Author: azeeadmin

Databases run the world, but database products are often some of the most mature and venerable software in the modern tech stack. Designers will pixel push, frontend engineers will add clicks to make it more difficult to drop out of a soporific Zoom call, but few companies are ever willing to rip out their database storage engine. Too much risk, and almost no return.

So it’s exceptional when a new database offering breaks through the barriers and redefines the enterprise.

Neo4j, which offers a graph-centric database and related products, announced today that it raised $325 million at a more than $2 billion valuation in a Series F deal led by Eurazeo, with additional capital from Alphabet’s venture wing GV. Eurazeo managing director Nathalie Kornhoff-Brüls will join the company’s board of directors.

That funding makes Neo4j among the most well-funded database companies in history, with a collective fundraise haul of more than half a billion dollars. For comparison, MongoDB, which trades on Nasdaq, raised $311 million in total according to Crunchbase before its IPO. Meanwhile, Cockroach Labs of CockroachDB fame has now raised $355 million in funding, including a $160 million round earlier this year at a similar $2 billion valuation.

The past decade has seen a whole new crop of next-generation database models, from scale-out SQL to document to key-value stores to time series and on and on and on. What makes graph databases like Neo4j unique is their focus on the connections between individual data entities. Graph-based data models have become central to modern machine learning and artificial intelligence applications, and are now widely used by data analysts in applications as diverse as marketing to fraud detection.

CEO and co-founder Emil Eifrem said that Neo4j, which was founded back in 2007, has hit its growth stride in recent years given the rising popularity of graph-based analysis. “We have a deep developer community of hundreds of thousands of developers actively building applications with Neo4j in any given month, but we also have a really deep data science community,” he said.

In the past, most business analysis was built on relational databases. Yet, inter-connected complexity is creeping in everywhere, and that’s where Eifrem believes Neo4j has a durable edge. As an example, “any company that ships stuff is tapping into this global fine-grain mesh spanning continent to continent,” he suggested. “All of a sudden the ship captain in the Suez Canal … falls asleep, and then they block the Suez Canal for a week, and then you’ve got to figure out how will this affect my enterprise, how does that cascade across my entire supply chain.” With a graph model, that analysis is a cinch.

Neo4j says that 800 enterprises are customers and 75% of the Fortune 100 are users of the company’s products.

We last checked in with the company in 2020 when it launched 4.0, which offered unlimited scaling. Today, Neo4j comes in a couple of different flavors. It’s a database that can be either self-hosted or purchased as a cloud service offering which it dubs Aura. That’s for the data storage folks. For the data scientists, the company offers Neo4j Graph Data Science Library, a set of comprehensive tools for analyzing graph data. The company offers free (or “community” tiers), affordable starting tiers and full-scale enterprise pricing options depending on needs.

Development continues on the database. This morning at its developers conference, Neo4j demonstrated what it dubbed its “super-scaling technology” on a 200 billion node graph with more than a trillion relationships between them, showing how its tools could offer “real-time” queries on such a large scale.

Unsurprisingly, Eifrem said that the new venture funding will be used to continue doubling down on “product, product, product” but emphasized a few major strategic initiatives as critical for the company. First, he wants to continue to deepen the company’s partnerships with public cloud providers. It already has a deep relationship with Google Cloud (GV was an investor in this round after all), and hopes to continue building relationships with other providers.

It’s also seeing a major uptick in interest from the APAC region. Eifrem said that the company recently opened up an office in Singapore to accelerate its sales in the broader IT market there.

Overall, “We think that graphs can be a significant part of the modern data landscape. In fact, we believe it can be the biggest part of the modern data landscape. And this round, I think, sends a clear signal [that] we’re going for it,” he said.

Erik Nordlander and Tom Hulme of GV were the leads for that firm. In addition, DTCP and Lightrock newly invested and previous investors One Peak, Creandum, and Greenbridge Partners joined the round.

Google this morning announced the launch of Tau, a new family of virtual machines built on AMD’s third-gen EPYC processor. According to the company, the new x86-compatible system offers a 42% price-performance boost over standard VMs. Google notably first started utilizing AMD EPYC processors for Cloud back in 2017, while Amazon Cloud’s offerings date back to 2018.

Google claims the Tau family “leapfrogs” existing cloud VMs. The systems come in a variety of configurations, ranging up to 60vCPUs per VM, and 4GB of memory per vCPU. Networking bandwidth goes up to 32 Gbps, and they can be coupled with a variety of different network attached storage.

“Customers across every industry are dealing with more demanding and data-intensive workloads and looking for strategic ways to speed up performance and reduce costs,” Google Cloud CEO Thomas Kurian said in a press release.  “Our work with key strategic partners like AMD has allowed us to broaden our offerings and deliver customers the best price performance for compute-heavy, business-critical applications– all on the cleanest cloud in the industry.”

Image Credits: Google

Google has already signed up some high-profile customers for an early trial, including Twitter, Snap and DoIT.

“High performance at the right price point is a critical consideration as we work to serve the global public conversation,” Twitter Platform Lead Nick Tornow said in a blog post. “We are excited by initial tests that show potential for double digit performance improvement. We are collaborating with Google Cloud to more deeply evaluate benefits on price and performance for specific compute workloads that we can realize through use of the new Tau VM family.”

Image Credits: Google

The Tau VMs will be arriving for Google Cloud in Q3 of this year. The company has already opened the system up to clients for pre-registration. Pricing is dependent on the configuration. For example, a 32vCPU VM sporting 128GB RAM will run around $1.35 an hour.

TechCrunch City Spotlight: Pittsburgh is getting closer, with impressive featured speakers including Carnegie Mellon University President Farnam Jahanian and Mayor Bill Peduto. However, we’ve saved the best for last: our last speaker is Karin Tsai, director of engineering at Duolingo, a $2.4 billion business that is all about making language learning fun and accessible.

The event will be held on June 29, so make sure to register here (for free) to listen to these conversations, enjoy the pitch off, and network with local talent.

Tsai joined Duolingo in 2012 as one of its first engineers, and first-hand witnessed the growth of the company from a scrappy startup into a 400-person global business. Her timestamp on the company has made her a key decision-maker in many of its biggest decisions, from which features to scrap to how to monetize without compromising its mission of providing free education to all.

One thing to note is that even though a whimsical owl and creative UX might seem straightforward, the language learning universe is controversial and requires healthy debate – and testing – for anyone within it.

“We’re trying to do things that no other apps really tackle: How do we create an experience that actually makes you extremely proficient in a language while accommodating the expectations from our learners” to be fun and convenient, Tsai told me when I interviewed her for my Duolingo EC-1. “Balancing efficacy with engagement is something that we constantly struggle with.”

In this chat, Tsai will break down how Duolingo turned to A/B testing to answer some of its biggest questions. We’ll also chat about more meta topics, as when to give up on measuring the unmeasurable, and when tests fail and instinct reigns supreme. Tsai admitted to me once that Duolingo spent years trying to figure out how to find a metric that could encompass learning comprehension and engagement in one fell swoop.

“What used to freeze us is that we thought we would need such a metric to make progress,” she explained. “And I think what honestly liberated us was saying essentially, ‘Screw it.’ We couldn’t make progress waiting for a learning metric.”

I’ll be interviewing Tsai, so anyone who registers for this event is welcome to throw me questions for her that I’ll try to embed in my chat.

Tsai will give us the startup builder perspective, while Mayor Peduto will speak to the challenges of building a startup ecosystem, and Carnegie Mellon University President Farnam Jahanian will discuss how to go from student to startup with the correct resources.

Don’t forget to register for this free event on June 29th (click here to register) so you can watch these chats and riff with audience members during networking opportunities. If you’re an early-stage startup founder based in Pittsburgh, you should apply to pitch your startup (click here to apply). Expect to do a live two-minute pitch, get feedback from local VCs, and maybe even win our pitch-off.

I can’t wait to see you there!

I spoke to Refraction AI co-founder/CTO Matthew Johnson-Roberson on the occasion of the Michigan startup’s $4.2 million seed raise. This week we posted a Q&A where he answers a wider range of topics about the delivery robotics company, and this bit jumped out at me:

It still boggles my mind that nobody has tried to copy what we’re doing. There were 10 or 12 sidewalk robot companies in early 2015, 2016 and 2017. Many of them, with a few exceptions, went out of business.

Image Credits: Refraction

The first part of the quote points to seemingly obvious truths that are still worth reiterating here. First: If you spot a need in the market you believe you can address, go for it. Second: There are likely even more opportunities for robotics and automation than we’ve considered. The second sentence seemingly negates the second point to some degree, but more than anything, I think it’s an indictment of how merciless this industry can be.

High risk/high reward, and all that, but even with a great idea, smart people and a healthy raise, bad timing can still land you flat on your face. For now, it seems, the timing is right. Delivery robotics are very much an industry that has been accelerated by the pandemic, in terms of interest, innovation and, of course, funding.

Image Credits: Nuro

As I noted last week, I spoke to Gatik co-founder and chief engineer Apeksha Kumavat, Nuro head of operations Amy Jones Satrom and Starship Technologies co-founder and CTO Ahti Heinla at last week’s TC Sessions: Mobility event. Here’s what Kumavat had to say about that acceleration:

Even before the pandemic hit, this whole e-commerce trend was already on the rise. No one wants their deliveries to be done after a week or two weeks. Everyone is expecting them to be done on the same day, as well as curbside pickup options. There was already a rise in the expectations of e-commerce and on-demand deliveries even before the pandemic hit. Post-March 2020, what we have seen is a huge increase in that trajectory.

More big news from Nuro (try saying that five times, fast), the delivery company just signed a deal with FedEx, marking a big step into package delivery.

Image Credits: Scythe Robotics

This week, I also spoke to another pair of robotics startups that have emerged from the pandemic with sizable rounds. Boulder-based Scythe emerged from stealth with a $13.8 million Series A, bringing its total funding to $18.6 million. The company specializes in landscaping robotics, starting with a mower. Given the potential market size, I’m honestly surprised there aren’t more companies doing this.

Interestingly, the company is offering a RaaS (robotics as a service) model, which is becoming increasingly popular in the space. Here it’s charging customers based on the number of acres mowed.

Image Credits: Dusty Robotics

Bay Area-based Dusty Robotics, meanwhile, raised a $16.5 million Series A, bringing its total raised to $23.7 million. Construction is a huge potential market with a lot of interest and players. Dusty’s offering is interesting and fairly unique, effectively printing plans on the floor of a construction site. The company likens it to “Ikea Instructions.” Here’s co-founder and CEO, Tessa Lau:

We just released our third-generation hardware platform, which was designed from the ground up by our team in Mountain View to be purpose-built for producing accurate and speedy layout on construction sites. We’ve been working on this product since fall of 2018 and have incorporated lessons learned from completing over 1 million square feet of production layout into this third-generation design.

And for good measure, here’s a fun one from Tencent Robotics.

IEEE Spectrum spotted the robot, which was actually announced a few weeks ago. According to the paper where Ollie appeared, the wheeled robot is more experimental than practical, but it’s capable of some pretty impressive feats none the less:

Experimental results demonstrate that the linear output regulation can maintain the standing of the robot, and that nonlinear controller can balance the robot under an initial starting angle far away from the equilibrium point, or under a changing robot height.

There isn’t a ton of info about Ollie available yet, but it sure is fun to watch.

Until recently, Google’s namesake Android app, which more than five billion installs to date, had a vulnerability that could have allowed an attacker to quietly steal personal data from a victim’s device.

Sergey Toshin, founder of mobile app security startup Oversecured, said in a blog post that the vulnerability has to do with how the Google app relies on code that is not bundled with the app itself. Many Android apps, including the Google app, reduce their download size and the storage space needed to run by relying on code libraries that are already installed on Android phones.

But the flaw in the Google app’s code meant it could be tricked into pulling a code library from a malicious app on the same device instead of the legitimate code library, allowing the malicious app to inherit the Google app’s permissions and granting it near-complete access to a user’s data. That access includes access to a user’s Google accounts, search history, email, text messages, contacts and call history, as well as being able to trigger the microphone and camera, and access the user’s location.

The malicious app would have to be launched once for the attack to work, Toshin said, but that the attack happens without the victim’s knowledge or consent. Deleting the malicious app would not remove the malicious components from the Google app, he said.

A Google spokesperson told TechCrunch that the company fixed the vulnerability last month and it had no evidence that the flaw has been exploited by attackers. Android’s in-built malware scanner, Google Play Protect, is meant to stop malicious apps from installing. But no security feature is perfect, and malicious apps have slipped through its net before.

Toshin said the Google app vulnerability is similar to another bug discovered by the startup in TikTok earlier this year, which if exploited could have allowed an attacker to steal a TikTok user’s session tokens to take control of their account.

Oversecured has found several other similar vulnerabilities, including Android’s Google Play app and more recently apps pre-installed on Samsung phones.

If you walk down the street shouting out the names of every object you see — garbage truck! bicyclist! sycamore tree! — most people would not conclude you are smart. But if you go through an obstacle course, and you show them how to navigate a series of challenges to get to the end unscathed, they would.

Most machine learning algorithms are shouting names in the street. They perform perceptive tasks that a person can do in under a second. But another kind of AI — deep reinforcement learning — is strategic. It learns how to take a series of actions in order to reach a goal. That’s powerful and smart — and it’s going to change a lot of industries.

Two industries on the cusp of AI transformations are manufacturing and supply chain. The ways we make and ship stuff are heavily dependent on groups of machines working together, and the efficiency and resiliency of those machines are the foundation of our economy and society. Without them, we can’t buy the basics we need to live and work.

Startups like Covariant, Ocado’s Kindred and Bright Machines are using machine learning and reinforcement learning to change how machines are controlled in factories and warehouses, solving inordinately difficult challenges such as getting robots to detect and pick up objects of various sizes and shapes out of bins, among others. They are attacking enormous markets: The industrial control and automation market was worth $152 billion last year, while logistics automation was valued at more than $50 billion.

As a technologist, you need a lot of things to make deep reinforcement learning work. The first piece to think about is how you will get your deep reinforcement learning agent to practice the skills you want it to acquire. There are only two ways — with real data or through simulations. Each approach has its own challenge: Data must be collected and cleaned, while simulations must be built and validated.

Some examples will illustrate what this means. In 2016, GoogleX advertised its robotic “arm farms” — spaces filled with robot arms that were learning to grasp items and teach others how to do the same — which was one early way for a reinforcement learning algorithm to practice its moves in a real environment and measure the success of its actions. That feedback loop is necessary for a goal-oriented algorithm to learn: It must make sequential decisions and see where they lead.

In many situations, it is not feasible to build the physical environment where a reinforcement learning algorithm can learn. Let’s say you want to test different strategies for routing a fleet of thousands of trucks moving goods from many factories to many retail outlets. It would be very expensive to test all possible strategies, and those tests would not just cost money to run, but the failed runs would lead to many unhappy customers.

For many large systems, the only possible way to find the best action path is with simulation. In those situations, you must create a digital model of the physical system you want to understand in order to generate the data reinforcement learning needs. These models are called, alternately, digital twins, simulations and reinforcement-learning environments. They all essentially mean the same thing in manufacturing and supply chain applications.

Recreating any physical system requires domain experts who understand how the system works. This can be a problem for systems as small as a single fulfillment center for the simple reason that the people who built those systems may have left or died, and their successors have learned how to operate but not reconstruct them.

Many simulation software tools offer low-code interfaces that enable domain experts to create digital models of those physical systems. This is important, because domain expertise and software engineering skills often cannot be found in the same person.

Why would you go through all this trouble for a single algorithm? Because deep reinforcement learning consistently produces results that other machine learning and optimization tools are incapable of. DeepMind used it, of course, to beat the world champion of the board game of Go. Reinforcement learning was part of the algorithms that were integral to achieving breakthrough results with chess, protein folding and Atari games. Likewise, OpenAI trained deep reinforcement learning to beat the best human teams at Dota 2.

Just like deep artificial neural networks began to find business applications in the mid-2010s, after Geoffrey Hinton was hired by Google and Yann LeCun by Facebook, so too, deep reinforcement learning will have an increasing impact on industries. It will lead to quantum improvements in robotic automation and system control on the same order as we saw with Go. It will be the best we have, and by a long shot.

The consequence of those gains will be immense increases in efficiency and cost savings in manufacturing products and operating supply chains, leading to decreases in carbon emissions and worksite accidents. And, to be clear, the chokepoints and challenges of the physical world are all around us. Just in the last year, our societies have been hit by multiple supply chain disruptions due to COVID, lockdowns, the Suez Canal debacle and extreme weather events.

Zooming in on COVID, even after the vaccine was developed and approved, many countries have had trouble producing it and distributing it quickly. These are manufacturing and supply chain problems that involve situations we could not prepare for with historical data. They required simulations to predict what would happen, as well as how we could best address crises when they do occur, as Michael Lewis illustrated in his recent book “The Premonition.”

It is precisely this combination of constraints and novel challenges that take place in factories and supply chains that reinforcement learning and simulation can help us solve more quickly. And we are sure to face more of them in the future.

eqtble founders (from l to r): Ethan Veres, Gabe Horwitz and Joseph Ifiegbu

“People are the backbone of any organization. People are more important than the product. Without people, you don’t have a product,” says Joseph Ifiegbu, who is Snap’s former head of human resources technology and also previous lead of WeWork’s People Analytics team.

Ifiegbu’s startup, called eqtble, wants to give HR teams the same kind of detailed analytics that product, sales and marketing departments have had for a long time, with the goal of creating more engaged and inclusive workplaces. The company, a Y Combinator alum, announced today it has raised $2.7 million in seed funding, led by Initialized Capital, with participation from SB Opportunity Fund, RS Ventures and other venture capital firms and angel investors.

Ifiegbu joined WeWork’s People Analytics team in 2017, when the company had a total of about 2,000 employees. By the time he left in 2020, that number had grown to 15,000 people. One of Ifiegbu’s first hires at WeWork was Gabe Horwitz, the first data scientist on the People Analytics’ team and now eqtble’s co-founder and chief product officer. The startup’s third co-founder and chief technology officer is Ethan Veres.

At many companies, especially ones that are growing quickly, workforce data is scattered across different HR software, including human resources information systems (HRIS), engagement platforms, benefit programs and employee surveys.

Because information is so fragmented, companies can miss important correlations. For example, they might not see the links between why top employees are quitting and how long it typically takes to promote people, or overlook pay inequality. This in turn impacts a company’s culture, including its approach to diversity, equity and inclusion, and ability to retain talented people.

As WeWork was rapidly scaling, the People Analytics team built tools to analyze data from across the company.

“There were a lot of questions being asked, like what is our promotion like? What is our attrition, are we hiring more men than women? There were all these questions and bottlenecks in our processes, and we wanted to have an understanding of our employees,” says Ifiegbu. “So we built systems to capture all that data, clean it, structure it and deliver dashboard insights to our leadership.”

The process took about two years, and the People Analytics team eventually grew to 15 people. Ifiegbu and Horwitz realized there were many companies that needed the same kind of analytics, but didn’t have WeWork’s resources. This prompted them to start working on eqtble.

“It took us such a long time and quite a bit of money because we had this team [at WeWork],” he says. “So how do we build something that delivers these insights to them, but doesn’t take that much time to do it, because we realize it’s very important that leadership and decision makers have the data to make decisions about their employees.”

How eqtble works

The current version of eqtble can be onboarded in six weeks, and Ifiegbu says the company’s goal is to shorten that process to just two days. Eqtble is sector agnostic and its target customers are high-growth companies that have between 250 to about 3,000 employees.

The human resources analytics platform can collect data from more than 100 sources (including Workday, ADP, Oracle, PeopleSoft, Qualtrics and Culture Amp, to name a few), and deliver insights and visualizations about four main areas: talent recruitment, workforce, engagement (including attrition, or when workers quit) and compensation.

One of eqtble’s summary dashboards

One of the things the platform can help HR teams do is identify why top candidates are declining offers.

For example, one of eqtble’s clients realized that their hiring managers were being passed more applications than they had time to look at. This created a bottleneck, because they weren’t able to interview people quickly enough. Other clients saw that candidates were dropping out because the interview process was too long.

“If you as an organization are saying ‘we’re going to have six rounds of interviews, it’s going to take three months to interview, you’re going to lose out on good candidates,” says Ifiegbu. “Other people are closing candidates within one to two weeks.”

Using data to increase diversity, equity and inclusion

It’s easy for a company to make DEI pledges, but even the best of intentions don’t result in progress if an organization isn’t willing to scrutinize itself. Because eqtble combines data from across a company, it can highlight potential issues before decision makers realize what is happening.

“Last year, all the companies were saying, ‘oh, we’re going to do this, we’re going to do all these things,’ and it’s like, ok, great, you can say anything, but the truth is you cannot change what you don’t measure,” says Ifiegbu.

For example, a company might be be proud of having a workforce that is divided equally between men and women, or that has a large percentage of people of color, when the reality is that many of them aren’t getting raises or being promoted into management roles.

“That 50/50 doesn’t mean anything if you don’t see representation at higher levels for women and people of color. What we’re doing is showing you a picture of your organization. If you can see the different parts of it, you can see the parts you can improve on and take actionable steps, not just lip service for the media,” says Ifiegbu. “Eqtble surfaces places you can improve or places where you are doing well so you can keep doing that.”

Ifiegbu is excited that the HR analytics space is gaining attention. “I feel like using data to drive decisions is such an important thing, and ultimately builds a healthier company.”

The seed funding will be used to grow eqtble’s engineering team and its platform’s machine learning and visualization capabilities, and user acquisition.

In a statement, Initialized Capital partner and president Jen Wolf said, “Important organizational issues like DEI or equitable compensation are not simply a box a company can check, they take honest commitment. Companies willing to make that commitment shouldn’t have to wait months or be discouraged by the financial investment it takes to understand the data they already own to make these meaningful changes. The eqtble team knows how to solve this, and they’re empowering other companies to do so.”

Jonathan Bush, the CEO and co-founder of Athenahealth, is a controversial figure in the controversial field of healthcare.

Over two decades after he started the now-public healthcare company, Bush lost Athenahealth to Elliott Management, an activist investor that bought the company alongside Veritas Capital. During this tense period of time, domestic violence allegations surfaced from his ex-wife, Sarah Seldon. Bush took responsibility for what he described as “regrettable incidents” that happened 14 years ago during a “particularly difficult personal time” in his life. Seldon, who TechCrunch attempted to reach for this story, made a statement then too, explaining that she and him have a “co-parenting relationship” with “respect, collaboration and love.”

After these public incidents, Bush went quiet and only later re-emerged as the executive chairman of Firefly Health, a primary care startup.

Now, Bush is back once again, this time as the co-founder of a new startup that aims to re-invent the digital health data stack, Zus. The company wants to create a shared data platform that doctor’s, regardless of specialty or location, can access to better understand their patients. Think of it as massive, fancy Google Doc built for healthcare, that healthtech startups can use to kickstart their solutions, faster.

Along with its launch, Zus announced today that it has raised a $34 million Series A led by Andreessen Horowitz, with participation from F-Prime Capital, Maverick Ventures, Rock Health, Martin Ventures and Oxeon Investments.

Bush’s venture-backed return to entrepreneurship may come as a surprise to some, including himself.

“I loved running Athena very much, all 22 years,” he said. “But I also loved fourth grade, and I don’t want to go back. I didn’t feel like I wanted to run a company again.” He changed his mind for two reasons: first, he expects that building a platform company will be different, and potentially less controversial, than building a traditional services business. Second, he sees “strong calling” to bring his tool to life amid a broader digital health boom.

“These digital health companies will largely not work, if they aren’t dramatically accelerated,” he said. “All of them now are facing this quandary: that it’s very hard to hire engineers, enormous regulation and complexity, one too many types of complexity associated with building technologies in medicine.”

With Zus, he’s trying to create capacity. The company has a lot of plans, which includes a growing library of software tools around patient relationship management, a data aggregation service that helps standardize medical records for sharing purposes, a platform that sits atop this information so that multiple doctors can access the same information, and a patient portal that lets users understand how their data is shared and accessed.

So far, the platform is being used by four partners: Cityblock Health, Dorsata, Firefly Health, which is Bush’s previous employer, and Oak Street Health.

Part of the company’s existence can be tied to recent regulation progress. The 21st Century Cures Act gave patients the right to access their medical records, and by next year, third parties can access that same data as well. Many think this newfound data portability could seed a massive new generation of healthcare apps, although there are some concerns about if patients know what they are signing up for.

Mimi Liu, chief technology officer of Firefly Health, said in a statement that Zus will help build out the parts of its infrastructure stack that can be commoditized, bringing its roll-out time from years to weeks and months. She added that its clinical value proposition will be improved because of the “downstream network effect that comes as a result of information sharing.”

A16z, who led the round, is an investor in Firefly Health, as well as a number of healthcare startups like Incredible Health, Omada, PatientPing, and Cedar.

Julie Yoo, general partner at Andreessen Horowitz, said that Zus embodies its digital health stack thesis, which argues need for “infrastructure platforms that serve the large and rapidly growing population of digital health companies, such that each company no longer has to build the same underlying tech and operations components over and over again, from scratch.”

When asked about Zus’ differentiation, Yoo said that the company will create a community-based marketplace for digital health companies to set up and trade notes, which she thinks has not yet existed in the sector.

“If anything, one might say that the precursor to this concept was the More Disruption Please (MDP) program at athenahealth, which makes Jonathan Bush uniquely qualified to build this more modern version of said concept,” she said. The MDP program was launched by Bush in 2017 with the goal of filling 200 seats in the Athenahealth’s San Francisco office with upccoming entreprepreneurs in healthcare.

Zus isn’t the first company to try to start an AWS for healthcare, and in fact there are numerous companies that all work on the different services that Zus wants to one day own, from administrative workflow to patient data retrieval. But, its holistic approach at a time when regulation is changing and investment is booming, along with an experienced founder with the right connections, could prepare it well for what’s to come.

Twine, a video chat startup that launched amid the pandemic as a sort of “Zoom for meeting new people,” shifted its focus to online events and, as a result, has now closed on $3.3 million in seed funding. To date, twine’s events customers have included names like Microsoft, Amazon, Forrester, and others, and the service is on track to do $1 million in bookings in 2021, the company says.

The new round was led by Moment Ventures, and included participation from Coelius Capital, AltaIR Capital, Mentors Fund, Rosecliff Ventures, AltaClub, and Bloom Venture Partners. Clint Chao, founding Partner at Moment, will join twine’s board of directors with the round’s close.

The shift into the online events space makes sense, given twine’s co-founders —  Lawrence Coburn, Diana Rau, and Taylor McLoughlin — hail from DoubleDutch, the mobile events technology provider acquired by Cvent in 2019.

Coburn, previously CEO of DoubleDutch, had been under a non-compete with its acquirer until December 2020, which is one reason why he didn’t first attempt a return to the events space.

The team’s original idea was to help people who were missing out on social connections under Covid lockdowns find a way to meet others and chat online. This early version of twine saw some small amount of traction, as 10% of its users were even willing to pay. But many more were nervous about being connected to random online strangers, twine found.

So the company shifted its focus to the familiar events space, with a specific focus on online events which grew in popularity due to the pandemic. While setting up live streams, text chats and Q&A has been possible, what’s been missing from many online events was the casual and unexpected networking that used to happen in-person.

“The hardest thing to bring to virtual events was the networking and the serendipity — like the conversations that used to happen in an elevator, in the bar, the lobby — these kinds of things,” explains Coburn. “So we began testing a group space version of twine — bringing twine to existing communities as opposed to trying to build our own, new community. And that showed a lot more legs,” he says.

By January 2021, the new events-focused version of twine was up-and-running, offering a set of professional networking tools for event owners. Unlike one-to-many or few-to-many video broadcasts, twine connects a small number of people for more intimate conversations.

“We did a lot of research with our customers and users, and beyond five [people in a chat], it turns into a webinar,” notes Coburn, of the limitations on twine’s video chat. In twine, a small handful of people are dropped into a video chat experience– and now, they’re not random online strangers. They’re fellow event attendees. That generally keeps user behavior professional and the conversations productive.

Event owners can use the product for free on twine’s website for small events with up to 30 users, but to scale up any further requires a license. Twine charges on a per attendee basis, where customers buy packs of attendees on a software-as-a-service model.

The company’s customers can then embed twine directly in their own website or add a link that pops open the twine website in a separate browser tab.

Coburn says twine has found a sweet spot with big corporate event programs. The company has around 25 customers, but some of those have already used twine for 10 or 15 events after first testing out the product for something smaller.

“We’re working with five or six of the biggest companies in the world right now,” noted Coburn.

Image Credits: twine

Because the matches are digital, twine can offer other tools like digital “business card” exchanges and analytics and reports for the event hosts and attendees alike.

Despite the cautious return to normal in the U.S., which may see in-person events return in the year ahead, twine believes there’s still a future in online events. Due to the pandemic’s lasting impacts, organizations are likely to adopt a hybrid approach to their events going forward.

“I don’t think there’s ever been an industry that has gone through a 15 months like the events industry just went through,” Coburn says. “These companies went to zero, their revenue went to zero and some of them were coming from hundreds of millions of dollars. So what happened was a digital transformation like the world has never seen,” he adds.

Now, there are tens of thousands of event planners who have gotten really good at tech and online events. And they saw the potential in online, which would sometimes deliver 4x or 5x the attendance of virtual, Coburn points out.

“This is why you see LinkedIn drop $50 million on Hopin,” he says, referring to the recent fundraise for the virtual conference technology business. (The deal was reportedly for less than $50 million). “This is why you see the rounds of funding that are going into Hoppin and Bizzabo and Hubilo and all the others. This is the taxi market, pre-Uber.”

Of course, virtual events may end up less concerned with social features when they can offer an in-person experience. And those who want to host online events may be looking for a broader solution than Zoom + twine, for example.

But twine has ideas about what it wants to do next, including asynchronous matchmaking, which could end up being more valuable as it could lead to better matches since it wouldn’t be limited to only who’s online now.

With the funding, twine is hiring in sales and customer success, working on accessibility improvements, and expanding its platform. To date, twine has raised $4.7 million.