Bringing order and understanding to unstructured information located across disparate silos has been one of the more significant breakthroughs of the big data era, and today a European startup that has built a platform to help with this challenge specifically in the area of life sciences — and has, notably, been used by labs to sequence and so far identify two major COVID-19 variants — is announcing some funding to continue building out its tools to a wider set of use cases, and to expand into North America.

Seqera Labs, a Barcelona-based data orchestration and workflow platform tailored to help scientists and engineers order and gain insights from cloud-based genomic data troves, as well as to tackle other life science applications that involve harnessing complex data from multiple locations, has raised $5.5 million in seed funding.

Talis Capital and Speedinvest co-led this round, with participation also from previous backer BoxOne Ventures and a grant from the Chan Zuckerberg Initiative, Mark Zuckerberg and Dr. Priscilla Chan’s effort to back open source software projects for science applications.

Seqera — a portmanteau of “sequence” and “era”, the age of sequencing data, basically — had previously raised less than $1 million, and quietly, it is already generating revenues, with five of the world’s biggest pharmaceutical companies part of its customer base, alongside biotech and other life sciences customers.

Seqera was spun out of the Centre for Genomic Regulation, a biomedical research center based out of Barcelona, where it was built as the commercial application of Nextflow, open source workflow and data orchestration software originally created by the founders of Seqera, Evan Floden and Paolo Di Tommaso, at the CGR.

Floden, Seqera’s CEO, told TechCrunch that he and Di Tommaso were motivated to create Seqera in 2018 after seeing Nextflow gain a lot of traction in the life science community, and subsequently getting a lot of repeat requests for further customization and features. Both Nextflow and Seqera have seen a lot of usage: the Nextflow runtime has been downloaded more than 2 million times, the company said, while Seqera’s commercial cloud offering has now processed more than 5 billion tasks.

The COVID-19 pandemic is a classic example of the acute challenge that Seqera (and by association Nextflow) aims to address in the scientific community. With COVID-19 outbreaks happening globally, each time a test for COVID-19 is processed in a lab, live genetic samples of the virus get collected. Taken together, these millions of tests represent a goldmine of information about the coronavirus and how it is mutating, and when and where it is doing so. For a new virus about which so little is understood and that is still persisting, that’s invaluable data.

So the problem is not if the data exists for better insights (it does); it is that it’s nearly impossible to use more legacy tools to view that data as a holistic body. It’s in too many places, and there is just too much of it, and it’s growing every day (and changing every day), which means that traditional approaches of porting data to a centralized location to run analytics on it just wouldn’t be efficient, and would cost a fortune to execute.

That is where Segera comes in. The company’s technology treats each source of data across different clouds as a salient pipeline which can be merged and analyzed as a single body, without that data ever leaving the boundaries of the infrastructure where it already exists. Customised to focus on genomic troves, scientists can then query that information for more insights. Seqera was central to the discovery of both the Alpha and Delta variants of the virus, and work is still ongoing as COVID-19 continues to hammer the globe.

Seqera is being used in other kinds of medical applications, such as in the realm of so-called “precision medicine.” This is emerging as a very big opportunity in complex fields like oncology: cancer mutates and behaves differently depending on many factors, including genetic differences of the patients themselves, which means that treatments are less effective if they are “one size fits all.”

Increasingly, we are seeing approaches that leverage machine learning and big data analytics to better understand individual cancers and how they develop for different populations, to subsequently create more personalized treatments, and Seqera comes into play as a way to sequence that kind of data.

This also highlights something else notable about the Seqera platform: it is used directly by the people who are analyzing the data — that is, the researchers and scientists themselves, without data specialists necessarily needing to get involved. This was a practical priority for the company, Floden told me, but nonetheless, it’s an interesting detail of how the platform is inadvertently part of that bigger trend of “no-code/low-code” software, designed to make highly technical processes usable by non-technical people.

It’s both the existing opportunity and how Seqera might be applied in the future across other kinds of data that lives in the cloud that makes it an interesting company, and it seems an interesting investment, too.

“Advancements in machine learning, and the proliferation of volumes and types of data, are leading to increasingly more applications of computer science in life sciences and biology,” said Kirill Tasilov, principal at Talis Capital, in a statement. “While this is incredibly exciting from a humanity perspective, it’s also skyrocketing the cost of experiments to sometimes millions of dollars per project as they become computer-heavy and complex to run. Nextflow is already a ubiquitous solution in this space and Seqera is driving those capabilities at an enterprise level – and in doing so, is bringing the entire life sciences industry into the modern age. We’re thrilled to be a part of Seqera’s journey.”

“With the explosion of biological data from cheap, commercial DNA sequencing, there is a pressing need to analyse increasingly growing and complex quantities of data,” added Arnaud Bakker, principal at Speedinvest. “Seqera’s open and cloud-first framework provides an advanced tooling kit allowing organisations to scale complex deployments of data analysis and enable data-driven life sciences solutions.”

Although medicine and life sciences are perhaps Seqera’s most obvious and timely applications today, the framework originally designed for genetics and biology can be applied to any a number of other areas: AI training, image analysis and astronomy are three early use cases, Floden said. Astronomy is perhaps very apt, since it seems that the sky is the limit.

“We think we are in the century of biology,” Floden said. “It’s the center of activity and it’s becoming data-centric, and we are here to build services around that.”

Today, Amazon Web Services is a mainstay in the cloud infrastructure services market, a $60 billion juggernaut of a business. But in 2008, it was still new, working to keep its head above water and handle growing demand for its cloud servers. In fact, 15 years ago last week, the company launched Amazon EC2 in beta. From that point forward, AWS offered startups unlimited compute power, a primary selling point at the time.

EC2 was one of the first real attempts to sell elastic computing at scale — that is, server resources that would scale up as you needed them and go away when you didn’t. As Jeff Bezos said in an early sales presentation to startups back in 2008, “you want to be prepared for lightning to strike, […] because if you’re not that will really generate a big regret. If lightning strikes, and you weren’t ready for it, that’s kind of hard to live with. At the same time you don’t want to prepare your physical infrastructure, to kind of hubris levels either in case that lightning doesn’t strike. So, [AWS] kind of helps with that tough situation.”

An early test of that value proposition occurred when one of their startup customers, Animoto, scaled from 25,000 to 250,000 users in a 4-day period in 2008 shortly after launching the company’s Facebook app at South by Southwest.

At the time, Animoto was an app aimed at consumers that allowed users to upload photos and turn them into a video with a backing music track. While that product may sound tame today, it was state of the art back in those days, and it used up a fair amount of computing resources to build each video. It was an early representation of not only Web 2.0 user-generated content, but also the marriage of mobile computing with the cloud, something we take for granted today.

For Animoto, launched in 2006, choosing AWS was a risky proposition, but the company found trying to run its own infrastructure was even more of a gamble because of the dynamic nature of the demand for its service. To spin up its own servers would have involved huge capital expenditures. Animoto initially went that route before turning its attention to AWS because it was building prior to attracting initial funding, Brad Jefferson, co-founder and CEO at the company explained.

“We started building our own servers, thinking that we had to prove out the concept with something. And as we started to do that and got more traction from a proof-of-concept perspective and started to let certain people use the product, we took a step back, and were like, well it’s easy to prepare for failure, but what we need to prepare for success,” Jefferson told me.

Going with AWS may seem like an easy decision knowing what we know today, but in 2007 the company was really putting its fate in the hands of a mostly unproven concept.

“It’s pretty interesting just to see how far AWS has gone and EC2 has come, but back then it really was a gamble. I mean we were talking to an e-commerce company [about running our infrastructure]. And they’re trying to convince us that they’re going to have these servers and it’s going to be fully dynamic and so it was pretty [risky]. Now in hindsight, it seems obvious but it was a risk for a company like us to bet on them back then,” Jefferson told me.

Animoto had to not only trust that AWS could do what it claimed, but also had to spend six months rearchitecting its software to run on Amazon’s cloud. But as Jefferson crunched the numbers, the choice made sense. At the time, Animoto’s business model was for free for a 30 second video, $5 for a longer clip, or $30 for a year. As he tried to model the level of resources his company would need to make its model work, it got really difficult, so he and his co-founders decided to bet on AWS and hope it worked when and if a surge of usage arrived.

That test came the following year at South by Southwest when the company launched a Facebook app, which led to a surge in demand, in turn pushing the limits of AWS’s capabilities at the time. A couple of weeks after the startup launched its new app, interest exploded and Amazon was left scrambling to find the appropriate resources to keep Animoto up and running.

Dave Brown, who today is Amazon’s VP of EC2 and was an engineer on the team back in 2008, said that “every [Animoto] video would initiate, utilize and terminate a separate EC2 instance. For the prior month they had been using between 50 and 100 instances [per day]. On Tuesday their usage peaked at around 400, Wednesday it was 900, and then 3,400 instances as of Friday morning.” Animoto was able to keep up with the surge of demand, and AWS was able to provide the necessary resources to do so. Its usage eventually peaked at 5000 instances before it settled back down, proving in the process that elastic computing could actually work.

At that point though, Jefferson said his company wasn’t merely trusting EC2’s marketing. It was on the phone regularly with AWS executives making sure their service wouldn’t collapse under this increasing demand. “And the biggest thing was, can you get us more servers, we need more servers. To their credit, I don’t know how they did it — if they took away processing power from their own website or others — but they were able to get us where we needed to be. And then we were able to get through that spike and then sort of things naturally calmed down,” he said.

The story of keeping Animoto online became a main selling point for the company, and Amazon was actually the first company to invest in the startup besides friends and family. It raised a total of $30 million along the way, with its last funding coming in 2011. Today, the company is more of a B2B operation, helping marketing departments easily create videos.

While Jefferson didn’t discuss specifics concerning costs, he pointed out that the price of trying to maintain servers that would sit dormant much of the time was not a tenable approach for his company. Cloud computing turned out to be the perfect model and Jefferson says that his company is still an AWS customer to this day.

While the goal of cloud computing has always been to provide as much computing as you need on demand whenever you need it, this particular set of circumstances put that notion to the test in a big way.

Today the idea of having trouble generating 3,400 instances seems quaint, especially when you consider that Amazon processes 60 million instances every day now, but back then it was a huge challenge and helped show startups that the idea of elastic computing was more than theory.

Factorial, a startup out of Barcelona that has built a platform that lets SMBs run human resources functions with the same kind of tools that typically are used by much bigger companies, is today announcing some funding to bulk up its own position: the company has raised $80 million, funding that it will be using to expand its operations geographically — specifically deeper into Latin American markets — and to continue to augment its product with more features.

CEO Jordi Romero, who co-founded the startup with Pau Ramon and Bernat Farrero — said in an interview that Factorial has seen a huge boom of growth in the last 18 months and counts more than anything 75,000 customers across 65 countries, with the average size of each customer in the range of 100 employees, although they can be significantly (single-digit) smaller or potentially up to 1,000 (the “M” of SMB, or SME as it’s often called in Europe).

“We have a generous definition of SME,” Romero said of how the company first started with a target of 10-15 employees but is now working in the size bracket that it is. “But that is the limit. This is the segment that needs the most help. We see other competitors of ours are trying to move into SME and they are screwing up their product by making it too complex. SMEs want solutions that have as much data as possible in one single place. That is unique to the SME.” Customers can include smaller franchises of much larger organizations, too: KFC, Booking.com, and Whisbi are among those that fall into this category for Factorial.

Factorial offers a one-stop shop to manage hiring, onboarding, payroll management, time off, performance management, internal communications and more. Other services such as the actual process of payroll or sourcing candidates, it partners and integrates closely with more localized third parties.

The Series B is being led by Tiger Global, and past investors CRV, Creandum, Point Nine and K Fund also participating, at a valuation we understand from sources close to the deal to be around $530 million post-money. Factorial has raised $100 million to date, including a $16 million Series A round in early 2020, just ahead of the Covid-19 pandemic really taking hold of the world.

That timing turned out to be significant: Factorial, as you might expect of an HR startup, was shaped by Covid-19 in a pretty powerful way.

The pandemic, as we have seen, massively changed how — and where — many of us work. In the world of desk jobs, offices largely disappeared overnight, with people shifting to working at home in compliance with shelter-in-place orders to curb the spread of the virus, and then in many cases staying there even after those were lifted as companies grappled both with balancing the best (and least infectious) way forward and their own employees’ demands for safety and productivity. Front-line workers, meanwhile, faced a completely new set of challenges in doing their jobs, whether it was to minimize exposure to the coronavirus, or dealing with giant volumes of demand for their services. Across both, organizations were facing economics-based contractions, furloughs, and in other cases, hiring pushes, despite being office-less to carry all that out.

All of this had an impact on HR. People who needed to manage others, and those working for organizations, suddenly needed — and were willing to pay for — new kinds of tools to carry out their roles.

But it wasn’t always like this. In the early days, Romero said the company had to quickly adjust to what the market was doing.

“We target HR leaders and they are currently very distracted with furloughs and layoffs right now, so we turned around and focused on how we could provide the best value to them,” Romero said to me during the Series A back in early 2020. Then, Factorial made its product free to use and found new interest from businesses that had never used cloud-based services before but needed to get something quickly up and running to use while working from home (and that cloud migration turned out to be a much bigger trend played out across a number of sectors). Those turning to Factorial had previously kept all their records in local files or at best a “Dropbox folder, but nothing else,” Romero said.

It also provided tools specifically to address the most pressing needs HR people had at the time, such as guidance on how to implement furloughs and layoffs, best practices for communication policies and more. “We had to get creative,” Romero said.

But it wasn’t all simple. “We did suffer at the beginning,” Romero now says. “People were doing furloughs and [frankly] less attention was being paid to software purchasing. People were just surviving. Then gradually, people realized they needed to improve their systems in the cloud, to manage remote people better, and so on.” So after a couple of very slow months, things started to take off, he said.

Factorial’s rise is part of a much, longer-term bigger trend in which the enterprise technology world has at long last started to turn its attention to how to take the tools that originally were built for larger organizations, and right size them for smaller customers.

The metrics are completely different: large enterprises are harder to win as customers, but represent a giant payoff when they do sign up; smaller enterprises represent genuine scale since there are so many of them globally — 400 million, accounting for 95% of all firms worldwide. But so are the product demands, as Romero pointed out previously: SMBs also want powerful tools, but they need to work in a more efficient, and out-of-the-box way.

Factorial is not the only HR startup that has been honing in on this, of course. Among the wider field are PeopleHR, Workday, Infor, ADP, Zenefits, Gusto, IBM, Oracle, SAP and Rippling; and a very close competitor out of Europe, Germany’s Personio, raised $125 million on a $1.7 billion valuation earlier this year, speaking not just to the opportunity but the success it is seeing in it.

But the major fragmentation in the market, the fact that there are so many potential customers, and Factorial’s own rapid traction are three reasons why investors approached the startup, which was not proactively seeking funding when it decided to go ahead with this Series B.

“The HR software market opportunity is very large in Europe, and Factorial is incredibly well positioned to capitalize on it,” said John Curtius, Partner at Tiger Global, in a statement. “Our diligence found a product that delighted customers and a world-class team well-positioned to achieve Factorial’s potential.”

“It is now clear that labor markets around the world have shifted over the past 18 months,” added Reid Christian, general partner at CRV, which led its previous round, which had been CRV’s first investment in Spain. “This has strained employers who need to manage their HR processes and properly serve their employees. Factorial was always architected to support employers across geographies with their HR and payroll needs, and this has only accelerated the demand for their platform. We are excited to continue to support the company through this funding round and the next phase of growth for the business.”

Notably, Romero told me that the fundraising process really evolved between the two rounds, with the first needing him flying around the world to meet people, and the second happening over video links, while he was recovering himself from Covid-19. Given that it was not too long ago that the most ambitious startups in Europe were encouraged to relocate to the U.S. if they wanted to succeed, it seems that it’s not just the world of HR that is rapidly shifting in line with new global conditions.