen

“To combat the issues I myself would personally encounter with open offices, as well as colleagues, we tried to build a private ‘phone booth’ ourselves,” says Meisner-Jensen. “We didn’t quite understand the specifics of air ventilation or acoustics at the time, so the booth got quite warm — warm enough that we coined it ‘the sweatbox.’ ”

With ROOM, they got serious about the product. The 10-square-foot ROOM One booth ships flat and can be assembled in less than 30 minutes by two people with a hex wrench. All it needs is an outlet to power its light and ventilation fan. Each is built from 1088 recycled plastic bottles for noise cancelling, so you’re not supposed to hear anything from outside. The box is 100 percent recyclable, plus it can be torn down and rebuilt if your startup implodes and you’re being evicted from your office.

The ROOM One features a bar-height desk with outlets and a magnetic bulletin board behind it, though you’ll have to provide your own stool. It’s actually designed not to be so comfy that you end up napping inside, which doesn’t seem like it’d be a problem with this somewhat cramped spot. “To solve the problem with noise at scale you want to provide people with space to take a call but not camp out all day,” Meisner-Jensen notes.

Booths by Zenbooth, Cubicall and TalkBox (from left)

A place to get into flow

Couldn’t office managers just buy noise-cancelling headphones for everyone? “It feels claustrophobic to me,” he laughs, but then outlines why a new workplace trend requires more than headphones. “People are doing video calls and virtual meetings much, much more. You can’t have all these people walking by you and looking at your screen. [A booth is] also giving you your own space to do your own work, which I don’t think you’d get from a pair of Bose. I think it has to be a physical space.”

But with plenty of companies able to construct physical spaces, it will be a challenge for ROOM to convey the subtleties of its build quality that warrant its price. “The biggest risk for ROOM right now are copycats,” Meisner-Jensen admits. “Someone entering our space claiming to do what we’re doing better but cheaper.” Alternatively, ROOM could lock in customers by offering a range of office furniture products. The co-founder hinted at future products, saying ROOM is already receiving demand for bigger multi-person prefab conference rooms and creative room divider solutions.

The importance of privacy goes beyond improved productivity when workers are alone. If they’re exhausted from overstimulation in a chaotic open office, they’ll have less energy for purposeful collaboration when the time comes. The bustle could also make them reluctant to socialize in off-hours, which could lead them to burn out and change jobs faster. Tech companies in particular are in a constant war for talent, and ROOM Ones could be perceived as a bigger perk than free snacks or a ping-pong table that only makes the office louder.

“I don’t think the solution is to go back to a world of cubicles and corner offices,” Meisner-Jensen concludes. It could take another decade for office architects to correct the overenthusiasm for open offices despite the research suggesting their harm. For now, ROOM’s co-founder is concentrating on “solving the issue of noise at scale” by asking, “How do we make the current workspaces work in the best way possible?”

Aug
09
2018
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Dropbox is crashing despite beating Wall Street expectations, announces COO Dennis Woodside is leaving

Back when Dennis Woodside joined Dropbox as its chief operating officer more than four years ago, the company was trying to justify the $10 billion valuation it had hit in its rapid rise as a Web 2.0 darling. Now, Dropbox is a public company with a nearly $14 billion valuation, and it once again showed Wall Street that it’s able to beat expectations with a now more robust enterprise business alongside its consumer roots.

Dropbox’s second quarter results came in ahead of Wall Street’s expectations on both the earnings and revenue front. The company also announced that Dennis Woodside will be leaving the company. Woodside joined at a time when Dropbox was starting to figure out its enterprise business, which it was able to grow and transform into a strong case for Wall Street that it could finally be a successful publicly traded company. The IPO was indeed successful, with the company’s shares soaring more than 40 percent in its debut, so it makes sense that Woodside has essentially accomplished his job by getting it into a business ready for Wall Street.

“I think as a team we accomplished a ton over the last four and a half years,” Woodside said in an interview. “When I joined they were a couple hundred million in revenue and a little under 500 people. [CEO] Drew [Houston] and Arash [Ferdowsi] have built a great business, since then we’ve scaled globally. Close to half our revenue is outside the U.S., we have well over 300,000 teams for our Dropbox business product, which was nascent there. These are accomplishments of the team, and I’m pretty proud.”

The stock initially exploded in extended trading by rising more than 7 percent, though even prior to the market close and the company reporting its earnings, the stock had risen as much as 10 percent. But following that spike, Dropbox shares are now down around 5 percent. Dropbox is one of a number of SaaS companies that have gone public in recent months, including DocuSign, that have seen considerable success. While Dropbox has managed to make its case with a strong enterprise business, the company was born with consumer roots and has tried to carry over that simplicity with the enterprise products it rolls out, like its collaboration tool Dropbox Paper.

Here’s a quick rundown of the numbers:

  • Q2 Revenue: Up 27 percent year-over-year to $339.2 million, compared to estimates of $331 million in revenue.
  • Q2 GAAP Gross Margin: 73.6 percent, as compared to 65.4 percent in the same period last year.
  • Q2 adjusted earnings: 11 cents per share compared, compared to estimates of 7 cents per share.
  • Paid users: 11.9 million paying users, up from 9.9 million in the same quarter last year.
  • ARPU: $116.66, compared to $111.19 same quarter last year.

So, not only is Dropbox able to show that it can continue to grow that revenue, the actual value of its users is also going up. That’s important, because Dropbox has to show that it can continue to acquire higher-value customers — meaning it’s gradually moving up the Fortune 100 chain and getting larger and more established companies on board that can offer it bigger and bigger contracts. It also gives it the room to make larger strategic moves, like migrating onto its own architecture late last year, which, in the long run could turn out to drastically improve the margins on its business.

“We did talk earlier in the quarter about our investment over the last couple years in SMR technology, an innovative storage technology that allows us to optimize cost and performance,” Woodside said. “We continue to innovate ways that allow us to drive better performance, and that drives better economics.”

The company is still looking to make significant moves in the form of new hires, including recently announcing that it has a new VP of product and VP of product marketing, Adam Nash and Naman Khan, respectively. Dropbox’s new team under CEO Drew Houston are tasked with continuing the company’s path to cracking into larger enterprises, which can give it a much more predictable and robust business alongside the average consumers that pay to host their files online and access them from pretty much anywhere.

In addition, there are a couple executive changes as Woodside transitions out. Yamini Rangan, currently VP of Business Strategy & Operations, will become Chief Customer Officer reporting to Houston, and comms VP Lin-Hua Wu will also report to Houston.

Dropbox had its first quarterly earnings check-in and slid past the expectations that Wall Street had, though its GAAP gross margin slipped a little bit and may have offered a slight negative signal for the company. But since then, Dropbox’s stock hasn’t had any major missteps, giving it more credibility on the public markets — and more resources to attract and retain talent with compensation packages linked to that stock.

“Our retention has been quite strong,” Woodside said. “We see strong retention characteristics across the customer set we have, whether it’s large or small. Obviously larger companies have more opportunity to expand over time, so our expansion metrics are quite strong in customers of over several hundred employees. But even among small businesses, Dropbox is the kind of product that has gravity. Once you start using it and start sharing it, it becomes a place where your business is small or large is managing all its content, it tends to be a sticky experience.”

Jul
30
2018
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A pickaxe for the AI gold rush, Labelbox sells training data software

Every artificial intelligence startup or corporate R&D lab has to reinvent the wheel when it comes to how humans annotate training data to teach algorithms what to look for. Whether it’s doctors assessing the size of cancer from a scan or drivers circling street signs in self-driving car footage, all this labeling has to happen somewhere. Often that means wasting six months and as much as a million dollars just developing a training data system. With nearly every type of business racing to adopt AI, that spend in cash and time adds up.

Labelbox builds artificial intelligence training data labeling software so nobody else has to. What Salesforce is to a sales team, Labelbox is to an AI engineering team. The software-as-a-service acts as the interface for human experts or crowdsourced labor to instruct computers how to spot relevant signals in data by themselves and continuously improve their algorithms’ accuracy.

Today, Labelbox is emerging from six months in stealth with a $3.9 million seed round led by Kleiner Perkins and joined by First Round and Google’s Gradient Ventures.

“There haven’t been seamless tools to allow AI teams to transfer institutional knowledge from their brains to software,” says co-founder Manu Sharma. “Now we have over 5,000 customers, and many big companies have replaced their own internal tools with Labelbox.”

Kleiner’s Ilya Fushman explains that “If you have these tools, you can ramp up to the AI curve much faster, allowing companies to realize the dream of AI.”

Inventing the best wheel

Sharma knew how annoying it was to try to forge training data systems from scratch because he’d seen it done before at Planet Labs, a satellite imaging startup. “One of the things that I observed was that Planet Labs has a superb AI team, but that team had been for over six months building labeling and training tools. Is this really how teams around the world are approaching building AI?,” he wondered.

Before that, he’d worked at DroneDeploy alongside Labelbox co-founder and CTO Daniel Rasmuson, who was leading the aerial data startup’s developer platform. “Many drone analytics companies that were also building AI were going through the same pain point,” Sharma tells me. In September, the two began to explore the idea and found that 20 other companies big and small were also burning talent and capital on the problem. “We thought we could make that much smarter so AI teams can focus on algorithms,” Sharma decided.

Labelbox’s team, with co-founders Ysiad Ferreiras (third from left), Manu Sharma (fourth from left), Brian Rieger (sixth from left) Daniel Rasmuson (seventh from left)

Labelbox launched its early alpha in January and saw swift pickup from the AI community that immediately asked for additional features. With time, the tool expanded with more and more ways to manually annotate data, from gradation levels like how sick a cow is for judging its milk production to matching systems like whether a dress fits a fashion brand’s aesthetic. Rigorous data science is applied to weed out discrepancies between reviewers’ decisions and identify edge cases that don’t fit the models.

“There are all these research studies about how to make training data” that Labelbox analyzes and applies, says co-founder and COO Ysiad Ferreiras, who’d led all of sales and revenue at fast-rising grassroots campaign texting startup Hustle. “We can let people tweak different settings so they can run their own machine learning program the way they want to, instead of being limited by what they can build really quickly.” When Norway mandated all citizens get colon cancer screenings, it had to build AI for recognizing polyps. Instead of spending half a year creating the training tool, they just signed up all the doctors on Labelbox.

Any organization can try Labelbox for free, and Ferreiras claims hundreds have. Once they hit a usage threshold, the startup works with them on appropriate SaaS pricing related to the revenue the client’s AI will generate. One called Lytx makes DriveCam, a system installed on half a million trucks with cameras that use AI to detect unsafe driver behavior so they can be coached to improve. Conde Nast is using Labelbox to match runway fashion to related items in their archive of content.

Eliminating redundancy, and jobs?

The big challenge is convincing companies that they’re better off leaving the training software to the experts instead of building it in-house where they’re intimately, though perhaps inefficiently, involved in every step of development. Some turn to crowdsourcing agencies like CrowdFlower, which has their own training data interface, but they only work with generalist labor, not the experts required for many fields. Labelbox wants to cooperate rather than compete here, serving as the management software that treats outsourcers as just another data input.

Long-term, the risk for Labelbox is that it’s arrived too early for the AI revolution. Most potential corporate customers are still in the R&D phase around AI, not at scaled deployment into real-world products. The big business isn’t selling the labeling software. That’s just the start. Labelbox wants to continuously manage the fine-tuning data to help optimize an algorithm through its entire life cycle. That requires AI being part of the actual engineering process. Right now it’s often stuck as an experiment in the lab. “We’re not concerned about our ability to build the tool to do that. Our concern is ‘will the industry get there fast enough?’” Ferreiras declares.

Their investor agrees. Last year’s big joke in venture capital was that suddenly you couldn’t hear a startup pitch without “AI” being referenced. “There was a big wave where everything was AI. I think at this point it’s almost a bit implied,” says Fushman. But it’s corporations that already have plenty of data, and plenty of human jobs to obfuscate, that are Labelbox’s opportunity. “The bigger question is ‘when does that [AI] reality reach consumers, not just from the Googles and Amazons of the world, but the mainstream corporations?’”

Labelbox is willing to wait it out, or better yet, accelerate that arrival — even if it means eliminating jobs. That’s because the team believes the benefits to humanity will outweigh the transition troubles.

“For a colonoscopy or mammogram, you only have a certain number of people in the world who can do that. That limits how many of those can be performed. In the future, that could only be limited by the computational power provided so it could be exponentially cheaper” says co-founder Brian Rieger. With Labelbox, tens of thousands of radiology exams can be quickly ingested to produce cancer-spotting algorithms that he says studies show can become more accurate than humans. Employment might get tougher to find, but hopefully life will get easier and cheaper too. Meanwhile, improving underwater pipeline inspections could protect the environment from its biggest threat: us.

“AI can solve such important problems in our society,” Sharma concludes. “We want to accelerate that by helping companies tell AI what to learn.”

Jul
26
2018
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Facebook acquires Redkix to enhance communications on Workplace by Facebook

Facebook had a rough day yesterday when its stock plunged after a poor earnings report. What better way to pick yourself up and dust yourself off than to buy a little something for yourself. Today the company announced it has acquired Redkix, a startup that provides tools to communicate more effectively by combining email with a more formal collaboration tool. The companies did not reveal the acquisition price.

Redkix burst out of the gate two years ago with a $17 million seed round, a hefty seed amount by any measure. What prompted this kind of investment was a tool that combined a collaboration tool like Slack or Workplace by Facebook with email. People could collaborate in Redkix itself, or if you weren’t a registered user, you could still participate by email, providing a more seamless way to work together.

Alan Lepofsky, who covers enterprise collaboration at Constellation Research, sees this tool as providing a key missing link. “Redkix is a great solution for bridging the worlds between traditional email messaging and more modern conversational messaging. Not all enterprises are ready to simply switch from one to the other, and Redkix allows for users to work in whichever method they want, seamlessly communicating with the other,” Lepofsky told TechCrunch.

As is often the case with these kinds of acquisitions, the company bought the technology  itself along with the team that created it. This means that the Redkix team including the CEO and CTO will join Facebook and they will very likely be shutting down the application after the acquisition is finalized.

Lepofsky thinks that enterprises that are adopting Facebook’s enterprise tool will be able to more seamlessly transition between the two modes of communication, the Workplace by Facebook tool and email, as they prefer.

Although a deal like this has probably been in the works for some time, after yesterday’s earning’s debacle, Facebook could be looking for ways to enhance its revenue in areas beyond the core Facebook platform. The enterprise collaboration tool does offer a possible way to do that in the future, and if they can find a way to incorporate email into it, it could make it a more attractive and broader offering.

Facebook is competing with Slack, the darling of this space and others like Microsoft, Cisco and Google around communications and collaboration. When it launched in 2015, it was trying to take that core Facebook product and put it in a business context, something Slack had been doing since the beginning.

To succeed in business, Facebook had to think differently than as a consumer tool, driven by advertising revenue and had to convince large organizations that they understood their requirements. Today, Facebook claims 30,000 organizations are using the tool and over time they have built in integrations to other key enterprise products, and keep enhancing it.

Perhaps with today’s acquisition, they can offer a more flexible way to interact with the platform and could increase those numbers over time.

Jul
23
2018
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Cogito scores $37M as AI-driven sentiment analysis biz grows

Cogito announced a $37 million Series C investment today led by Goldman Sachs Growth Equity. Previous investors Salesforce Ventures and OpenView also chipped in. Mark Midle of Goldman Sachs’ Merchant Banking Division, has joined Cogito’s Board of Directors

The company has raised over $64 million since it emerged from the MIT Human Dynamics Lab back in 2007 trying to use the artificial intelligence technology available at the time to understand sentiment and apply it in a business context.

While it took some time for the technology to catch up with the vision, and find the right use case, company CEO and founder Joshua Feast says today they are helping customer service representatives understand the sentiment and emotional context of the person on the line and give them behavioral cues on how to proceed.

“We sell software to very large software, premium brands with many thousands of people in contact centers. The purpose of our solution is to help provide a really wonderful service experience in moments of truth,” he explained. Anyone who deals with a large company’s customer service has likely felt there is sometimes a disconnect between the person on the phone and their ability to understand your predicament and solve your problem.

Cogito in action giving customer service reps real-time feedback.

He says using his company’s solution, which analyzes the contents of the call in real time, and provides relevant feedback, the goal is to not just complete the service call, but to leave the customer feeling good about the brand and the experience. Certainly a bad experience can have the opposite effect.

He wants to use technology to make the experience a more human interaction and he recognizes that as an organization grows, layers of business process make it harder for the customer service representative to convey that humanity. Feast believes that technology has helped create this problem and it can help solve it too.

While the company is not talking about valuation or specific revenue at this point, Feast reports that revenue has grown 3X over the last year. Among their customers are Humana and Metlife, two large insurance companies, each with thousands of customer service agents.

Cogito is based in downtown Boston with 117 employees at last count, and of course they hope to use the money to add on to that number and help scale this vision further.

“This is about scaling our organization to meet client’s needs. It’s also about deepening what we do. In a lot of ways, we are only scratching the surface [of the underlying technology] in terms of how we can use AI to support emotional connections and help organizations be more human,” Feast said.

Jul
18
2018
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Okta nabs ScaleFT to build out ‘Zero Trust’ security framework

Okta, the cloud identity management company, announced today it has purchased a startup called ScaleFT to bring the Zero Trust concept to the Okta platform. Terms of the deal were not disclosed.

While Zero Trust isn’t exactly new to a cloud identity management company like Okta, acquiring ScaleFT gives them a solid cloud-based Zero Trust foundation on which to continue to develop the concept internally.

“To help our customers increase security while also meeting the demands of the modern workforce, we’re acquiring ScaleFT to further our contextual access management vision — and ensure the right people get access to the right resources for the shortest amount of time,” Okta co-founder and COO Frederic Kerrest said in a statement.

Zero Trust is a security framework that acknowledges work no longer happens behind the friendly confines of a firewall. In the old days before mobile and cloud, you could be pretty certain that anyone on your corporate network had the authority to be there, but as we have moved into a mobile world, it’s no longer a simple matter to defend a perimeter when there is effectively no such thing. Zero Trust means what it says: you can’t trust anyone on your systems and have to provide an appropriate security posture.

The idea was pioneered by Google’s “BeyondCorp” principals and the founders of ScaleFT are adherents to this idea. According to Okta, “ScaleFT developed a cloud-native Zero Trust access management solution that makes it easier to secure access to company resources without the need for a traditional VPN.”

Okta wants to incorporate the ScaleFT team and, well, scale their solution for large enterprise customers interested in developing this concept, according to a company blog post by Kerrest.

“Together, we’ll work to bring Zero Trust to the enterprise by providing organizations with a framework to protect sensitive data, without compromising on experience. Okta and ScaleFT will deliver next-generation continuous authentication capabilities to secure server access — from cloud to ground,” Kerrest wrote in the blog post.

ScaleFT CEO and co-founder Jason Luce will manage the transition between the two companies, while CTO and co-founder Paul Querna will lead strategy and execution of Okta’s Zero Trust architecture. CSO Marc Rogers will take on the role of Okta’s Executive Director, Cybersecurity Strategy.

The acquisition allows the Okta to move beyond purely managing identity into broader cyber security, at least conceptually. Certainly Roger’s new role suggests the company could have other ideas to expand further into general cyber security beyond Zero Trust.

ScaleFT was founded in 2015 and has raised $2.8 million over two seed rounds, according to Crunchbase data.

Jul
18
2018
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Swim.ai raises $10M to bring real-time analytics to the edge

Once upon a time, it looked like cloud-based serviced would become the central hub for analyzing all IoT data. But it didn’t quite turn out that way because most IoT solutions simply generate too much data to do this effectively and the round-trip to the data center doesn’t work for applications that have to react in real time. Hence the advent of edge computing, which is spawning its own ecosystem of startups.

Among those is Swim.ai, which today announced that it has raised a $10 million Series B funding round led by Cambridge Innovation Capital, with participation from Silver Creek Ventures and Harris Barton Asset Management. The round also included a strategic investment from Arm, the chip design firm you may still remember as ARM (but don’t write it like that or their PR department will promptly email you). This brings the company’s total funding to about $18 million.

Swim.ai has an interesting take on edge computing. The company’s SWIM EDX product combines both local data processing and analytics with local machine learning. In a traditional approach, the edge devices collect the data, maybe perform some basic operations against the data to bring down the bandwidth cost and then ship it to the cloud where the hard work is done and where, if you are doing machine learning, the models are trained. Swim.ai argues that this doesn’t work for applications that need to respond in real time. Swim.ai, however, performs the model training on the edge device itself by pulling in data from all connected devices. It then builds a digital twin for each one of these devices and uses that to self-train its models based on this data.

“Demand for the EDX software is rapidly increasing, driven by our software’s unique ability to analyze and reduce data, share new insights instantly peer-to-peer – locally at the ‘edge’ on existing equipment. Efficiently processing edge data and enabling insights to be easily created and delivered with the lowest latency are critical needs for any organization,” said Rusty Cumpston, co-founder and CEO of Swim.ai. “We are thrilled to partner with our new and existing investors who share our vision and look forward to shaping the future of real-time analytics at the edge.”

The company doesn’t disclose any current customers, but it is focusing its efforts on manufacturers, service providers and smart city solutions. Update: Swim.ai did tell us about two customers after we published this story: The City of Palo Alto and Itron.

Swim.ai plans to use its new funding to launch a new R&D center in Cambridge, UK, expand its product development team and tackle new verticals and geographies with an expanded sales and marketing team.

Jul
17
2018
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Standard Cognition raises another $5.5M to create a cashier-less checkout experience

As Amazon looks to increasingly expand its cashier-less grocery stories — called Amazon Go – across different regions, there’s at least one startup hoping to end up everywhere else beyond Amazon’s empire.

Standard Cognition aims to help businesses create that kind of checkout experience based on machine vision, using image recognition to figure out that a specific person is picking up and walking out the door with a bag of Cheetos. The company said it’s raised an additional $5.5 million in a round in what the company is calling a seed round extension from CRV. The play here is, like many startups, to create something that a massive company is going after — like image recognition for cashier-less checkouts — for the long tail businesses rather than locking them into a single ecosystem.

Standard Cognition works with security cameras that have a bit more power than typical cameras to identify people that walk into a store. Those customers use an app, and the camera identifies everything they are carrying and bills them as they exit the store. The company has said it works to anonymize that data, so there isn’t any kind of product tracking that might chase you around the Internet that you might find on other platforms.

“The platform is built at this point – we are now focused on releasing the platform to each retail partner that signs on with us,” Michael Suswal, Co-founder and COO said. “Most of the surprises coming our way come from learning about how each retailer prefers to run their operations and store experiences. They are all a little different and require us to be flexible with how we deploy.”

It’s a toolkit that makes sense for both larger and smaller retailers, especially as the actual technology to install cameras or other devices that can get high-quality video or have more processing power goes down over time. Baking that into smaller retailers or mom-and-pop stores could help them get more foot traffic or make it easier to keep tabs on what kind of inventory is most popular or selling out more quickly. It offers an opportunity to have an added layer of data about how their store works, which could be increasingly important over time as something like Amazon looks to start taking over the grocery experience with stores like Amazon Go or its massive acquisition of Whole Foods.

“While we save no personal data in the cloud, and the system is built for privacy (no facial recognition among other safety features that come with being a non-cloud solution), we do use the internet for a couple of things,” Suswal said. “One of those things is to update our models and push them fleet wide. This is not a data push. It is light and allows us to make updates to models and add new features. We refer to it as the Tesla model, inspired by the way a driver can have a new feature when they wake up in the morning. We are also able to offer cross-store analytics to the retailer using the cloud, but no personal data is ever stored there.”

It’s thanks to advances in machine learning — and the frameworks and hardware that support it — that have made this kind of technology easier to build for smaller companies. Already there are other companies that look to be third-party providers for popular applications like voice recognition (think SoundHound) or machine vision (think Clarifai). All of those aim to be an option outside of whatever options larger companies might have like Alexa. It also means there is probably going to be a land grab and that there will be other interpretations of what the cashier-less checkout experience looks like, but Standard Cognition is hoping it’ll be able to get into enough stores to be an actual challenger to Amazon Go.

Jul
16
2018
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Fastly raises another $40 million before an IPO

Last round before the IPO. That’s how Fastly frames its new $40 million Series F round. It means that the company has raised $219 million over the past few years.

The funding round was led by Deutsche Telekom Capital Partners with participation from Sozo Ventures, Swisscom Ventures, and existing investors.

Fastly operates a content delivery network to speed up web requests. Let’s say you type nytimes.com in your browser. In the early days of the internet, your computer would send a request to one of The New York Times’ servers in a data center. The server would receive the request and send back the page to the reader.

But the web has grown immensely, and this kind of architecture is no longer sustainable. The New York Times use Fastly to cache its homepage, media and articles on Fastly’s servers. This way, when somebody types nytimes.com, Fastly already has the webpage on its servers and can send it directly. For some customers, it can represent as much as 90 percent of requests.

Scale and availability are one of the benefits of using a content delivery network. But speed is also another one. Even though the web is a digital platform, it’s very physical by nature. When you load a page on a server on the other side of the world, it’s going to take hundreds of milliseconds to get the page. Over time, this latency adds up and it feels like a sluggish experience.

Fastly has data centers and servers all around the world so that you can load content in less than 20 or 30 milliseconds. This is particularly important for Stripe or Ticketmaster as response time can greatly influence an e-commerce purchase.

Fastly’s platform also provides additional benefits, such as DDoS mitigation and web application firewall. One of the main challenges for the platform is being able to cache content as quickly as possible. Users upload photos and videos all the time, so it should be on Fastly’s servers within seconds.

The company has tripled its customer base over the past three years. It had a $100 million revenue run rate in 2017. Customers now include Reddit, GitHub, Stripe, Ticketmaster and Pinterest.

There are now 400 employees working for Fastly. It’s worth noting that women represent 42 percent of the executive team, and 65 percent of the engineering leads are women, people of color or LGBTQ (or the intersection of those categories). And if you haven’t read all the diversity reports from tech companies, those are great numbers.

Jul
12
2018
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Spring Health raises $6M to help employees get access to personalized mental health treatment

In recent months, we’ve seen more and more funding flowing into tools for mental wellness — whether that’s AI-driven tools to help patients find help to meditation apps — and it seems like that trend is starting to pick up even more steam as smaller companies are grabbing the attention of investors.

There’s another one picking up funding today in Spring Health, a platform for smaller companies to help their employees get more access to mental health treatment. The startup looks to give employers get access to a simple, effective way to start offering that treatment for their employees in the form of personalized mental wellness plans. The employees get access to confidential plans in addition to access to a network and ways to get in touch with a therapist or psychiatrist as quickly as possible. The company said it has raised an additional $6 million in funding led by Rethink Impact, with Work-Bench, BBG Ventures, and The Partnership Fund for New York City joining the round. RRE Ventures and the William K. Warren Foundation also participated.

“…I realized that mental health care is largely a guessing game: you use trial-and-error to find a compatible therapist, and you use trial-and-error to find the right treatment regimen, whether that’s a specific cocktail of medications or a specific type of psychotherapy,” CEO and co-founder April Koh said. “Everything around us is personalized these days – like shopping on Amazon, search results on Google, and restaurant recommendations on Yelp – but you can’t get personalized recommendations for your mental health care. I wanted to build a platform that connects you with the right care for you from the very beginning. So I partnered with leading expert on personalized psychiatry, Dr. Adam Chekroud our Chief Scientist, and my friend Abhishek Chandra, our CTO, to start Spring Health.”

The startup bills itself as an online mental health clinic that offers recommendations for employees, such as treatment options or tweaks to their daily routines (like exercise regimens). Like other machine learning-driven platforms, Spring Health puts a questionnaire in front of the end employee that adapts to the responses they are giving and then generates a wellness plan for that specific individual. As more and more patients get on the service, it gets more data, and can improve those recommendations over time. Those patients are then matched with clinicians and licensed medical health professionals from the company’s network.

“We found that employers were asking for it,” Koh said. “As a company we started off by selling an AI-enabled clinical decision support tool to health systems to empower their doctors to make data-driven decisions. While selling that tool to one big health system, word reached their benefits department, and they reached out to us and told us they need something in benefits to deal with mental health needs of their employee base. When that happened, we decided to completely focus on selling a “full-stack” mental health solution to employers for their employees. Instead of selling a tool to doctors, we decided we would create our own network of best-in-class mental health providers who would use our tools to deliver the best mental health care possible.”

However, Spring Health isn’t the only startup looking to create an intelligent matching system for employees seeking mental health. Lyra Health, another tool to help employees securely and confidentially begin the process of getting mental health treatment, raised $45 million in May this year. But Spring Health and Lyra Health are both part of a wave of startups looking to create ways for employees to more efficiently seek care powered by machine learning and capitalizing on the cost and difficulty of those tools dropping dramatically.

And it’s not the only service in the mental wellness category also picking up traction, with meditation app Calm raising $27 million at a $250 million valuation. Employers naturally have a stake in the health of their employees, and as all these apps look to make getting mental health treatment or improving mental wellness easier — and less of a taboo — the hope is they’ll continue to lower the barrier to entry, both from the actual product inertia and getting people comfortable with seeking help in the first place.

“I think VC’s are realizing there’s a huge opportunity to disrupt mental health care and make it accessible, convenient and affordable. But from our perspective, the problem with the space is that there is a lot of unvetted, non-evidence-based technology. There’s a ton of vaporware surrounding AI, big data, and machine-learning, especially in mental health care. We want to set a higher standard in mental healthcare that is based on evidence and clinical validation. Unlike most mental health care solutions on the market, we have multiple peer-reviewed publications in top medical journals like JAMA, describing and substantiating our technology. We know that our personalized recommendations and our Care Navigation approach are evidence-based and proven to work.

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