Fundings & Exits
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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.
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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.
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Ultimate Software is acquiring PeopleDoc for $300 million
Public company Ultimate Software is acquiring French startup PeopleDoc for $300 million in cash and stock. The transaction is expected to close in the third quarter of 2018. These two companies both make HR solutions.
Ultimate Software has been around for a while. It went public in 1998 and switched to a software-as-a-service solution in 2002 — this solution is called UltiPro. It lets you manage all things HR, from payroll to benefits, time management, onboarding, performance management and more.
PeopleDoc is a younger French startup that has raised over $50 million. As the name suggests, PeopleDoc lets you centralized all HR documents related to you in a single location. They can come from multiple sources and systems, they’ll all be there.
The startup has also worked on an onboarding solution and other tools to automate HR processes as much as possible. For instance, you can use PeopleDoc to communicate with the HR team and notify them of a change.
Ultimate Software has around 4,100 customers, which represent around 38 million employees. So it’s clear that the company is going after big clients. Each customer employs 9,200 people on average.
PeopleDoc has a thousand customers and serves 4 million employees. While PeopleDoc is significantly smaller than Ultimate Software, it’s a notable acquisition for the startup.
Ultimate Software says that it plans to spend $75 million in cash when the acquisition closes. PeopleDoc shareholders will receive another $50 million a year later.
Finally, Ultimate Software is spending around $175 million in stock for the rest of the acquisition. The company has been doing incredibly well on the stock market, consistently going up over the past ten years.
There are two reasons behind the acquisition. First, Ultimate Software has been mostly focused on American customers. With today’s acquisition, Ultimate Software will be able to convince new international customers, particularly in Europe.
Second, PeopleDoc will continue to operate as a subsidiary as these two companies don’t exactly do the same thing. In fact, Ultimate Software will start distributing PeopleDoc’s services to its own customers next year.
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An app that uses AI to help you improve your basketball shot just raised $4 million
Let’s be real: you are most certainly never going to be as good as Steve Nash, Chris Paul, James Harden — or really any professional NBA player. But it probably won’t stop you from trying to practice or model your game around your favorite players, and spend hours upon hours figuring out how to get better.
And while there are going to be plenty of attempts to smash image recognition and AI into that problem, a company called NEX Team is hoping to soften the blow a bit by helping casual players figure out their game, rather than trying to be as good as a professional NBA player. Using phone cameras and image recognition on the back end, its primary app HomeCourt will measure a variety of variables like shot trajectory, jump height, and body position, and help understand how to improve a player’s shooting form. It’s not designed to help that player shoot like Ray Allen, but at least start hitting those mid-range jumpers. The company said it’s raised $4 million from Charmides Capital and Mandra Capital, as well as Steve Nash, Jeremy Lin, Sam “Trust The Process” Hinkie (sigh), Mark Cuban and Dani Reiss.
“We don’t call ourselves a basketball company, we think of ourselves as a mobile AI company,” CEO and co-founder David Lee said. “It happens that basketball is the first sport where we’re applying our tech. When you think about digitizing sports, as a runner or cyclist, you’ve had access to a feedback loop for a while [on treadmills and other tools]. But for basketball and other sports like basketball, that loop didn’t exist. We believed with computer vision, you can digitize a lot of different sports, one of which is basketball. We’re not just building an app for the professional basketball athletes, we’re focused on building an app where value can be generated across the basketball community.”
The app starts off with an iPhone. Players can boot up their camera and begin recording their shots, and the app will go back and track what worked and what didn’t work with that shot, as well as where the player is making and missing those shots.It’s not tracking every single motion of the player, but once a player makes a shot, it will track that trajectory and shooting form, like where his or her feet are planted. That kind of feedback can help players understand the kinds of small tweaks they can make to improve their shooting percentage over time, such as release speed or jump hight. And while it’s not designed to be hugely robust like the kinds of advanced tracking technology that show up in advanced training facilities at some larger sports franchises, it aims to be a plug-and-play way of getting feedback on a player’s game right away.
Still, that doesn’t necessarily stop the app from showing up in slightly more professional situations, like recruiting or in athletic centers on college campuses, Lee said. Each college is looking for the next DeAndre Ayton or Ben Simmons, as well as new ways to try to find those recruits. While not every college will end up with the top recruits in the country and get bounced in the second round of the NCAA Men’s Basketball tournament, it offers an additional way for younger players to refine their game to the point they potentially get the attention of those universities — or the NBA, should the one-and-done rule that requires athletes to play a year in college end up disappearing.
“A lot of these coaches are looking at a lot of evaluation tools,” Lee said. “If Alex is waking up at 5 a.m. to put in work, it’s not just about makes and misses, it’s about work ethic. It’s harder to evaluate and digitize a sport. Only [a fraction] of the basketball happens in their practice facilities. How do they help their players evaluate their workout sessions when they’re in those situations? That opens up the doors to do that as well.”
In order to appeal to those broader audiences, the startup is rolling out bite-sized challenges as a way to try to attract the more casual consumers that want to dip their toes into HomeCourt. You see these kinds of challenge-based activities in apps like Strava as a way to try to attract users or keep them engaged in a lighter and more competitive way without having to go into a full-on event like a race or a tournament. It’s one way to try to wrangle the competitive elements of sports like basketball without a ton of competitive pressure as users get more and more comfortable with the way they play and their shooting style.
That bite-sized style of activity also serves pretty well when it comes to creating content, as has been proven popular by apps like Overtime that specialize in highlights of certain players. HomeCourt hopes to add a social layer on top of that to, once again, increase that kind of stickiness and build a community around what would otherwise be a purely technical tool — and one that might scare off more casual players with a very sabermetrics-feeling approach.
Lee also said he hopes the app will eventually broaden into other sports, like Golf or Tennis, where tracking the ball might be more complicated or the motions considerably different from basketball. That’s based on building technology that tracks the movement of the player, and not just the ball, in order to determine the trajectory or success of that specific shots. The hope is that basketball is a first step in terms of achieving that.
“For golf, seeing your whole form as going into your swing is more important — that’s the input in terms of getting where the ball goes,” Lee said. “We’re trying to think about how to reduce as much friction as possible. Imagine being able to use the app to track makes or misses, but also tracking your player movement and form, measuring it, and comparing it to another player’s backswing. We’re hoping to do that in basketball [first].”
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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.
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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.
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Klang gets $8.95M for an MMO sim sitting atop Improbable’s dev platform
Berlin-based games studio Klang, which is building a massive multiplayer online simulation called Seed utilizing Improbable’s virtual world builder platform, has just bagged $8.95M in Series A funding to support development of the forthcoming title.
The funding is led by veteran European VC firm Northzone. It follows a seed raise for Seed, finalized in March 2018, and led by Makers Fund, with participation by firstminute capital, Neoteny, Mosaic Ventures, and Novator — bringing the total funding raised for the project to $13.95M.
The studio was founded in 2013, and originally based in Reykjavík, Iceland, before relocating to Berlin. Klang’s original backers include Greylock Partners, Joi Ito, and David Helgason, as well as original investors London Venture Partners.
The latest tranche of funding will be used to expand its dev team and for continued production on Seed which is in pre-alpha at this stage — with no release date announced yet.
Nor is there a confirmed pricing model. We understand the team is looking at a variety of ideas at this stage, such as tying the pricing to the costs of simulating the entities.
They have released the below teaser showing the pre-alpha build of the game — which is described as a persistent simulation where players are tasked with colonizing an alien planet, managing multiple characters in real-time and interacting with characters managed by other human players they encounter in the game space.
The persistent element refers to the game engine maintaining character activity after the player has logged off — supporting an unbroken simulation.
Klang touts its founders’ three decades of combined experience working on MMOs EVE Online and Dust 514, and now being rolled into designing and developing the large, player-driven world they’re building with Seed.
Meanwhile London-based Improbable bagged a whopping $502M for its virtual world builder SpatialOS just over a year ago. The dev platform lets developers design and build massively detailed environments — to offer what it bills as a new form of simulation on a massive scale — doing this by utilizing distributed cloud computing infrastructure and machine learning technology to run a swarm of hundreds of game engines so it can support a more expansive virtual world vs software running off of a single engine or server.
Northzone partner Paul Murphy, who is leading the investment in Klang, told us: “It is unusual to raise for a specific title, and we are for all intents and purposes investing in Klang as a studio. We are very excited about the team and the creative potential of the studio. But our investment thesis is based on looking for something that really stands out and is wildly ambitious over and above everything else that’s out there. That is how we feel about the potential of Seed as a simulation.”
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Japanese startup Paidy raises $55M Series C to let people shop online without a credit card
Paidy, a fintech startup that enables Japanese consumers to shop online without using a credit card, announced today that it has raised a $55 million Series C. The round was led by Japanese trade conglomerate Itochu Corporation, with participation from Goldman Sachs.
The Tokyo-based startup says this brings its total funding so far to $80 million, including a $15 million Series B announced two years ago. One notable fact about Paidy’s funding is that it’s raised a sizable amount for Japanese startup, especially one with non-Japanese founders (its CEO and co-founder is Canadian and Goldman Sachs alum Russell Cummer, left in the photo above with CTO and co-founder Lee Smith).
Paidy was launched because even though Japan’s credit card penetration rate is high, their usage rate is relatively low, even for online purchases. Instead, shoppers pay cash on delivery or at convenience stores, which function as combination logistics/payment centers in many Japanese cities.
This is convenient for buyers because they don’t have to enter a credit card online or worry about fraud, but a hassle for businesses that often need to float cash for merchandise that hasn’t been paid for yet or deal with incomplete deliveries.
Paidy makes it possible for people to buy online without creating an account or using their credit cards. Instead, if a merchant uses Paidy, its customers are able to check out by entering their mobile phone numbers and email addresses. Then Paidy authenticates them with a four-digit code sent through SMS or voice. Every month, customers settle their bills, which include all transactions they made using Paidy, at a convenience store or through bank transfers or auto-debits (installment and subscription plans are also available).
The value proposition for businesses is that Paidy can increase their customer base and guarantee they get paid by using machine learning algorithms to underwrite transactions. The company claims that there are now 1.4 million active Paidy accounts, with the ambitious goal of increasing that number to 11 million by 2020 by expanding to bigger merchants and offline transactions.
In a press statement, Cummer said “We are extremely honored that Paidy’s business concept was highly valued by one of Japan’s most prestigious business conglomerates, ITOCHU. Through this tie-up, we expect to launch new merchants in order to deliver Paidy’s frictionless and intuitive financial solution to a much broader audience.”
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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 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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Box acquires Butter.ai to make search smarter
Box announced today that it has acquired Butter.ai, a startup that helps customers search for content intelligently in the cloud. The terms of the deal were not disclosed, but the Butter.AI team will be joining Box.
Butter.AI was started by two ex-Evernote employees, Jack Hirsch and Adam Walz. The company was partly funded by Evernote founder and former CEO Phil Libin’s Turtle Studios. The latter is a firm established with a mission to use machine learning to solve real business problems like finding the right document wherever it is.
Box has been adding intelligence to its platform for some time, and this acquisition brings the Butter.AI team on board and gives them more machine learning and artificial intelligence known-how while helping to enhance search inside of the Box product.
“The team from Butter.ai will help Box to bring more intelligence to our Search capabilities, enabling Box’s 85,000 customers to more easily navigate through their unstructured information — making searching for files in Box more contextualized, predictive and personalized,” Box’s Jeetu Patel wrote in a blog post announcing the acquisition.
That means taking into account the context of the search and delivering documents that make sense given your role and how you work. For instance, if you are a salesperson and you search for a contract, you probably want a sales contract and not one for a freelancer or business partnership.
For Butter, the chance to have access to all those customers was too good to pass up. “We started Butter.ai to build the best way to find documents at work. As it turns out, Box has 85,000 customers who all need instant access to their content. Joining Box means we get to build on our original mission faster and at a massive scale,” company CEO and co-founder Jack Hirsch said.
The company launched in September 2017, and up until now it has acted as a search assistant inside Slack you can call upon to search for documents and find them wherever they live in the cloud. The company will be winding down that product as it becomes part of the Box team.
As is often the case in these deals, the two companies have been working closely together and it made sense for Box to bring the Butter.AI team into the fold where it can put its technology to bear on the Box platform.
“After launching in September 2017 our customers were loud and clear about wanting us to integrate with Box and we quickly delivered. Since then, our relationship with Box has deepened and now we get to build on our vision for a MUCH larger audience as part of the Box team,” the founders wrote in a Medium post announcing the deal.
The company raised $3.3 million over two seed rounds. Investors included Slack and General Catalyst.
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