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Meet Justos, the new Brazilian insurtech that just got backing from the CEOs of 7 unicorns
Here in the U.S. the concept of using a driver’s data to decide the cost of auto insurance premiums is not a new one.
But in markets like Brazil, the idea is still considered relatively novel. A new startup called Justos claims it will be the first Brazilian insurer to use drivers’ data to reward those who drive safely by offering “fairer” prices.
And now Justos has raised about $2.8 million in a seed round led by Kaszek, one of the largest and most active VC firms in Latin America. Big Bets also participated in the round, along with the CEOs of seven unicorns, including Assaf Wand, CEO and co-founder of Hippo Insurance; David Vélez, founder and CEO of Nubank; Carlos Garcia, founder and CEO of Kavak; Sergio Furio, founder and CEO of Creditas; Patrick Sigrist, founder of iFood and Fritz Lanman, CEO of ClassPass. (There’s a seventh CEO who wishes to remain anonymous). Senior executives from Robinhood, Stripe, Wise, Carta and Capital One also put money in the round.
Serial entrepreneurs Dhaval Chadha, Jorge Soto Moreno and Antonio Molins co-founded Justos, having most recently worked at various Silicon Valley-based companies including ClassPass, Netflix and Airbnb.
“While we have been friends for a while, it was a coincidence that all three of us were thinking about building something new in Latin America,” Chadha said. “We spent two months studying possible paths, talking to people and investors in the United States, Brazil and Mexico, until we came up with the idea of creating an insurance company that can modernize the sector, starting with auto insurance.”
Ultimately, the trio decided that the auto insurance market would be an ideal sector considering that in Brazil, an estimated more than 70% of cars are not insured.
The process to get insurance in the country, by any accounts, is a slow one. It takes up to 72 hours to receive initial coverage and two weeks to receive the final insurance policy. Insurers also take their time in resolving claims related to car damages and loss due to accidents, the entrepreneurs say. They also charge that pricing is often not fair or transparent.
Justos aims to improve the whole auto insurance process in Brazil by measuring the way people drive to help price their insurance policies. Similar to Root here in the U.S., Justos intends to collect users’ data through their mobile phones so that it can “more accurately and assertively price different types of risk.” This way, the startup claims it can offer plans that are up to 30% cheaper than traditional plans, and grant discounts each month, according to the driving patterns of the previous month of each customer.
“We measure how safely people drive using the sensors on their cell phones,” Chadha said. “This allows us to offer cheaper insurance to users who drive well, thereby reducing biases that are inherent in the pricing models used by traditional insurance companies.”
Justos also plans to use artificial intelligence and computerized vision to analyze and process claims more quickly and machine learning for image analysis and to create bots that help accelerate claims processing.
“We are building a design-driven, mobile first and customer experience that aims to revolutionize insurance in Brazil, similar to what Nubank did with banking,” Chadha told TechCrunch. “We will be eliminating any hidden fees, a lot of the small text and insurance-specific jargon that is very confusing for customers.”
Justos will offer its product directly to its customers as well as through distribution channels like banks and brokers.
“By going direct to consumer, we are able to acquire users cheaper than our competitors and give back the savings to our users in the form of cheaper prices,” Chadha said.
Customers will be able to buy insurance through Justos’ app, website or even WhatsApp. For now, the company is only adding potential customers to a waitlist but plans to begin selling policies later this year..
During the pandemic, the auto insurance sector in Brazil declined by 1%, according to Chadha, who believes that indicates “there is latent demand raring to go once things open up again.”
Justos has a social good component as well. Justos intends to cap its profits and give any leftover revenue back to nonprofit organizations.
The company also has an ambitious goal: to help make insurance become universally accessible around the world and the roads safer in general.
“People will face everyday risks with a greater sense of safety and adventure. Road accidents will reduce drastically as a result of incentives for safer driving, and the streets will be safer,” Chadha said. “People, rather than profits, will become the focus of the insurance industry.”
Justos plans to use its new capital to set up operations, such as forming partnerships with reinsurers and an insurance company for fronting, since it is starting as an MGA (managing general agent).
It’s also working on building out its products such as apps, its back end and internal operations tools, as well as designing all its processes for underwriting, claims and finance. Justos’ data science team is also building out its own pricing model.
The startup will be focused on Brazil, with plans to eventually expand within Latin America, then Iberia and Asia.
Kaszek’s Andy Young said his firm was impressed by the team’s previous experience and passion for what they’re building.
“It’s a huge space, ripe for innovation and this is the type of team that can take it to the next level,” Young told TechCrunch. “The team has taken an approach to building an insurance platform that blends being consumer-centric and data-driven to produce something that is not only cheaper and rewards safety but as the brand implies in Portuguese, is fairer.”
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Breinify announces $11M seed to bring data science to the marketing team
Breinify is a startup working to apply data science to personalization, and do it in a way that makes it accessible to nontechnical marketing employees to build more meaningful customer experiences. Today the company announced a funding round totaling $11 million.
The investment was led by Gutbrain Ventures and PBJ Capital with participation from Streamlined Ventures, CXO Fund, Amino Capital, Startup Capital Ventures and Sterling Road.
Breinify co-founder and CEO Diane Keng says that she and co-founder and CTO Philipp Meisen started the company to bring predictive personalization based on data science to marketers with the goal of helping them improve a customer’s experience by personalizing messages tailored to individual tastes.
“We’re big believers that the world, especially consumer brands, really need strong predictive personalization. But when you think about consumer big brands or the retailers that you buy from, most of them aren’t data scientists, nor do they really know how to activate [machine learning] at scale,” Keng told TechCrunch.
She says that she wanted to make this type of technology more accessible by hiding the complexity behind the algorithms powering the platform. “Instead of telling you how powerful the algorithms are, we show you [what that means for the] consumer experience, and in the end what that means for both the consumer and you as a marketer individually,” she said.
That involves the kind of customizations you might expect around website messaging, emails, texts or whatever channel a marketer might be using to communicate with the buyer. “So the AI decides you should be shown these products, this offer, this specific promotion at this time, [whether it’s] the web, email or SMS. So you’re not getting the same content across different channels, and we do all that automatically for you, and that’s [driven by the algorithms],” she said.
Breinify launched in 2016 and participated in the TechCrunch Disrupt Startup Battlefield competition in San Francisco that year. She said it was early days for the company, but it helped them focus their approach. “I think it gave us a huge stage presence. It gave us a chance to test out the idea just to see where the market was in regards to needing a solution like this. We definitely learned a lot. I think it showed us that people were interested in personalization,” she said. And although the company didn’t win the competition, it ended up walking away with a funding deal.
Today the startup is growing fast and has 24 employees, up from 10 last year. Keng, who is an Asian woman, places a high premium on diversity.
“We partner with about four different kinds of diversity groups right now to source candidates, but at the end of the day, I think if you are someone that’s eager to learn, and you might not have all the skills yet, and you’re [part of an under-represented] group we encourage everyone to apply as much as possible. We put a lot of work into trying to create a really well-rounded group,” she said.
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Emotion-detection software startup Affectiva acquired for $73.5M
Smart Eye, the publicly traded Swedish company that supplies driver monitoring systems for a dozen automakers, has acquired emotion-detection software startup Affectiva for $73.5 million in a cash-and-stock deal.
Affectiva, which spun out of the MIT Media Lab in 2009, has developed software that can detect and understand human emotion, which Smart Eye is keen to combine with its own AI-based eye-tracking technology. The companies’ founders see an opportunity to expand beyond driver monitoring systems — tech that is often used in conjunction with advanced driver assistance systems to track and measure awareness — and into the rest of the vehicle. Together, the technology could help them break into the emerging “interior sensing” market, which can be used to monitor the entire cabin of a vehicle and deliver services in response to the occupant’s emotional state.
Under the terms of the deal, $67.5 million will be paid with 2,354,668 new Smart Eye shares, of which 2,015,626 are to be issued upon closing of the transaction. The remaining 339,042 Smart Eye shares will be issued within two years of closing. About $6 million will be paid in cash once the deal closes in June 2021.
Affectiva and Smart Eye were competitors. A meeting at the technology trade show CES in 2020 put the two companies on a path to merge.
“Martin and I realized like, wow, we are on a path to compete with each other — and wouldn’t it be so much better if we joined forces?” Affective co-founder and CEO Dr. Rana el Kaliouby said in an interview Tuesday. “By joining forces, we kind of check all the boxes for what the OEMs are looking for with interior sensing, we leapfrog the competition and we have an opportunity to do this better and faster than we could have done it on our own.”
Boston-based Affectiva brings its emotion-detection software to the deal, which will allow Smart Eye to offer its existing automotive partners a variety of products. Smart Eye helps Affectiva move beyond the development and prototype work and into production contracts. Smart Eye has won 84 production contracts with 13 OEMs, including BMW and GM. Smart Eye, which has offices in Gothenburg, Detroit, Tokyo and Chongqing, China, also has a division that provides research organizations such as NASA with high-fidelity eye tracking systems for human factors research.
Smart Eye founder and CEO Martin Krantz said that European manufacturers building luxury and premium vehicles led the charge for driver monitoring systems.
“We see the same pattern repeating itself now for interior sensing,” Krantz said. “I think a large part of the early contracts will be European premium OEMs such as Mercedes, BMW, Audi, JLR, Porsche.” Krantz added that there are a number of other premium brands it will target in other regions, including Cadillac and Lexus.
The opportunity will initially be in passenger vehicles driven by humans and will eventually expand as greater levels of automated driving enter the market.
Affectiva, which employs 100 people at its offices in Boston and Cairo, also has another business unit that applies its emotio-detection software to media analytics. This division, which will be part of the deal and will operate separately, is profitable, Kaliouby said, noting the software is used by 70% of the world’s largest advertisers to measure and understand emotional responses to media content.
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Extra Crunch roundup: Jam City SPAC, startup PR, telemedicine market map, more
For this morning’s edition of The Exchange, Alex Wilhelm studied information recently released by mobile gaming studio Jam City as it prepares to go public in a $1.2 billion blank-check deal with DPCM Capital.
“Jam City is a bit like Zynga, but unless you are a mobile-gaming aficionado, you might not have heard of it,” he writes.
Since its launch, Jam City has raised upwards of $300 million, including a $145 million round in 2019. At the time, the company was riding high after signing a deal with Disney to adapt some of the media giant’s intellectual property, which includes brands like Marvel, Fox and Pixar.
Almost half of all Americans play mobile games, so Alex reviewed Jam City’s investor deck, a transcript of the investor presentation call and a press release to see how it stacks up against Zynga, which “has done great in recent quarters, including posting record revenue and bookings in the first three months of 2021.”
(Full disclosure: the second time I worked at a startup founded by Mark Pincus, Zinga slept behind my desk and I was one of her favorite dog-sitters.)
Thanks for reading Extra Crunch; I hope you have an excellent weekend!
Walter Thompson
Senior Editor, TechCrunch
@yourprotagonist
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5 ways to raise your startup’s PR game
Image Credits: Andrii Yalanskyi (opens in a new window)/ Getty Images
The ability to effectively communicate can make or break your launch. It will play a role in determining who wins a new space — you or a competitor.
So how do you make a splash? How do you stay relevant?
For one, you have to stop thinking that what you are up to is interesting.
Every early-stage startup must identify and evaluate a strategic advantage
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Whether you’re building a company or thinking about investing, it’s important to understand your strategic advantage.
In order to determine one, you should ask fundamental questions: What’s the long-term, sustainable reason that the company will stay in business?
As M&A accelerates, deal-makers are leveraging AI and ML to keep pace
Image Credits: Fanatic Studio (opens in a new window) / Getty Images
The global pandemic has changed the way we work, including how and where we work. For those involved in the mergers and acquisitions (M&A) industry, a notoriously relationship-driven business, this has meant in-person boardroom handshakes have been replaced by video conference calls, remote collaboration and potentially less travel in the future.
The pandemic has also accelerated digital transformation, and deal-makers have embraced digital tools to help them execute effectively.
The quickening pace of digital transformation is no longer about ensuring a competitive edge. Today, it’s also about business resilience. But what’s on the horizon, and how else will technology evolve to meet the needs of companies and deal-makers?
There are still many inefficiencies in managing M&A, but technologies such as artificial intelligence, especially machine learning, are helping to make the process faster and easier.
New Relic’s business remodel will leave new CEO with work to do
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Lew Cirne, New Relic’s founder and CEO, is stepping into the executive chairman role. He will be replaced by Bill Staples on July 1.
Cirne spent the last several years rebuilding the company’s platform and changing its revenue model, aiming for what he hopes is long-term success.
TechCrunch decided to dig into the company’s financials to see just what challenges Staples may face as he moves into the corner office. The resulting picture is one that shows a company doing hard work for a more future-aligned product map and business model, albeit one that may not generate the sort of near-term growth that gives Staples ample breathing room with public investors.
Fast growth pushes an unprofitable no-code startup into the public markets: Inside Monday.com’s IPO filing
Image Credits: Nigel Sussman (opens in a new window)
At long last, the Monday.com crew dropped an F-1 filing to go public in the United States. TechCrunch has long known that the company, which sells corporate productivity and communications software, has scaled north of $100 million in annual recurring revenue (ARR).
The countdown to its IPO filing — an F-1, because the company is based in Israel, rather than the S-1s filed by domestic companies — has been ticking for several quarters.
The Exchange has been riffling through the document since it came out, and we’ve picked up on a few things to explore.
The battle for voice recognition inside vehicles is heating up
Image Credits: Bryce Durbin
Until recently, integrating affordable voice-recognition software into an automobile was something from science fiction.
But last year, the percentage of vehicles offering in-car connected services reached 45%. By 2024, analysts predict cars with voice recognition will comprise 60% of the market.
Considering how much time many of us spend behind the wheel, there’s an infinite number of applications for the technology. For our latest Extra Crunch market map, we sized up the general market opportunity before creating a roster of major players and reaching out to investors to see where they’re placing bets.
Industrial automation startup Bright Machines hauls in $435M by going public via SPAC
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Bright Machines is going public via a SPAC-led combination that will see the 3-year-old company merge with SCVX, raising gross cash proceeds of $435 million in the process.
After the transaction is consummated, the startup will sport an anticipated equity valuation of $1.6 billion.
The Bright Machines news indicates that the great SPAC chill was not a deep freeze. And the transaction itself, in conjunction with the previously announced Desktop Metal blank-check deal, implies that there is space in the market for hardware startup liquidity via SPACs. Perhaps that will unlock more late-stage capital for hardware-focused upstarts.
We took a look at what Bright Machines does, and then the financial details that it shared as part of its news.
Want to double your rate of return? Seek counsel from experienced executives
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As a rule of thumb, it takes 7-8 years for a successful startup to achieve an exit. But there’s a simple way to speed up the clock: Bring in one or more founders who have previous executive experience.
According to data gathered by Rob Olson, partner and head of data strategy at venture engine M13, startups that have two or more experienced founders tend to exit 33% faster and raise 34% less capital.
“Combined, these two improvements can nearly double an investor’s rate of return,” says Olson.
Should startups build or buy telehealth infrastructure?
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Digital health in the U.S. got a huge boost from COVID-19 as more people started consulting physicians and urgent care providers remotely in the midst of lockdowns. So much so that McKinsey estimates that up to $250 billion of the current healthcare expenditure in the U.S. has the potential to be spent virtually.
The prominence of digital health is undoubtedly here to stay, but how it looks and feels from provider to provider is still a debate among sector startups.
But for providers who want to deliver care virtually across the country, it’s not as simple as adding a Zoom invite to an annual check-up. The process requires intention every step of the way — right from the clinicians delivering remote care to the choice of payment processor.
Help TechCrunch find the best email marketers for startups
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Email marketing has been with us for decades, but today it has been refined to a science and an art form.
If you’re an early-stage founder, it is one of the best ways to build and grow your direct relationship with your customer. You know how fickle the platforms can be. You can’t afford to mess this up.
So when and how should you think about doing email marketing, versus all of your other frantic priorities?
Here at Extra Crunch, we’re helping you find the answers. We launched a survey of founders who want to recommend a great email marketer or agency they have worked with to the rest of the startup world.
For companies that use ML, labeled data is the key differentiator
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When a company chooses supervised learning, it needs to have a strategy that allows it to label data as quickly as it acquires it.
Supervised learning is currently the most practical approach for most ML challenges, but it requires the crucial additional step of making raw data smart by labeling it.
How Expensify got to $100M in revenue by hiring ‘stem cells’ and not ‘cogs in a wheel’
Image Credits: Nigel Sussman
The influence of a founder on their company’s culture cannot be overstated. Everything from their views on the product and business to how they think about people affects how their company’s employees will behave, and since behavior, in turn, informs culture, the consequences of a founder’s early decisions can be far-reaching.
So it’s not surprising that Expensify has its own take on almost everything it does when you consider what its founder and CEO David Barrett learned early in his life: “Basically everyone is wrong about basically everything.”
As we saw in part 1 of this EC-1, this led him to the revelation that it’s easier to figure things out for yourself than finding advice that applies to you. Eventually, these insights would inform how he would go about shaping Expensify.
Inside Marqeta’s fascinating fintech IPO
Image Credits: Nigel Sussman (opens in a new window)
Marqeta, long a darling of the fintech market though less well known than some companies in its sector due to its infrastructure nature, filed to go public late last week
If you are not familiar with Marqeta, it powers the payment card tech behind products that you use, like Square, a key customer and driver of the unicorn’s growth. Marqeta exhibits a number of fascinating fintech characteristics (majority revenue from interchange, a rabidly competitive market) that make it very interesting to unspool.
May Mobility’s Edwin Olson and Nina Grooms Lee and Toyota AI Ventures’ Jim Adler on validating your startup idea
When a founder has a work history that includes the name of the parent company of one of their key investors, you probably assume that was one of the first deals to come together. Not so with May Mobility and Toyota AI Ventures, which connected for the company’s second seed round after May went out and raised its original seed purely on the strength of its own ideas and proposed solutions.
That’s one of the many interesting things we learned from speaking to May Mobility co-founder and CEO Edwin Olson, as well as Chief Product Officer Nina Grooms Lee and Toyota AI Ventures founding partner Jim Adler on an episode of Extra Crunch Live.
Extra Crunch Live goes down every Wednesday at 3 p.m. EDT/noon PDT. Our next episode is with Sequoia’s Shaun Maguire and Vise’s Samir Vasavada, and you can check out the upcoming schedule right here.
Meanwhile, read on for highlights from our chat with Olson, Grooms Lee and Adler, and then stay tuned at the end for a recording of the full session, including our live pitch-off.
WalkMe is going public: Let’s stroll through its numbers
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WalkMe is the second Israel-based technology company to file to go public this week: No-code startup Monday.com is also pursuing an American IPO.
WalkMe’s software provides visual overlays on websites that help users navigate the product in question. Per the company’s F-1 filing, other elements of its service that matter include its onboarding system, Workstation, or its “single interface to the applications within an enterprise and simplifies task completion through a natural language conversational interface and automation.” We’re including that last feature because it says “automation,” which, in the wake of the UiPath IPO, is a word worth watching. Investors are.
At a high level, WalkMe is a SaaS business, which means that when we digest its results we are digging into a modern software company. Let’s do just that.
Can Squarespace dodge the direct-listing value trap?
Image Credits: Nigel Sussman (opens in a new window)
Squarespace’s reference price has been set at $50 per share.
We went over Squarespace’s recently disclosed Q2 and full-2021 guidance and asked how its expectations compare to its reference-price-defined pre-trading valuation. Then, we set some stakes in the ground regarding historical direct-listing results and what we might expect from the company as it adds a third set of data to our quiver.
Let’s get into the numbers!
Mapping out one edtech company’s $200M bet on lifelong learning
Image Credits: Getty Images / DrAfter123
Mumbai-based Emeritus, an edtech company that works with universities to create online upskilling courses for employed folks, just spent a big chunk of cash to break into K-12.
Emeritus, which is part of the Eruditus group, announced this week that it plans to acquire iD Tech, a STEM education service for children. The acquisition, which has not yet closed, is estimated to be around $200 million and leaves iD Tech operating as an independent brand for now.
ID Tech brings a whole different set of customers to its umbrella: The startup offers courses for elementary through high-school students across the globe taught by college students in the U.S.
5 innovative fundraising methods for emerging VCs and PEs
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According to Versatile VC founder David Teten, five new strategies are gaining traction among fund managers looking to raise capital from family offices and high-net-worth individuals:
- Online communities and virtual events.
- Platforms that help other investors access your fund.
- Soliciting under the 506(c) designation.
- Launching a rolling fund.
- Crowdfunding from retail investors into a general partnership.
In a summary of a class he taught for the Oper8r VC fund accelerator, Teten offers actionable advice for anyone who wants to connect with pre-qualified investors.
Dear Sophie: What’s happening with visa application receipt notices?
Image Credits: Bryce Durbin/TechCrunch
Dear Sophie,
Our startup employs several individuals who are on work visas or have employment authorization. Many of them have been waiting for quite a while for the government to tell them their applications have been received.
Why? When will things be back on track? We have a few employees who are waiting for green cards, and a few F-1 visa holders who will be extending their OPT to STEM OPT.
Is there anything we can do?
— Patient in Pasadena
Arrival’s Denis Sverdlov on the new era of car manufacturing
Image Credits: Bryce Durbin
Electric vehicle company Arrival wants to break the current auto manufacturing model. Instead of one giant factory and an assembly line, Arrival’s commercial electric vans, buses and cars are robotically built in small, regional microfactories, of which the company wants to open 31 by the end of 2025.
If you want to achieve something radically more efficient, you have to go deeper, into complex, high-level computational algorithms that are not normally used in consumer-facing products.
The London-based company, founded in 2015, joined the ranks of EV companies going public via SPAC, merging with blank-check company CIIG Merger Corp. in March. UPS has already ordered 10,000 of Arrival’s robotically engineered vans, and the company recently signed a deal with Uber to create purpose-built EVs for ride-hail drivers.
Arrival founder Denis Sverdlov has been at the intersection of technological advancement and societal change before.
Chasing hype is human nature: The tyranny of startup trends
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The fear of missing out (FOMO) spreads faster than wildfire and often overwhelms rational decision-making.
In the VC community, investors look for lessons from disruptive startups they can use to identify other potential winners. But hype leads to bad decision-making, rushed due diligence and wishful thinking.
When and if those startups actually do well, “irrational FOMO takes over” because the initial assessment was based on bad information, says Victor Echevarria, a partner at Jackson Square Ventures. “Trends are addictive; to remain disciplined and avoid hype is to deny our innate instincts.”
It’s natural for investors to follow the crowd, but in the race to the bottom, FOMO can be high-octane fuel.
Robinhood’s epic Q1 growth explains its fundraising boom
Image Credits: Nigel Sussman (opens in a new window)
The Exchange explores Robinhood’s financial results using the lens of payment for order flow (PFOF) income, which the company said during a congressional hearing constitutes the majority of its revenues.
This particular revenue growth — or the lack thereof — is a good way to understand not only Robinhood’s own results but also its larger market. If Robinhood is seeing rapid growth and strong trading volumes, we can infer with some confidence that others in its space are enjoying a related, if not similar, level of interest.
For Public.com, eToro and others like Freetrade (as well as our own understanding), how Robinhood performed recently is key. So, let’s explore the data.
How to ensure data quality in the era of Big Data
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A little over a decade has passed since The Economist warned us that we would soon be drowning in data. The modern data stack has emerged as a proposed life-jacket for this data flood — spearheaded by Silicon Valley startups such as Snowflake, Databricks and Confluent.
Today, any entrepreneur can sign up for BigQuery or Snowflake and have a data solution that can scale with their business in a matter of hours. The emergence of cheap, flexible and scalable data storage solutions was largely a response to changing needs spurred by the massive explosion of data.
Currently, the world produces 2.5 quintillion bytes of data daily (there are 18 zeros in a quintillion). The explosion of data continues in the roaring ‘20s, both in terms of generation and storage — the amount of stored data is expected to continue to double at least every four years. However, one integral part of modern data infrastructure still lacks solutions suitable for the Big Data era and its challenges: Monitoring of data quality and data validation.
Investors help Procore build a decacorn valuation in public debut
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Watching construction tech software company Procore go public Thursday after pricing above its range makes the IPO slowdown look like the deceleration that wasn’t.
Investors quickly bid up the company’s value in trading, giving Procore a higher valuation than it might have anticipated, along with a boost of confidence for the IPO market in general.
Construction tech may not be as glamorous as space travel, but it’s a massive industry that’s fraught with inefficiencies.
Procore initially set an IPO range of $60 to $65 per share before pricing at $67 per share Wednesday night. Its debut was worth gross proceeds north of $600 million and a fully diluted valuation of $9.6 billion. As of early afternoon Thursday, shares were trading at a solid $85.25.
In light of Procore’s debut, TechCrunch is digging quickly into the company’s new valuation and its resulting revenue multiples.
Telemedicine startups are positioning themselves for a post-pandemic world
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It’s impossible to predict how healthcare institutions will operate post-pandemic, but with so many people now accustomed to telemedicine, startups that provide services around virtual care continue to be poised for success.
Telemedicine has faced an uphill battle to become more relevant in the U.S., with challenges such as meeting HIPAA compliance requirements and insurance companies unwilling to pay for virtual visits. But when COVID-19 began raging across the globe and people had to stay home, both the insurance and healthcare industries were forced to adapt.
Now that people see the benefits and conveniences of “dialing a doc” from the kitchen table, healthcare has changed forever.
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Google Cloud launches Vertex AI, a new managed machine learning platform
At Google I/O today Google Cloud announced Vertex AI, a new managed machine learning platform that is meant to make it easier for developers to deploy and maintain their AI models. It’s a bit of an odd announcement at I/O, which tends to focus on mobile and web developers and doesn’t traditionally feature a lot of Google Cloud news, but the fact that Google decided to announce Vertex today goes to show how important it thinks this new service is for a wide range of developers.
The launch of Vertex is the result of quite a bit of introspection by the Google Cloud team. “Machine learning in the enterprise is in crisis, in my view,” Craig Wiley, the director of product management for Google Cloud’s AI Platform, told me. “As someone who has worked in that space for a number of years, if you look at the Harvard Business Review or analyst reviews, or what have you — every single one of them comes out saying that the vast majority of companies are either investing or are interested in investing in machine learning and are not getting value from it. That has to change. It has to change.”
Image Credits: Google
Wiley, who was also the general manager of AWS’s SageMaker AI service from 2016 to 2018 before coming to Google in 2019, noted that Google and others who were able to make machine learning work for themselves saw how it can have a transformational impact, but he also noted that the way the big clouds started offering these services was by launching dozens of services, “many of which were dead ends,” according to him (including some of Google’s own). “Ultimately, our goal with Vertex is to reduce the time to ROI for these enterprises, to make sure that they can not just build a model but get real value from the models they’re building.”
Vertex then is meant to be a very flexible platform that allows developers and data scientist across skill levels to quickly train models. Google says it takes about 80% fewer lines of code to train a model versus some of its competitors, for example, and then help them manage the entire lifecycle of these models.
Image Credits: Google
The service is also integrated with Vizier, Google’s AI optimizer that can automatically tune hyperparameters in machine learning models. This greatly reduces the time it takes to tune a model and allows engineers to run more experiments and do so faster.
Vertex also offers a “Feature Store” that helps its users serve, share and reuse the machine learning features and Vertex Experiments to help them accelerate the deployment of their models into producing with faster model selection.
Deployment is backed by a continuous monitoring service and Vertex Pipelines, a rebrand of Google Cloud’s AI Platform Pipelines that helps teams manage the workflows involved in preparing and analyzing data for the models, train them, evaluate them and deploy them to production.
To give a wide variety of developers the right entry points, the service provides three interfaces: a drag-and-drop tool, notebooks for advanced users and — and this may be a bit of a surprise — BigQuery ML, Google’s tool for using standard SQL queries to create and execute machine learning models in its BigQuery data warehouse.
“We had two guiding lights while building Vertex AI: get data scientists and engineers out of the orchestration weeds, and create an industry-wide shift that would make everyone get serious about moving AI out of pilot purgatory and into full-scale production,” said Andrew Moore, vice president and general manager of Cloud AI and Industry Solutions at Google Cloud. “We are very proud of what we came up with in this platform, as it enables serious deployments for a new generation of AI that will empower data scientists and engineers to do fulfilling and creative work.”
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Sylvera grabs seed backing from Index to help close the accountability gap around carbon offsetting
U.K.-based startup Sylvera is using satellite, radar and lidar data-fuelled machine learning to bolster transparency around carbon offsetting projects in a bid to boost accountability and credibility — applying independent ratings to carbon offsetting projects.
The ratings are based on proprietary data sets it’s developed in conjunction with scientists from research organisations including UCLA, the NASA Jet Propulsion Laboratory and University College London.
It’s just grabbed $5.8 million in seed funding led by VC firm Index Ventures. All its existing institutional investors also participated — namely: Seedcamp, Speedinvest and Revent. It also has backing from leading angels, including the existing and former CEOs of NYSE, Thomson Reuters, Citibank and IHS Markit. (It confirms it has committed not to receive any investment from traditional carbon-intensive companies.) And it’s just snagged a $2 million research contract from Innovate UK.
The problem it’s targeting is that the carbon offsetting market suffers from a lack of transparency.
This fuels concerns that many offsetting projects aren’t living up to their claims of a net reduction in carbon emissions — and that “creative” carbon accountancy is rather being used to generate a lot of hot air: In the form of positive-sounding PR, which sums to meaningless greenwashing and more pollution as polluters get to keep on pumping out climate changing emissions.
Nonetheless, the carbon offset markets are poised for huge growth — of at least 15x by 2030 — as large corporates accelerate their net zero commitments. And Sylvera’s bet is that that will drive demand for reliable, independent data — to stand up the claimed impact.
How exactly is Sylvera benchmarking carbon offsets? Co-founder Sam Gill says its technology platform draws on multiple layers of satellite data to capture project performance data at scale and at a high frequency.
It applies machine learning to analyze and visualize the data, while also conducting what it bills as “deep analytical work to assess the underlying project quality”. Via that process it creates a standardised rating for a project, so that market participants are able to transact according to their preferences.
It makes its ratings and analysis data available to its customers via a web application and an API (for which it charges a subscription).
“We assess two critical areas of a project — its carbon performance, and its ‘quality’,” Gill tells TechCrunch. “We score a project against these criteria, and give them ratings — much like a Moody’s rating on a bond.”
Carbon performance is assessed by gathering “multi-layered data” from multiple sources to understand what is going on on the ground of these projects — such as via multiple satellite sources such as multispectral image, radar, and lidar data.
“We collate this data over time, ingest it into our proprietary machine learning algorithms, and analyse how the project has performed against its stated aims,” Gill explains.
Quality is assessed by considering the technical aspects of the project. This includes what Gill calls “additionality”; aka “does the project have a strong claim to delivering a better outcome than would have occurred but for the existence of the offset revenue?”.
There is a known problem with some carbon offsets claimed against forests where the landowner had no intention of logging, for example. So if there wasn’t going to be any deforestation the carbon credit is essentially bogus.
He also says it looks at factors like permanence (“how long will the project’s impacts last?”); co-benefits (“how well has the project incorporated the UN’s Sustainability Development Goals?); and risks (“how well is the project mitigating risks, in particular those from humans and those from natural causes?”).
Clearly it’s not an exact science — and Gill acknowledges risks, for example, are often interlinked.
“It is critical to assess these performance and quality in tandem,” he tells TechCrunch. “It’s not enough to simply say a project is achieving the carbon goals set out in its plan.
“If the additionality of a project is low (e.g. it was actually unlikely the project would have been deforested without the project) then the achievement of the carbon goals set out in the project does not generate the anticipated carbon goals, and the underlying offsets are therefore weaker than appreciated.”
Commenting on the seed funding in a statement, Carlos Gonzalez-Cadenas, partner at Index Ventures, said: “This is a phenomenally strong team with the vision to build the first carbon offset rating benchmark, providing comprehensive insights around the quality of offsets, enabling purchase decisions as well as post-purchase monitoring and reporting. Sylvera is putting in place the building blocks that will be required to address climate change.”
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DataRobot expands platform and announces Zepl acquisition
DataRobot, the Boston-based automated machine learning startup, had a bushel of announcements this morning as it expanded its platform to give technical and nontechnical users alike something new. It also announced it has acquired Zepl, giving it an advanced development environment where data scientists can bring their own code to DataRobot. The two companies did not share the acquisition price.
Nenshad Bardoliwalla, SVP of Product at DataRobot says that his company aspires to be the leader in this market and it believes the path to doing that is appealing to a broad spectrum of user requirements, from those who have little data science understanding to those who can do their own machine learning coding in Python and R.
“While people love automation, they also want it to be [flexible]. They don’t want just automation, but then you can’t do anything with it. They also want the ability to turn the knobs and pull the levers,” Bardoliwalla explained.
To resolve that problem, rather than building a coding environment from scratch, it chose to buy Zepl and incorporate its coding notebook into the platform in a new tool called Composable ML. “With Composable ML and with the Zepl acquisition, we are now providing a really first-class environment for people who want to code,” he said.
Zepl was founded in 2016 and raised $13 million along the way, according to Crunchbase data. The company didn’t want to reveal the number of employees or the purchase price, but the acquisition gives it advanced capabilities, especially a notebook environment to call its own to attract those more advanced users to the platform. The company plans to incorporate the Zepl functionality into the platform, while also leaving the standalone product in place.
Bardoliwalla said that they see the Zepl acquisition as an extension of the automated side of the house, where these tools can work in conjunction with one another with machines and humans working together to generate the best models. “This [generates an] organic mixture of the best of what a system can generate using DataRobot AutoML and the best of what human beings can do and kind of trying to compose those together into something really interesting […],” Bardoliwalla said.
The company is also introducing a no-code AI app builder that enables nontechnical users to create apps from the data set with drag and drop components. In addition, it’s adding a tool to monitor the accuracy of the model over time. Sometimes, after a model is in production for a time, the accuracy can begin to break down as the data on which the model is based is no longer valid. This tool monitors the model data for accuracy and warns the team when it’s starting to fall out of compliance.
Finally, the company is announcing a model bias monitoring tool to help root out model bias that could introduce racist, sexist or other assumptions into the model. To avoid this, the company has built a tool to identify when it sees this happening both in the model-building phase and in production. It warns the team of potential bias, while providing them with suggestions to tweak the model to remove it.
DataRobot is based in Boston and was founded in 2012. It has raised more than $750 million and has a valuation of over $2.8 billion, according to PitchBook.
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Longevity startup Gero AI has a mobile API for quantifying health changes
Sensor data from smartphones and wearables can meaningfully predict an individual’s ‘biological age’ and resilience to stress, according to Gero AI.
The ‘longevity’ startup — which condenses its mission to the pithy goal of “hacking complex diseases and aging with Gero AI” — has developed an AI model to predict morbidity risk using ‘digital biomarkers’ that are based on identifying patterns in step-counter sensor data which tracks mobile users’ physical activity.
A simple measure of ‘steps’ isn’t nuanced enough on its own to predict individual health, is the contention. Gero’s AI has been trained on large amounts of biological data to spots patterns that can be linked to morbidity risk. It also measures how quickly a personal recovers from a biological stress — another biomarker that’s been linked to lifespan; i.e. the faster the body recovers from stress, the better the individual’s overall health prognosis.
A research paper Gero has had published in the peer-reviewed biomedical journal Aging explains how it trained deep neural networks to predict morbidity risk from mobile device sensor data — and was able to demonstrate that its biological age acceleration model was comparable to models based on blood test results.
Another paper, due to be published in the journal Nature Communications later this month, will go into detail on its device-derived measurement of biological resilience.
The Singapore-based startup, which has research roots in Russia — founded back in 2015 by a Russian scientist with a background in theoretical physics — has raised a total of $5 million in seed funding to date (in two tranches).
Backers come from both the biotech and the AI fields, per co-founder Peter Fedichev. Its investors include Belarus-based AI-focused early stage fund, Bulba Ventures (Yury Melnichek). On the pharma side, it has backing from some (unnamed) private individuals with links to Russian drug development firm, Valenta. (The pharma company itself is not an investor).
Fedichev is a theoretical physicist by training who, after his PhD and some ten years in academia, moved into biotech to work on molecular modelling and machine learning for drug discovery — where he got interested in the problem of ageing and decided to start the company.
As well as conducting its own biological research into longevity (studying mice and nematodes), it’s focused on developing an AI model for predicting the biological age and resilience to stress of humans — via sensor data captured by mobile devices.
“Health of course is much more than one number,” emphasizes Fedichev. “We should not have illusions about that. But if you are going to condense human health to one number then, for a lot of people, the biological age is the best number. It tells you — essentially — how toxic is your lifestyle… The more biological age you have relative to your chronological age years — that’s called biological acceleration — the more are your chances to get chronic disease, to get seasonal infectious diseases or also develop complications from those seasonal diseases.”
Gero has recently launched a (paid, for now) API, called GeroSense, that’s aimed at health and fitness apps so they can tap up its AI modelling to offer their users an individual assessment of biological age and resilience (aka recovery rate from stress back to that individual’s baseline).
Early partners are other longevity-focused companies, AgelessRx and Humanity Inc. But the idea is to get the model widely embedded into fitness apps where it will be able to send a steady stream of longitudinal activity data back to Gero, to further feed its AI’s predictive capabilities and support the wider research mission — where it hopes to progress anti-ageing drug discovery, working in partnerships with pharmaceutical companies.
The carrot for the fitness providers to embed the API is to offer their users a fun and potentially valuable feature: A personalized health measurement so they can track positive (or negative) biological changes — helping them quantify the value of whatever fitness service they’re using.
“Every health and wellness provider — maybe even a gym — can put into their app for example… and this thing can rank all their classes in the gym, all their systems in the gym, for their value for different kinds of users,” explains Fedichev.
“We developed these capabilities because we need to understand how ageing works in humans, not in mice. Once we developed it we’re using it in our sophisticated genetic research in order to find genes — we are testing them in the laboratory — but, this technology, the measurement of ageing from continuous signals like wearable devices, is a good trick on its own. So that’s why we announced this GeroSense project,” he goes on.
“Ageing is this gradual decline of your functional abilities which is bad but you can go to the gym and potentially improve them. But the problem is you’re losing this resilience. Which means that when you’re [biologically] stressed you cannot get back to the norm as quickly as possible. So we report this resilience. So when people start losing this resilience it means that they’re not robust anymore and the same level of stress as in their 20s would get them [knocked off] the rails.
“We believe this loss of resilience is one of the key ageing phenotypes because it tells you that you’re vulnerable for future diseases even before those diseases set in.”
“In-house everything is ageing. We are totally committed to ageing: Measurement and intervention,” adds Fedichev. “We want to building something like an operating system for longevity and wellness.”
Gero is also generating some revenue from two pilots with “top range” insurance companies — which Fedichev says it’s essentially running as a proof of business model at this stage. He also mentions an early pilot with Pepsi Co.
He sketches a link between how it hopes to work with insurance companies in the area of health outcomes with how Elon Musk is offering insurance products to owners of its sensor-laden Teslas, based on what it knows about how they drive — because both are putting sensor data in the driving seat, if you’ll pardon the pun. (“Essentially we are trying to do to humans what Elon Musk is trying to do to cars,” is how he puts it.)
But the nearer term plan is to raise more funding — and potentially switch to offering the API for free to really scale up the data capture potential.
Zooming out for a little context, it’s been almost a decade since Google-backed Calico launched with the moonshot mission of ‘fixing death’. Since then a small but growing field of ‘longevity’ startups has sprung up, conducting research into extending (in the first instance) human lifespan. (Ending death is, clearly, the moonshot atop the moonshot.)
Death is still with us, of course, but the business of identifying possible drugs and therapeutics to stave off the grim reaper’s knock continues picking up pace — attracting a growing volume of investor dollars.
The trend is being fuelled by health and biological data becoming ever more plentiful and accessible, thanks to open research data initiatives and the proliferation of digital devices and services for tracking health, set alongside promising developments in the fast-evolving field of machine learning in areas like predictive healthcare and drug discovery.
Longevity has also seen a bit of an upsurge in interest in recent times as the coronavirus pandemic has concentrated minds on health and wellness, generally — and, well, mortality specifically.
Nonetheless, it remains a complex, multi-disciplinary business. Some of these biotech moonshots are focused on bioengineering and gene-editing — pushing for disease diagnosis and/or drug discovery.
Plenty are also — like Gero — trying to use AI and big data analysis to better understand and counteract biological ageing, bringing together experts in physics, maths and biological science to hunt for biomarkers to further research aimed at combating age-related disease and deterioration.
Another recent example is AI startup Deep Longevity, which came out of stealth last summer — as a spinout from AI drug discovery startup Insilico Medicine — touting an AI ‘longevity as a service’ system which it claims can predict an individual’s biological age “significantly more accurately than conventional methods” (and which it also hopes will help scientists to unpick which “biological culprits drive aging-related diseases”, as it put it).
Gero AI is taking a different tack toward the same overarching goal — by honing in on data generated by activity sensors embedded into the everyday mobile devices people carry with them (or wear) as a proxy signal for studying their biology.
The advantage being that it doesn’t require a person to undergo regular (invasive) blood tests to get an ongoing measure of their own health. Instead our personal device can generate proxy signals for biological study passively — at vast scale and low cost. So the promise of Gero’s ‘digital biomarkers’ is they could democratize access to individual health prediction.
And while billionaires like Peter Thiel can afford to shell out for bespoke medical monitoring and interventions to try to stay one step ahead of death, such high end services simply won’t scale to the rest of us.
If its digital biomarkers live up to Gero’s claims, its approach could, at the least, help steer millions towards healthier lifestyles, while also generating rich data for longevity R&D — and to support the development of drugs that could extend human lifespan (albeit what such life-extending pills might cost is a whole other matter).
The insurance industry is naturally interested — with the potential for such tools to be used to nudge individuals towards healthier lifestyles and thereby reduce payout costs.
For individuals who are motivated to improve their health themselves, Fedichev says the issue now is it’s extremely hard for people to know exactly which lifestyle changes or interventions are best suited to their particular biology.
For example fasting has been shown in some studies to help combat biological ageing. But he notes that the approach may not be effective for everyone. The same may be true of other activities that are accepted to be generally beneficial for health (like exercise or eating or avoiding certain foods).
Again those rules of thumb may have a lot of nuance, depending on an individual’s particular biology. And scientific research is, inevitably, limited by access to funding. (Research can thus tend to focus on certain groups to the exclusion of others — e.g. men rather than women; or the young rather than middle aged.)
This is why Fedichev believes there’s a lot of value in creating a measure than can address health-related knowledge gaps at essentially no individual cost.
Gero has used longitudinal data from the UK’s biobank, one of its research partners, to verify its model’s measurements of biological age and resilience. But of course it hopes to go further — as it ingests more data.
“Technically it’s not properly different what we are doing — it just happens that we can do it now because there are such efforts like UK biobank. Government money and also some industry sponsors money, maybe for the first time in the history of humanity, we have this situation where we have electronic medical records, genetics, wearable devices from hundreds of thousands of people, so it just became possible. It’s the convergence of several developments — technological but also what I would call ‘social technologies’ [like the UK biobank],” he tells TechCrunch.
“Imagine that for every diet, for every training routine, meditation… in order to make sure that we can actually optimize lifestyles — understand which things work, which do not [for each person] or maybe some experimental drugs which are already proved [to] extend lifespan in animals are working, maybe we can do something different.”
“When we will have 1M tracks [half a year’s worth of data on 1M individuals] we will combine that with genetics and solve ageing,” he adds, with entrepreneurial flourish. “The ambitious version of this plan is we’ll get this million tracks by the end of the year.”
Fitness and health apps are an obvious target partner for data-loving longevity researchers — but you can imagine it’ll be a mutual attraction. One side can bring the users, the other a halo of credibility comprised of deep tech and hard science.
“We expect that these [apps] will get lots of people and we will be able to analyze those people for them as a fun feature first, for their users. But in the background we will build the best model of human ageing,” Fedichev continues, predicting that scoring the effect of different fitness and wellness treatments will be “the next frontier” for wellness and health (Or, more pithily: “Wellness and health has to become digital and quantitive.”)
“What we are doing is we are bringing physicists into the analysis of human data. Since recently we have lots of biobanks, we have lots of signals — including from available devices which produce something like a few years’ long windows on the human ageing process. So it’s a dynamical system — like weather prediction or financial market predictions,” he also tells us.
“We cannot own the treatments because we cannot patent them but maybe we can own the personalization — the AI that personalized those treatments for you.”
From a startup perspective, one thing looks crystal clear: Personalization is here for the long haul.
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Personalized nutrition startup Zoe closes out Series B at $53M total raise
Personalized nutrition startup Zoe — named not for a person but after the Greek word for ‘life’ — has topped up its Series B round with $20M, bringing the total raised to $53M.
The latest close of the B round was led by Ahren Innovation Capital, which the startup notes counts two Nobel laureates as science partners. Also participating are two former American football players, Eli Manning and Ositadimma “Osi” Umenyiora; Boston, US-based seed fund Accomplice; healthcare-focused VC firm THVC and early stage European VC, Daphni.
The U.K.- and U.S.-based startup was founded back in 2017 but operated in stealth mode for three years, while it was conducting research into the microbiome — working with scientists from Massachusetts General Hospital, Stanford Medicine, Harvard T.H. Chan School of Public Health, and King’s College London.
One of the founders, professor Tim Spector of King’s College — who is also the author of a number of popular science books focused on food — became interested in the role of food (generally) and the microbiome (in particular) on overall health after spending decades researching twins to try to understand the role of genetics (nature) vs nurture (environmental and lifestyle factors) on human health.
Zoe used data from two large-scale microbiome studies to build its first algorithm which it began commercializing last September — launching its first product into the U.S. market: A home testing kit that enables program participants to learn how their body responds to different foods and get personalized nutrition advice.
The program costs around $360 (which Zoe takes in six instalments) and requires participants to (self) administer a number of tests so that it can analyze their biology, gleaning information about their metabolic and gut health by looking at changes in blood lipids, blood sugar levels and the types of bacteria in their gut.
Zoe uses big data and machine learning to come up with predictive insights on how people will respond to different foods so that it can offer individuals guided advice on what and how to eat, with the goal of improving gut health and reducing inflammatory responses caused by diet.
The combination of biological responses it analyzes sets it apart from other personalized nutrition startups with products focused on measuring one element (such as blood sugar) — is the claim.
But, to be clear, Zoe’s first product is not a regulated medical device — and its FAQ clearly states that it does not offer medical diagnosis or treatment for specific conditions. Instead it says only that it’s “a tool that is meant for general wellness purposes only”. So — for now — users have to take it on trust that the nutrition advice it dishes up is actually helpful for them.
The field of scientific research into the microbiome is undoubtedly early — Zoe’s co-founder states that very clearly when we talk — so there’s a strong component here, as is often the case when startups seek to use data and AI to generate valuable personalized predictions, whereby early adopters are helping to further Zoe’s research by contributing their data. Potentially ahead of the sought for individual efficacy, given so much is still unknown around how what we eat affects our health.
For those willing to take a punt (and pay up), they get an individual report detailing their biological responses to specific foods that compares them to thousands of others. The startup also provides them with individualized ‘Zoe’ scores for specific foods in order to support meal planning that’s touted as healthier for them.
“Reduce your dietary inflammation and improve gut health with a 4 week plan tailored to your unique biology and life,” runs the blurb on Zoe’s website. “Built around your food scores, our app will teach you how to make smart swaps, week by week.”
The marketing also claims no food is “off limits” — implying there’s a difference between Zoe’s custom food scores and (weight-loss focused) diets that perhaps require people to cut out a food group (or groups) entirely.
“Our aim is to empower you with the information and tools you need to make the best decisions for your body,” is Zoe’s smooth claim.
The underlying premise is that each person’s biology responds differently to different foods. Or, to put it another way, while we all most likely know at least one person who stays rake-thin and (seemingly) healthy regardless of what (or even how much) they eat, if we ate the same diet we’d probably expect much less pleasing results.
“What we’re able to start scientifically putting some evidence behind is something that people have talked about for a long time,” says co-founder George Hadjigeorgiou. “It’s early [for scientific research into the microbiome] but we have shown now to the world that even twins have different gut microbiomes, we can change our gut microbiomes through diet, lifestyle and how we live — and also that there are associations around particular [gut] bacteria and foods and a way to improve them which people can actually do through our product.”
Users of Zoe’s first product need to be willing (and able) to get pretty involved with their own biology — collecting stool samples, performing finger prick tests and wearing a blood glucose monitor to feed in data so it can analyze how their body responds to different foods and offer up personalized nutrition advice.
Another component of its study of biological responses to food has involved thousands of people eating “special scientific muffins”, which it makes to standardized recipes, so it can benchmark and compare nutritional responses to a particular blend of calories, carbohydrate, fat, and protein.
While eating muffins for science sounds pretty fine, the level of intervention required to make use of Zoe’s first at-home test kit product is unlikely to appeal to those with only a casual interest in improving their nutrition.
Hadjigeorgiou readily agrees the program, as it is now, is for those with a particular problem to solve that can be linked to diet/nutrition (whether obesity, high cholesterol or a disease like type 2 diabetes, and so on). But he says Zoe’s goal is to be able to open up access to personalized nutrition advice much more widely as it keeps gathering more data and insights.
“The idea is, as always, we start with a focused set of people with problems to solve who we believe will have a life-changing experience,” he tells TechCrunch. “At this point we are not trying to create a product for everyone — and we understand that that has limitations in terms of how much we scale in the beginning. Although even still within this focused group of people I can assure you there’s tonnes of people!
“But absolutely the whole idea is that after we get a first [set of users]… then with more data and with more experience we can simplify and start making this simpler and more accessible — both in terms of its simplicity and also it’s price. So more and more people. Because at the end of the day everyone has this right to be able to optimize and understand and be in control — and we want to make that available to everyone.
“Regardless of background and regardless of socio-economic status. And, in fact, many of the people who have the biggest problems around health etc are the ones who have maybe less means and ability to do that.”
Zoe isn’t disclosing how many early users it’s onboarded so far but Hadjigeorgiou says demand is high (it’s currently operating a wait-list for new sign ups).
He also touts promising early results from interim trial with its first users — saying participants experienced more energy (90%), felt less hunger (80%) and lost an average of 11 pounds after three months of following their AI-aided, personalized nutrition plan. Albeit, without data on how many people are involved in the trials it’s not possible to quantify the value of those metrics.
The extra Series B funding will be used to accelerate the rollout of availability of the program, with a U.K. launch planned for this year — and other geographies on the cards for 2022. Spending will also go on continued recruitment in engineering and science, it says.
Zoe already grabbed some eyeballs last year, as the coronavirus pandemic hit the West, when it launched a COVID-19 symptom self-reporting app. It has used that data to help scientists and policy makers understand how the virus affects people.
The Zoe COVID-19 app has had some 5M users over the last year, per Hadjigeorgiou — who points to that (not-for-profit) effort as an example of the kind of transformative intervention the company hopes to drive in the nutrition space down the line.
“Overnight we got millions and millions of people contributing to help uncover new insights around science around COVID-19,” he says, highlighting that it’s been able to publish a number of research papers based on data contributed by app users. “For example the lack of smell and taste… was something that we first [were able to prove] scientifically, and then it became — because of that — an official symptom in the list of the government in the U.K.
“So that was a great example how through the participation of people — in a very, very fast way, which we couldn’t predict when we launched it — we managed to have a big impact.”
Returning to diet, aren’t there some pretty simple ‘rules of thumb’ that anyone can apply to eat more healthily — i.e. without the need to shell out for a bespoke nutrition plan? Basic stuff like eat your greens, avoid processed foods and cut down (or out) sugar?
“There are definitely rules of thumb,” Hadjigeorgiou agrees. “We’ll be crazy to say they’re not. I think it all comes back to the point that although there are rules of thumb and over time — and also through our research, for example — they can become better, the fact of the matter is that most people are becoming less and less healthy. And the fact of the matter is that life is messy and people do not eat even according to these rules of thumb so I think part of the challenge is… [to] educate and empower people for their messy lives and their lifestyle to actually make better choices and apply them in a way that’s sustainable and motivating so they can be healthier.
“And that’s what we’re finding with our customers. We are helping them to make these choices in an empowering way — they don’t need to count calories, they don’t need to restrict themselves through a Keto [diet] regime or something like that. We basically empower them to understand this is the impact food has on your body — real time, how your blood sugar levels change, how your bacteria change, how your blood fat levels changes. And through that empowerment through insight then we say hey, now we’ll give you this course, it’s very simple, it’s like a game — and we’ll given you all these tools to combine different foods, make foods work for you. No food is off limits — but try to eat most days a 75 score [based on the food points Zoe’s app assigns].
“In that very empowering way we see people get very excited, they see a fun game that is also impacting their gut and metabolism and they start feeling these amazing effects — in terms of less hunger, more energy, losing weight and over time as well evolving their health. That’s why they say it’s life changing as well.”
Gamifying research for the goal of a greater good? To the average person that surely sounds more appetitizing than ‘eat your greens’.
Though, as Hadjigeorgiou concedes, research in the field of microbiome — where Zoe’s commercial interests and research USP lie — is “early”. Which means that gathering more data to do more research will remain a key component of the business for the foreseeable future. And with so much still to be understood about the complex interactions between food, exercise and other lifestyle factors and human health, the mission is indeed massive.
In the meanwhile, Zoe will be taking it one suggestive nudge at a time.
“Sugar is bad, kale’s great but the whole kind of magic happens in the middle,” Hadjigeorgiou goes on. “Is oatmeal good for you? Is rice good for you? Is wholewheat pasta good for you? How do you combine wholewheat pasta and butter? How much do you have? This is where basically most of our life happens.
“Because people don’t eat ice-cream the whole day and people don’t eat kale the whole day. They eat all these other foods in the middle and that’s where the magic is — knowing how much to have, how to combine them to make it better, how to combine it with exercise to make it better? How to eat a food that doesn’t dip your sugar levels three hours after you eat it which causes hunger for you. Theses are all the things we’re able to predict and present in a simple and compelling way through a score system to people — and in turn help them [understand their] metabolic response to food.”
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Persona lands $50M for identity verification after seeing 10x YoY revenue growth
The identity verification space has been heating up for a while and the COVID-19 pandemic has only accelerated demand with more people transacting online.
Persona, a startup focused on creating a personalized identity verification experience “for any use case,” aims to differentiate itself in an increasingly crowded space. And investors are banking on the San Francisco-based company’s ability to help businesses customize the identity verification process — and beyond — via its no-code platform in the form of a $50 million Series B funding round.
Index Ventures led the financing, which also included participation from existing backer Coatue Management. In late January 2020, Persona raised $17.5 million in a Series A round. The company declined to reveal at which valuation this latest round was raised.
Businesses and organizations can access Persona’s platform by way of an API, which lets them use a variety of documents, from government-issued IDs through to biometrics, to verify that customers are who they say they are. The company wants to make it easier for organizations to implement more watertight methods based on third-party documentation, real-time evaluation such as live selfie checks and AI to verify users.
Persona’s platform also collects passive signals such as a user’s device, location, and behavioral signals to provide a more holistic view of a user’s risk profile. It offers a low code and no code option depending on the needs of the customer.
The company’s momentum is reflected in its growth numbers. The startup’s revenue has surged by “more than 10 times” while its customer base has climbed by five times over the past year, according to co-founder and CEO Rick Song, who did not provide hard revenue numbers. Meanwhile, Persona’s headcount has more than tripled to just over 50 people.
“When we look back at the space five to 10 years ago, AI was the next differentiation and every identity verification company is doing AI and machine learning,” Song told TechCrunch. “We believe the next big differentiator is more about tailoring and personalizing the experience for individuals.”
As such, Song believes that growth can be directly tied to Persona’s ability to help companies with “unique” use cases with a SaaS platform that requires little to no code and not as much heavy lifting from their engineering teams. Its end goal, ultimately, is to help businesses deter fraud, stay compliant and build trust and safety while making it easier for them to customize the verification process to their needs. Customers span a variety of industries, and include Square, Robinhood, Sonder, Brex, Udemy, Gusto, BlockFi and AngelList, among others.
“The strategy your business needs for identity verification and management is going to be completely different if you’re a travel company verifying guests versus a delivery service onboarding new couriers versus a crypto company granting access to user funds,” Song added. “Even businesses within the same industry should tailor the identity verification experience to each customer if they want to stand out.”
Image Credits: Persona
For Song, another thing that helps Persona stand out is its ability to help customers beyond the sign-on and verification process.
“We’ve built an identity infrastructure because we don’t just help businesses at a single point in time, but rather throughout the entire lifecycle of a relationship,” he told TechCrunch.
In fact, much of the company’s growth last year came in the form of existing customers finding new use cases within the platform in addition to new customers signing on, Song said.
“We’ve been watching existing customers discover more ways to use Persona. For example, we were working with some of our customer base on a single use case and now we might be working with them on 10 different problems — anywhere from account opening to a bad actor investigation to account recovery and anything in between,” he added. “So that has probably been the biggest driver of our growth.”
Index Ventures Partner Mark Goldberg, who is taking a seat on Persona’s board as part of the financing, said he was impressed by the number of companies in Index’s own portfolio that raved about Persona.
“We’ve had our antennas up for a long time in this space,” he told TechCrunch. “We started to see really rapid adoption of Persona within the Index portfolio and there was the sense of a very powerful and very user friendly tool, which hadn’t really existed in the category before.”
Its personalization capabilities and building block-based approach too, Goldberg said, makes it appealing to a broader pool of users.
“The reality is there’s so many ways to verify a user is who they say they are or not on the internet, and if you give people the flexibility to design the right path to get to a yes or no, you can just get to a much better outcome,” he said. “That was one of the things we heard — that the use cases were not like off the rack, and I think that has really resonated in a time where people want and expect the ability to customize.”
Persona plans to use its new capital to grow its team another twofold by year’s end to support its growth and continue scaling the business.
In recent months, other companies in the space that have raised big rounds include Socure and Sift.
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