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Stoke nabs $15.5M to boost its AI-driven freelance management system



Elevate your enterprise data technology and strategy at Transform 2021.

Tomorrow, Stoke will announce it has raised $15.5 million in a series A round of funding. The company, which offers a freelance management system (FMS) to help enterprises manage independent contractors, freelancers, consultants, agencies, and gig workers, will use the funds to build out engineering, product marketing, and sales, Stoke cofounder and CEO Shahar Erez told VentureBeat.

In terms of the product itself, he says the company wants to expand its partner ecosystem for marketplaces with sources for talent, including improving the experience for sourcing and adding a greater variety of sourcing capabilities. The company will also work toward launching global compliance for classification, rounding out its compliance offerings for the U.S. and some European countries, which Erez said are now “pretty solidified.”

In March, Stoke launched its Worker Classification Engine, an AI-powered system that analyzes companies’ relationships with contractors and freelancers and alerts them to potentially costly compliance risks. The model is built on more than 3,000 classification cases filed in the U.S.

“We’re seeing more regulatory changes that are impacting the future of work,” Erez said, mentioning classification, data compliance, and tax regulations. “Companies that need to move faster and have a growing number of independent contractors do not have the right tooling in place to build toward this new composition of workforce.”

The future of work

In addition to spurring a shift to remote work, the pandemic greatly accelerated dependence on freelance and contract work. An Upwork survey from September revealed that 59 million Americans performed freelance work in the last 12 months, an increase of 2 million since 2019. These freelancers represent 36% of the total U.S. workforce and half of the Gen Z workforce, with Upwork citing a “tough job market for recent college graduates” as a reason behind the boom. By 2027, it’s estimated that 86.5 million people — 50.9 percent of the total U.S. workforce — will be freelancing, according to Statista. The rapid shift has made it difficult for some enterprises to keep up with the workforce changes happening inside their own companies.

“A lot of these organizations don’t even know how many independent contractors they have,” Erez said. “Ask any CFO, CEO, or head of HR and you won’t find a single person who knows. There are so many things wrong with that because there is really no process. No process for effectively onboarding, no process to make sure they’re signing the right legal documents, and then we’re seeing events like freelancers not getting paid on time. Again, no one’s bad intention. It’s just a huge mess.”

Indeed, payments are a significant problem — 71% of freelancers report difficulty getting paid, according to the Freelancers Union. This type of arrangement also typically excludes workers from accessing company health care benefits and other support structures, like paid time off. The narrative is often that workers are going freelance for the flexibility, but the loss of millions of full-time jobs during the pandemic certainly plays a role. Zip Recruiter reported the percentage of temporary job postings on its platform jumped from 24% to 34% when the pandemic hit. Meanwhile, 90% of active job seekers on the platform are looking for a permanent, full-time position.

Financing the pivot

With these workforce changes rapidly underway, companies that can help enterprises navigate their extended worker base are raking in investor cash. In March, HoneyBook announced it had raised $155 million at a $1.1 billion valuation post-money. Utmost, which functions specifically for Workday customers, also announced a $21 million series B round of funding last month. Other companies in this space include giants like Fiverr and Upwork, as well as and

This latest round marks a total of $20 million in funding for Israel-based Stoke, which launched in 2019. The round was led by Battery Ventures, with participation from all previous investors and angels, including TLV Partners, Dynamic, and Loop.

“We’ve been interested for years in companies shaping the future of work, and the pandemic has made that investment thesis even more relevant and led directly to our interest in Stoke,” Itzik Parnafes, general partner at Battery Ventures, told VentureBeat.


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Artificial Intelligence

AI Powered Lender for Merchants Myos Raises $30 Million Series A Round



Myos has raised $30 million in Series A funding led by Fasanara Capital and Xploration Capital. Tomahawk.VC and a group of new and existing investors also participated in the funding round, according to the company. Berlin-based Myos states that it will use the additional capital to grow its existing customer base in Europe as well as entering international markets while further developing the company’s AI-based online lending platform. To date, Myos has provided merchants with over €30 million in more than 1,000 loans. Frankfurt-based Raisin Bank acts as the banking partner for Myos.

Myos provides working capital for digital merchants noting this sector is frequently considered “unbankable” during their startup phase. Myos eliminates the need for annuities or personal guarantees in financing for online sellers. Myos monitors the price, sales success, and competition of a given product on online marketplaces using AI and data-driven technology to evaluate the risk to then issue loans to merchants.

“We’re a step ahead of traditional lenders because we look at the retail products, not the merchants,” says Nikolaus Hilgenfeldt, co-founder and CEO of Myos. “With Myos, merchants can quickly receive loans of up to a million Euros, without complex business plans or personal guarantees which are often not available in the start-up phase”.

Eugene Timko, Managing Partner at Xploration Capital, says the transparency of online marketplaces creates the possibility of a fundamentally new risk assessment for trade loans.

“With this principle, Myos has succeeded in bringing its product to the market and we see its enormous potential for the e-commerce industry.”

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Artificial Intelligence, the startup behind time-tracking app Timely, raises $14M to build more AI-based productivity apps



Time is your most valuable asset — as the saying goes — and today a startup called, which is building AI-based productivity tools to help you with your own time management, is announcing some funding to double down on its ambitions: it wants not only to help manage your time, but to, essentially, provide ways to use it better in the future.

The startup, based out of Oslo, Norway, initially made its name with an app called Timely, a tool for people to track time spent doing different tasks. aimed not just at people who are quantified self geeks, but those who need to track time for practical reasons, such as consultants or others who work on the concept of billable hours. Timely has racked up 500,000 users since 2014, including more than 5,000 paying businesses in 160 countries.

Now, has raised $14 million as it gears up to launch its next apps, Dewo (pronounced “De-Voh”), an app that is meant to help people do more “deep work” by learning about what they are working on and filtering out distractions to focus better; and Glue, described as a knowledge hub to help in the creative process. Both are due to be released later in the year.

The funding is being led by local investors Melesio and Sanden, with participation from Investinor, Concentric and SNÖ Ventures, who backed previously.

“Productivity apps” has always been something of a nebulous category in the world of connected work. They can variously cover any kind of collaboration management software ranging from Asana and Jira through to Slack and Notion; or software that makes doing an existing work task more efficiently than you did it before (eg Microsoft has described all of what goes into Microsoft 365 — Excel, Word, Powerpoint, etc. — as “productivity apps”); or, yes, apps like those from that aim to improve your concentration or time management.

These days, however, it feels like the worlds of AI and advances in mobile computing are increasingly coming together to evolve that concept once again.

If the first wave of smartphone communications and the apps that are run on smartphone devices — social, gaming, productivity, media, information, etc. — have led to us getting pinged by a huge amount of data from lots of different places, all of the time, then could it be that the second wave is quite possibly going to usher in a newer wave of tools to handle all that better, built on the premise that not everything is of equal importance? No-mo FOMO? We’ll see.

In any case, some bigger platform players also helping to push the agenda of what productivity means in this day and age.

For example, in Apple’s recent preview of iOS 15 (due to come out later this year) the company gave a supercharge to its existing “do not disturb” feature on its phones, where it showed off a new Focus mode, letting users customize how and when they want to receive notifications from which apps, and even which apps they want to have displayed, all organized by different times of day (eg work time), place, calendar items, and so on.

Today, iPhone plays so many roles in our lives. It’s where we get information, how people reach us, and where we get things done. This is great, but it means our attention is being pulled in so many different directions and finding that balance between work and life can be tricky,” said Apple’s Craig Federighi in the WWDC keynote earlier this month. “We want to free up space to focus and help you be in the moment.” How well that gets used, and how much other platforms like Google follow suit, will be interesting to see play out. It feels, in any case, like it could be the start of something.

And, serendipitously — or maybe because this is some kind of zeitgeist — this is also playing into what has built and is building. 

Mathias Mikkelsen, the Oslo-based founder of, first came up for his idea for Timely (which had also been the original name of the whole startup) when he was working as a designer in the ad industry, one of those jobs that needed to track what he was working on, and for how long, in order to get paid.

He said he knew the whole system as it existed was inefficient: “I just thought it was insane how cumbersome and old it was. But at the same time how important it was for the task,” he said.

The guy had an entrepreneurial itch that he was keen to scratch, and this idea would become the salve to help him. Mikkelsen was so taken with building a startup around time management, that he sold his apartment in Oslo and moved himself to San Francisco to be where he believed was the epicenter of startup innovation. He tells me he lived off the proceeds of his flat for two years “in a closet” in a hacker house, bootstrapping Timely, until eventually getting into an accelerator (500 Startups) and subsequently starting to raise money. He eventually moved back to Oslo after two years to continue growing the business, as well as to live somewhere a little more spacious.

The startup’s big technical breakthrough with Timely was to figure out an efficient way of tracking time for different tasks, not just time worked on anything, without people having to go through a lot of data entry.

The solution: to integrate with a person’s computer, plus a basic to-do schedule for a day or week, and then match up which files are open when to determine how long one works for one client or another. Phone or messaging conversations, for the moment, are not included, and neither are the contents of documents — just the titles of them. Nor is data coming from wearable devices, although you could see how that, too, might prove useful.

The basic premise is to be personalised, so managers and others cannot use Timely to track exactly what people are doing, although they can track and bill for those billable hours. All this is important, as it also will feed into how DeWo and Glue will work.

The startup’s big conceptual breakthrough came around the same time: Getting time tracking or any productivity right “has never been a UI problem,” Mikkelsen said. “It’s a human nature problem.” This is where the AI comes in, to nudge people towards something they identify as important, and nudge them away from work that might not contribute to that. Tackling bigger issues beyond time are essential to improving productivity overall, which is why now wants to extend to apps for carving out time for deep thinking and creative thinking.

While it might seem to be a threat that a company like Apple has identified the same time management predicament that has, and is looking to solve that itself, Mikkelsen is not fazed. He said he thinks of Focus as not unlike Apple’s work on Health: there will be ways of feeding information into Apple’s tool to make it work better for the user, and so that will be’s opportunity to hopefully grow, not cannibalize, its own audience with Timely and its two new apps. It is, in a sense, a timely disruption.

“Memory’s proven software is already redefining how businesses around the world track, plan and manage their time. We look forward to working with the team to help new markets profit from the efficiencies, insights and transparency of a Memory-enabled workforce,” said Arild Engh, a partner at Melesio, in a statement.

Kjartan Rist,  a partner at Concentric, added: “We continue to be impressed with Memory’s vision to build and launch best-in-class products for the global marketplace. The company is well on its way to becoming a world leader in workplace productivity and collaboration, particularly in light of the remote and hybrid working revolution of the last 12 months. We look forward to supporting Mathias and the team in this exciting new chapter.”

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A.I. drug discovery platform Insilico Medicine announces $255 million in Series C funding



Insilico Medicine, an A.I-based platform for drug development and discovery announced $255 million in Series C financing on Tuesday. The massive round is reflective of a recent breakthrough for the company: proof that it’s A.I based platform can create a new target for a disease, develop a bespoke molecule to address it, and begin the clinical trial process. 

It’s also yet another indicator that A.I and drug discovery continues to be especially attractive for investors. 

Insilico Medicine is a Hong Kong-based company founded in 2014 around one central premise: that A.I assisted systems can identify novel drug targets for untreated diseases, assist in the development of new treatments, and eventually predict how well those treatments may perform in clinical trials. Previously, the company had raised $51.3 million in funding, according to Crunchbase

Insilico Medicine’s aim to use A.I to drive drug development isn’t particularly new, but there is some data to suggest that the company might actually accomplish that gauntlet of discovery all the way through trial prediction. In 2020, the company identified a novel drug target for idiopathic pulmonary fibrosis, a disease in which tiny air sacs in the lungs become scarred, which makes breathing laborious. 

Two A.I-based platforms first identified 20 potential targets, narrowed it down to one, and then designed a small molecule treatment that showed promise in animal studies. The company is currently filing an investigational new drug application with the FDA and will begin human dosing this year, with aims to begin a clinical trial late this year or early next year. 

The focus here isn’t on the drug, though, it’s on the process. This project condensed the process of preclinical drug development that typically takes multiple years and hundreds of millions of dollars into just 18 months, for a total cost of about $2.6 million. Still, founder Alex Zhavoronkov doesn’t think that Insilico Medicine’s strengths lie primarily in accelerating preclinical drug development or reducing costs: its main appeal is in eliminating an element of guesswork in drug discovery, he suggests. 

“Currently we have 16 therapeutic assets, not just IPF,” he says. “It definitely raised some eyebrows.” 

“It’s about the probability of success,” he continues. “So the probability of success of connecting the right target to the right disease with a great molecule is very, very low. The fact that we managed to do it in IPF and other diseases I can’t talk about yet – it increases confidence in A.I in general.” 

Bolstered partially by the proof-of-concept developed by the IPF project and enthusiasm around A.I based drug development, Insilico Medicine attracted a long list of investors in this most recent round. 

The round is led by Warburg Pincus, but also includes investment from Qiming Venture Partners, Pavilion Capital, Eight Roads Ventures, Lilly Asia Ventures, Sinovation Ventures, BOLD Capital Partners, Formic Ventures, Baidu Ventures, and new investors. Those include CPE, OrbiMed, Mirae Asset Capital, B Capital Group, Deerfield Management, Maison Capital, Lake Bleu Capital, President International Development Corporation, Sequoia Capital China and Sage Partners. 

This current round was oversubscribed four-fold, according to Zhavoronkov. 

A 2018 study of 63 drugs approved by the FDA between 2009 and 2018 found that the median capitalized research and development investment needed to bring a drug to market was $985 million, which also includes the cost of failed clinical trials. 

Those costs and the low likelihood of getting a drug approved has initially slowed the process of drug development. R&D returns for biopharmaceuticals hit a low of 1.6 percent in 2019, and bounced back to a measly 2.5 percent in 2020 according to a 2021 Deloitte report

Ideally, Zhavoronkov imagines an A.I-based platform trained on rich data that can cut down on the amount of failed trials. There are two major pieces of that puzzle: PandaOmics, an A.I platform that can identify those targets; and Chemistry 42, a platform that can manufacture a molecule to bind to that target.

“We have a tool, which incorporates more than 60 philosophies for target discovery,” he says. 

“You are betting something that is novel, but at the same time you have some pockets of evidence that strengthen your hypothesis. That’s what our A.I does very well.” 

Although the IPF project has not been fully published in a peer-reviewed journal, a similar project published in Nature Biotechnology was. In that paper, Insilco’s deep learning model was able to identify potential compounds in just 21 days

The IPF project is a scale-up of this idea. Zhavoronkov doesn’t just want to identify molecules for known targets, he wants to find new ones and shepherd them all the way through clinical trials. And, indeed, also to continue to collect data during those clinical trials that might improve future drug discovery projects. 

“So far nobody has challenged us to solve a disease in partnership” he says. “If that happens, I’ll be a very happy man.” 

That said, Insilico Medicine’s approach to novel target discovery has been used piecemeal, too. For instance, Insilico Medicine has collaborated with Pfizer on novel target discovery, and Johnson and Johnson on small molecule design and done both with Taisho Pharmaceuticals. Today, the company also announced a new partnership with Teva Branded Pharmaceutical Products R&D, Inc. Teva will aim to use PandaOmics to identify new drug targets.

That said, it’s not just Insilico Medicine raking in money and partnerships. The whole field of A.I-based novel targets has been experiencing significant hype.

In 2019 Nature noted that at least 20 partnerships between major drug companies and A.I drug discovery tech companies had been reported. In 2020, investment in A.I companies pursuing drug development increased to $13.9 billion, a four-fold increase from 2019, per Stanford University’s Artificial Intelligence Index annual report. R&D cost 

Drug discovery projects received the greatest amount of private A.I investment in 2020, a trend that can partially be attributed to the pandemic’s need for rapid drug development. However, the roots of the hype predate Covid-19. 

Zhavorokov is aware that A.I based drug development is riding a bit of a hype wave right now. “Companies without substantial evidence supporting their A.I powered drug discovery claims manage to raise very quickly,” he notes. 

Insilico Medicine, he says, can distinguish itself based on the quality of its investors. “Our investors don’t gamble,” he says. 

But like so many other A.I-based drug discovery platforms, we’ll have to see whether they make it through the clinical trial churn. 

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Four Reasons SaaS Analytics Is Exploding



Click to learn more about author Josh Good.

Shifting to the cloud helped countless businesses keep the lights on in 2020, as widespread disruption gave them the means and motive to fully embrace Software-as-a-Service (SaaS) across their business. Cloud services are now serving as the foundation for how enterprises adopt products and services, including analytics solutions. For those watching the market, it is clear that Gartner is right that public cloud services will support as much as 90% of data and analytics innovations by 2022. Here are the four trends spurring that demand for cloud-based SaaS analytics. 

1. Augmented analytics supports human capital

The need to enable employees to decide and act through a combination of artificial intelligence (AI), machine learning (ML), and analytics is growing. They are among the technologies that are giving businesses a competitive advantage, but their output – valuable and highly actionable insights – would be limited without cloud technology, specifically SaaS solutions.

The pressure for businesses to digitally transform has accelerated during the pandemic, where maturing technologies, such as AI and natural language understanding (NLU), help workers manage incredible volumes of data and tap into insights faster. The traditional methods of conducting infrastructure assessments and regression testing for any new analytics upgrades constrains companies and keeps teams from evolving their analytics adoption at the speed of business. Instead of growing at a pace that allows them to achieve active intelligence from their data, businesses would be held back and ultimately lose any advantage they may have in the marketplace. By using augmented analytics in conjunction with their other SaaS services, they can get the most out of their data and put their innovations to work.

2. Suppliers and stakeholders gain more from collaboration

Most public and private organizations are not set up to share analysis with suppliers, partners, or customers, but shared information often leads to improvements throughout the supply chain, helping organizations get a complete picture of their operational environments. SaaS analytics makes it possible to share data without hurdles and within a governed framework to establish and maintain data security and integrity.

A great example of this is digital supply chain company SDI, which aggregates data in a technology platform to give spend data to all of its buyers, helping them to make more informed decisions. The company also shares through the cloud up-to-date information and visualizations to customers on KPIs and reporting metrics, offering crystal-clear transparency to all of its customers at any moment.

3. SaaS makes analytics scalable and easier to adopt

In an on-premises world, businesses were advised to start small and focus on well-defined projects where analytics could make an immediate impact. Once they saw a decent return on investment (ROI), they could then scale up. While this might have seemed like the best strategy five years ago, the upfront investment was immense, even for a small project. And if that project was successful, the next step involved more time and effort to upgrade infrastructure to support the expanded use cases. 

SaaS analytics lowers the technical barrier to entry, as well as the level of investment required for initial projects, allowing organizations to achieve ROI much sooner and expand on demand. Today, the process of moving to an enterprise-grade, SaaS analytics software takes only minutes and the right motivation.

4. SaaS solutions are more secure and reliable

The data that corporations possess is their most valuable possession. If managers have any doubt about the value of their businesses’ data, they should look no further than the measures cybercriminals take to steal it – and the amount of money businesses pay to get it back. Cyberattacks are less effective in a SaaS environment, which lowers the data security risk. SaaS software providers that are trusted for stringent security are certified in SOC II and ISO27001, strict standards that engender confidence that their analytics software can protect their data in a cloud environment. 

Saas Delivers What Businesses Need in the Digital Era

The innovations that we can expect from SaaS analytics within the next year will be profound. SaaS analytics is catalyzing digital transformation, supporting fast-paced innovation, and driving the real-time insight that businesses need to make up-to-the-second decisions. Its growing adoption is already telling us everything we need to know about the enterprise appetite for more real-time and scalable, data-driven decision-making.

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