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Socure raises $100M at $1.3B valuation, proving identity verification is hotter than ever
The COVID-19 pandemic has accelerated digital adoption in a way that no one could have ever anticipated, and as more people conduct more services online and via mobile devices, businesses have had to work even harder to validate users and security. One company working to serve that need, Socure — which uses AI and machine learning to verify identities — announced Tuesday that it has raised $100 million in a Series D funding round at a $1.3 billion valuation.
Given how much of our lives have shifted online, it’s no surprise that the U.S. digital identity market is projected to increase to over $30 billion by 2023 from just under $15 billion in 2019, according to One World Identity. This has led to skyrocketing demand for the services provided by identity verification companies.
The founding team set out on a mission to be able to verify 100% of “good IDs” in real-time while “completely eliminating” identity fraud across the internet.
Historically, Socure has been focused on the financial services industry, but it plans to use its new capital to further expand into “every consumer-facing vertical” including online gaming, healthcare, telco, e-commerce and on-demand services.
The startup’s predictive analytics platform applies artificial intelligence and machine-learning techniques with online/offline data intelligence (from email, phone, address, IP, device, velocity and the broader internet) to verify that people are, in fact, who they say they are when applying for various accounts.
Today, Socure has more than 350 customers including three top five banks, six top 10 card issuers, a “top” credit bureau and over 75 fintechs such as Varo Money, Public, Chime and Stash.
In 2020, Socure grew its customer base by over 85% year over year and expanded its workforce by over 50% to about 240 people today.
Accel led Socure’s latest financing, which included participation from existing backers Commerce Ventures, Scale Venture Partners, Flint Capital, Citi Ventures, Wells Fargo Strategic Capital, Synchrony, Sorenson, Two Sigma Ventures and others.
The round comes less than six months after the company raised $35 million in a round led by Sorenson Ventures, and brings the New York-based company’s total raised to $196 million since its 2012 inception.
Socure founder and CEO Johnny Ayers says his company’s identity management products can help B2C enterprises achieve know-your-customer (KYC) auto-approval rates of up to 97%. This means that financial institutions can more easily capture fraud, for example, via Socure’s single API. The company also claims that by more easily verifying thin-file (those without much credit history) and young consumers, it can help reduce the underbanked population.
The pandemic and resulting shutdowns resulted in a massive demand for trusted digital identity, Ayers believes.
“This growth tracks with a larger trend marked by the broad migration of businesses to accept applications and onboard new customers online, with many companies accelerating their transformation from digital-first to digital-only,” he told TechCrunch.
Overall fraud attempts among Socure’s existing customer base nearly doubled in the second quarter of 2020 — with certain segments seeing rises as high as 150%, according to Ayers.
“These instances did not involve actual fraud but instead were flagged by Socure as suspicious and blocked prior to inflicting damage,” he said.
Looking ahead, the company plans to use its new capital to also enhance its product offering as it continues to develop patents.
Accel partner Amit Jhawar will join Socure’s board as part of the funding round.
In a blog post, Jhawar described Socure as “a purpose-built solution designed to handle the wave of new online users because its machine learning models have learned from every identity it has already seen.”
As former COO at Braintree and general manager at Venmo, Jhawar knows a thing or two about the importance of identity verification, especially in the financial services space.
He wrote: “I knew immediately that the Socure solution would be a game-changer because the solution can be used in every step of the customer lifecycle, from account creation to login to transaction.”
Socure also has hinted that it has an IPO in its future.
In a written statement, Ayers said: “We are incredibly grateful for the chance to innovate and partner to solve this problem with some of the greatest companies in the world and are energized for the opportunities that lay ahead for Socure, especially as we make our march to a potential IPO.”
Via email, he told TechCrunch that the company will “potentially” look at public markets in 2022 or 2023, when it feels “the time is right for the business.”
The story was updated post-publication with live comments from Socure
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Flatfile scores $7.6M seed investment to simplify data onboarding
One of the huge challenges companies like enterprise SaaS vendors face with new customers is getting customer data into their service. It’s a problem that Flatfile founders faced firsthand in their jobs, and they decided to solve it. Today, the company announced a healthy $7.6 million seed investment to expand on that vision.
The company also announced the release of its latest product, called Concierge.
Two Sigma Ventures led the investment, with participation from previous investors Afore Capital, Designer Fund and Gradient Ventures (Google’s AI-focused venture fund).
Company CEO David Boskovic says he and co-founder Eric Crane recognized that this is a problem just about every company faces. Let’s say you sign up for a CRM tool like HubSpot (which is a Flatfile customer). Your first step is to get your customer data into the new service.
As Boskovic points out, if you have thousands of existing customers that can be a real problem, often involving days or even weeks to prepare the data, depending on the size of your customer base. It typically includes importing your data from an existing source, then manually moving it to an Excel spreadsheet.
“What we’re trying to solve for at Flatfile is automating that entire process. You can drop in any data that you have and get it into a new product, and what that solves from a market perspective is the speed of adopting new software,” Boskovic told TechCrunch.
Image Credit: Flatfile
He says they have automated the process to the point it usually takes just a few minutes to process the data, If there are problems that Flatfile can’t solve, it presents the issue to the user who can fix it and move on.
The founders realized that not every use case is going to involve a simple one-to-one data transfer, so they created their new product called Concierge to help companies manage more complex data integration scenarios for their customers.
“What we do is we provide a bridge between disparate data formats that are a little bit more complex and let our customers collaborate with their new customers that they are onboarding to bring the data to the right state to use it in the new system,” Boskovic explained.
Whatever they are doing, it seems to be working. The company launched in 2018 and today has 160 customers with 300 sitting on a waiting list. It has increased that customer count by 5x since the beginning of the year in the middle of a pandemic.
Any product that reduces labor and increases efficiency and collaboration in a digital context is going to get the attention of customers right now, and Flatfile is seeing a huge spike in interest in spite of the current economy. “We’re helping onboard customers quickly and more efficiently. And our Concierge service can also help reduce in-person touch points by reducing this long, typical data onboarding process,” Boskovic said.
The company has not had to change the way it has worked because of the pandemic, as it has been a distributed workforce from day one. In fact, Boskovic is in Denver and co-founder Eric Crane is based in Atlanta. The startup currently has 14 employees, but plans to fill at least 10 roles this year.
“We’ve got a pretty aggressive hiring map. Our pipeline is bigger than we can handle from a sales perspective,” he said. That means they will be looking to fill sales, marketing and product jobs.
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Two Sigma leads $12m series A for expert knowledge network NewtonX
Knowledge is the fuel of business. Every decision requires a full understanding of the data underlying it, and that means reaching out not only to an organization’s own staff for insight, but also to experts in the wider world. Management consultants, research agencies, and data providers make hundreds of billions of dollars per year attempting to answer key questions for business executives.
Sometimes they are successful, but many times, finding the right expert can be vexing. For the most important decisions, having multiple experts or even hundreds of experts provide their opinion might be critical to success.
Germain Chastel and Sascha Eder know the problem well. Former McKinsey consultants, they worked with some of the top technology companies in the Valley attempting to answer their questions — but oftentimes struggled to do so given the unique problems that confront those organizations. “We realized it was really hard to find experts who could teach them something and had the insights that were relevant,” Chastel explained.
In early 2017, the two left McKinsey and eventually joined forces with Anuja Ketan, and together the trio formed NewtonX. NewtonX is a “knowledge access platform” which attempts to intelligently answer questions posed to it by business clients. Clients answer a carefully calibrated series of questions to properly vet and scope a query, and then NewtonX farms it out to it network of experts for insight.
That rapid-response network has now gotten the attention of Two Sigma Ventures, the venture wing of the high-flying algorithmic-trading hedge fund, which led a $12 million Series A round into New York City-based NewtonX. That’s a follow up to a $3 million seed round co-led by Third Prime Capital and Xfund last year.
Today, the company offers two main product lines. First is what it calls Expert Calls, which are similar to the traditional expert network offering of companies like GLG. Here, a client answers a series of structured questions to determine a single expert to talk to and get feedback from.
The more interesting product to me, and the one representing 70% of the startup’s revenue right now, is Expert Surveys. With this product, the goal is to ask a business question to a wider number of experts who might provide a variety of responses. So, for instance, NewtonX could potentially answer a query such as how CIOs at large Fortune 500 companies are budgeting for cybersecurity this year.
Where NewtonX gets interesting is that it doesn’t want to just casually facilitate these calls and surveys, but instead, the startup wants to build out a true knowledge graph that can better answer questions faster with each activity on the platform. As the platform gets smarter about knowledge, the idea is that on-boarding a new client or initiating a new survey or question will be faster since the platform will already know many of the nuances of that particular field of business.
Over the two and a half years since the company’s founding, it has found wide support among businesses. It counts Microsoft, 23&Me, and Gartner as public clients, and also has a list of 20 corporates already on the platform. Chastel told me that nine of the top ten management consulting firms have also used NewtonX services, and many top research firms have also used the product.
The NewtonX team. Courtesy of NewtonX
Early revenues has allowed the company to expand early. It has 32 employees at its offices near Grand Central, and Chastel noted to me that a majority of employees and a majority of managers are women. He said that the firm’s technology to identify experts on the web is also the basis for their own recruiting efforts.
With the new funding, the company intends to grow to 100 head count locally, and also expand out is client success and expert success teams.
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Pro.com raises $33M for its home improvement platform
Pro.com is basically a general contractor for the age of Uber and Prime Now. While the company started as a marketplace for hiring home improvement professionals, it has now morphed into a general contractor and serves Denver, Phoenix, San Francisco, San Jose and Seattle. Today, Pro.com announced that it has raised a $33 million Series B round led by WestRiver Group, Goldman Sachs and Redfin. Previous investors DFJ, Madrona Venture Group, Maveron and Two Sigma Ventures also participated.
WestRiver founder Erik Anderson, Redfin CEO Glenn Kelman and former Microsoft exec Charlotte Guyman are joining the Pro.com board.
“Many of Redfin’s customers struggle to get professional renovation services, so we know firsthand that Pro.com’s market opportunity is massive,” writes Redfin’s Kelman. “Pro.com and Redfin share a commitment to combining technology and local, direct services to best take care of customers.”
The company tells me that the round caps off a successful 2018, where Pro.com saw its job bookings grow by 275 percent over 2017, a number that was also driven by its expansion beyond the Seattle market (as well as the good economic climate that surely helped in driving homeowners to tackle more home improvement projects). The company now has 125 employees.
With this funding round, Pro.com has now raised a total of $60 million. It’ll use the funding to enter more markets, with Portland, Oregon being next on the list, and expand its team as it goes along.
It’s no secret that the home improvement market could use a bit of a jolt. The market is extremely local and fragmented — and finding the right contractor for any major project is a long and difficult process, where the outcome is never quite guaranteed. The process has enough vagaries that many people never get around to actually commissioning their projects. Pro.com wants to change that with a focus on transparency and technology. That’s a startup that’s harder to scale than the marketplace the company started out with, but it also gives the company a chance to establish itself as one of the few well-known brands in this space.
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Former Facebook engineer picks up $15M for AI platform Spell
In 2016, Serkan Piantino packed up his desk at Facebook with hopes to move on to something new. The former director of Engineering for Facebook AI Research had every intention to keep working on AI, but quickly realized a huge issue.
Unless you’re under the umbrella of one of these big tech companies like Facebook, it can be very difficult and incredibly expensive to get your hands on the hardware necessary to run machine learning experiments.
So he built Spell, which today received $15 million in Series A funding led by Eclipse Ventures and Two Sigma Ventures.
Spell is a collaborative platform that lets anyone run machine learning experiments. The company connects clients with the best, newest hardware hosted by Google, AWS and Microsoft Azure and gives them the software interface they need to run, collaborate and build with AI.
“We spent decades getting to a laptop powerful enough to develop a mobile app or a website, but we’re struggling with things we develop in AI that we haven’t struggled with since the 70s,” said Piantino. “Before PCs existed, the computers filled the whole room at a university or NASA and people used terminals to log into a single main frame. It’s why Unix was invented, and that’s kind of what AI needs right now.”
In a meeting with Piantino this week, TechCrunch got a peek at the product. First, Piantino pulled out his MacBook and opened up Terminal. He began to run his own code against MNIST, which is a database of handwritten digits commonly used to train image detection algorithms.
He started the program and then moved over to the Spell platform. While the original program was just getting started, Spell’s cloud computing platform had completed the test in less than a minute.
The advantage here is obvious. Engineers who want to work on AI, either on their own or for a company, have a huge task in front of them. They essentially have to build their own computer, complete with the high-powered GPUs necessary to run their tests.
With Spell, the newest GPUs from Nvidia and Google are virtually available for anyone to run their tests.
Individual users can get on for free, specify the type of GPU they need to compute their experiment and simply let it run. Corporate users, on the other hand, are able to view the runs taking place on Spell and compare experiments, allowing users to collaborate on their projects from within the platform.
Enterprise clients can set up their own cluster, and keep all of their programs private on the Spell platform, rather than running tests on the public cluster.
Spell also offers enterprise customers a “spell hyper” command that offers built-in support for hyperparameter optimization. Folks can track their models and results and deploy them to Kubernetes/Kubeflow in a single click.
But perhaps most importantly, Spell allows an organization to instantly transform their model into an API that can be used more broadly throughout the organization, or used directly within an app or website.
The implications here are huge. Small companies and startups looking to get into AI now have a much lower barrier to entry, whereas large traditional companies can build out their own proprietary machine learning algorithms for use within the organization without an outrageous upfront investment.
Individual users can get on the platform for free, whereas enterprise clients can get started for $99/month per host you use over the course of a month. Piantino explains that Spell charges based on concurrent usage, so if the customer has 10 concurrent things running, the company considers that the “size” of the Spell cluster and charges based on that.
Piantino sees Spell’s model as the key to defensibility. Whereas many cloud platforms try to lock customers in to their entire suite of products, Spell works with any language framework and lets users plug and play on the platforms of their choice by simply commodifying the hardware. In fact, Spell doesn’t even share with clients which cloud cluster (Microsoft Azure, Google or AWS) they’re on.
So, on the one hand the speed of the tests themselves goes up based on access to new hardware, but, because Spell is an agnostic platform, there is also a huge advantage in how quickly one can get set up and start working.
The company plans to use the funding to further grow the team and the product, and Piantino says he has his eye out for top-tier engineering talent, as well as a designer.
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Timescale is leading the next wave of NYC database tech
Data is the lifeblood of the modern corporation, yet acquiring, storing, processing, and analyzing it remains a remarkably challenging and expensive project. Every time data infrastructure finally catches up with the streams of information pouring in, another source and more demanding decision-making makes the existing technology obsolete.
Few cities rely on data the same way as New York City, nor has any other city so shaped the technology that underpins our data infrastructure. Back in the 1960s, banks and accounting firms helped to drive much of the original computation industry with their massive finance applications. Today, that industry has been supplanted by finance and advertising, both of which need to make microsecond decisions based on petabyte datasets and complex statistical models.
Unsurprisingly, the city’s hunger for data has led to waves of database companies finding their home in the city.
As web applications became increasingly popular in the mid-aughts, SQL databases came under increasing strain to scale, while also proving to be inflexible in terms of their data schemas for the fast-moving startups they served. That problem spawned Manhattan-based MongoDB, whose flexible “NoSQL” schemas and horizontal scaling capabilities made it the default choice for a generation of startups. The company would go on to raise $311 million according to Crunchbase, and debuted late last year on NASDAQ, trading today with a market cap of $2 billion.
At the same time that the NoSQL movement was hitting its stride, academic researchers and entrepreneurs were exploring how to evolve SQL to scale like its NoSQL competitors, while retaining the kinds of features (joining tables, transactions) that make SQL so convenient for developers.
One leading company in this next generation of database tech is New York-based Cockroach Labs, which was founded in 2015 by a trio of former Square, Viewfinder, and Google engineers. The company has gone on to raise more than $50 million according to Crunchbase from a luminary list of investors including Peter Fenton at Benchmark, Mike Volpi at Index, and Satish Dharmaraj at Redpoint, along with GV and Sequoia.
While web applications have their own peculiar data needs, the rise of the internet of things (IoT) created a whole new set of data challenges. How can streams of data from potentially millions of devices be stored in an easily analyzable manner? How could companies build real-time systems to respond to that data?
Mike Freedman and Ajay Kulkarni saw that problem increasingly manifesting itself in 2015. The two had been roommates at MIT in the late 90s, and then went on separate paths into academia and industry respectively. Freedman went to Stanford for a PhD in computer science, and nearly joined the spinout of Nicira, which sold to VMware in 2012 for $1.26 billion. Kulkarni joked that “Mike made the financially wise decision of not joining them,” and Freedman eventually went to Princeton as an assistant professor, and was awarded tenure in 2013. Kulkarni founded and worked at a variety of startups including GroupMe, as well as receiving an MBA from MIT.
The two had startup dreams, and tried building an IoT platform. As they started building it though, they realized they would need a real-time database to process the data streams coming in from devices. “There are a lot of time series databases, [so] let’s grab one off the shelf, and then we evaluated a few,” Kulkarni explained. They realized what they needed was a hybrid of SQL and NoSQL, and nothing they could find offered the feature set they required to power their platform. That challenge became the problem to be solved, and Timescale was born.
In many ways, Timescale is how you build a database in 2018. Rather than starting de novo, the team decided to build on top of Postgres, a popular open-source SQL database. “By building on top of Postgres, we became the more reliable option,” Kulkarni said of their thinking. In addition, the company opted to make the database fully open source. “In this day and age, in order to get wide adoption, you have to be an open source database company,” he said.
Since the project’s first public git commit on October 18, 2016, the company’s database has received nearly 4,500 stars on Github, and it has raised $16.1 million from Benchmark and NEA .
Far more important though are their customers, who are definitely not the typical tech startup roster and include companies from oil and gas, mining, and telecommunications. “You don’t think of them as early adopters, but they have a need, and because we built it on top of Postgres, it integrates into an ecosystem that they know,” Freedman explained. Kulkarni continued, “And the problem they have is that they have all of this time series data, and it isn’t sitting in the corner, it is integrated with their core service.”
New York has been a strong home for the two founders. Freedman continues to be a professor at Princeton, where he has built a pipeline of potential grads for the company. More widely, Kulkarni said, “Some of the most experienced people in databases are in the financial industry, and that’s here.” That’s evident in one of their investors, hedge fund Two Sigma. “Two Sigma had been the only venture firm that we talked to that already had built out their own time series database,” Kulkarni noted.
The two also benefit from paying customers. “I think the Bay Area is great for open source adoption, but a lot of Bay Area companies, they develop their own database tech, or they use an open source project and never pay for it,” Kulkarni said. Being in New York has meant closer collaboration with customers, and ultimately more revenues.
Open source plus revenues. It’s the database way, and the next wave of innovation in the NYC enterprise infrastructure ecosystem.
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