artificial intelligence
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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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Shift Technology raises $220M at a $1B+ valuation to fight insurance fraud with AI
While incumbent insurance providers continue to get disrupted by startups like Lemonade, Alan, Clearcover, Pie and many others applying tech to rethink how to build a business around helping people and companies mitigate against risks with some financial security, one issue that has not disappeared is fraud. Today, a startup out of France is announcing some funding for AI technology that it has built for all insurance providers, old and new, to help them detect and prevent it.
Shift Technology, which provides a set of AI-based SaaS tools to insurance companies to scan and automatically flag fraud scenarios across a range of use cases — they include claims fraud, claims automation, underwriting, subrogation detection and financial crime detection — has raised $220 million, money that it will be using both to expand in the property and casualty insurance market, the area where it is already strong, as well as to expand into health, and to double down on growing its business in the U.S. It also provides fraud detection for the travel insurance sector.
This Series D is being led by Advent International, via Advent Tech, with participation from Avenir and others. Accel, Bessemer Venture Partners, General Catalyst and Iris Capital — who were all part of Shift’s Series C led by Bessemer in 2019 — also participated. With this round, Paris-and-Boston-based Shift Technology has now raised some $320 million and has confirmed that it is now valued at over $1 billion.
The company currently has around 100 customers across 25 different countries — with the list including Generali France and Mitsui Sumitomo, to give you an idea of where it’s pitching its business — and says that it has already analyzed nearly two billion claims, data that’s feeding its machine learning algorithms to improve how they work.
The challenge (or I suppose, opportunity) that Shift is tackling, however, is much bigger. The Coalition Against Insurance Fraud, a nonprofit in the U.S., estimates that at least $80 billion of fraudulent claims are made annually in the U.S. alone, but the figure is likely significantly higher. One problem has, ironically, been the move to more virtualized processes, which open the door to malicious actors exploiting loopholes in claims filing and fudging information. Another is the fact that insurance has grown as a market, but so too has the amount of people who are in financial straights, leading to more desperate and illegal acts to gain an edge.
Shift is also not alone in tackling this issue: the market for insurance fraud detection technology globally was estimated to be worth $2.5 billion in 2019 and projected to be worth as much as $8 billion by 2024.
In addition to others in claims management tech such as Brightcore and Guidewire, many of the wave of insurtech startups are building in their own in-house AI-based fraud protection, and it’s very likely that we’ll see a rise of other fraud protection services, built out of adjacent areas like fintech to guard against financial crime, making their way to insurance. As many a fintech entrepreneur has said to me in the past, the mechanics of how the two verticals work and the compliance issues both face are very closely aligned.
“The entire Shift team has worked tirelessly to build this company and provide insurers with the technology solutions they need to empower employees to best be there for their policyholders. We are thrilled to partner with Advent International, given their considerable sector expertise and global reach and are taking another giant step forward with this latest investment,” stated Jeremy Jawish, CEO and co-founder, Shift Technology, in a statement. “We have only just scratched the surface of what is possible when AI-based decision automation and optimization is applied to the critical processes that drive the insurance policy lifecycle.”
For its backers, one key point with Shift is that it’s helping older providers bring on more tools and services that can help them improve their margins as well as better compete against the technology built by newer players.
“Since its founding in 2014, Shift has made a name for itself in the complex world of insurance,” said Thomas Weisman, an Advent director, in a statement. “Shift’s advanced suite of SaaS products is helping insurers to reshape manual and often time-consuming claims processes in a safer and more automated way. We are proud to be part of this exciting company’s next wave of growth.”
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Cymulate nabs $45M to test and improve cybersecurity defenses via attack simulations
With cybercrime on course to be a $6 trillion problem this year, organizations are throwing ever more resources at the issue to avoid being a target. Now, a startup that’s built a platform to help them stress-test the investments that they have made into their security IT is announcing some funding on the back of strong demand from the market for its tools.
Cymulate, which lets organizations and their partners run machine-based attack simulations on their networks to determine vulnerabilities and then automatically receive guidance around how to fix what is not working well enough, has picked up $45 million, funding that the startup — co-headquartered in Israel and New York — will be using to continue investing in its platform and to ramp up its operations after doubling its revenues last year on the back of a customer list that now numbers 300 large enterprises and mid-market companies, including the Euronext stock exchange network as well as service providers such as NTT and Telit.
London-based One Peak Partners is leading this Series C, with previous investors Susquehanna Growth Equity (SGE), Vertex Ventures Israel, Vertex Growth and Dell Technologies Capital also participating.
According to Eyal Wachsman, the CEO and co-founder, Cymulate’s technology has been built not just to improve an organization’s security, but an automated, machine learning-based system to better understand how to get the most out of the security investments that have already been made.
“Our vision is to be the largest cybersecurity ‘consulting firm’ without consultants,” he joked.
The valuation is not being disclosed, but as some measure of what is going on, David Klein, managing partner at One Peak, said in an interview that he expects Cymulate to hit a $1 billion valuation within two years at the rate it’s growing and bringing in revenue right now. The startup has now raised $71 million, so it’s likely the valuation is in the mid-hundreds of millions. (We’ll continue trying to get a better number to have a more specific data point here.)
Cymulate — pronounced “sigh-mulate”, like the “cy” in “cyber” and a pun of “simulate”) is cloud-based but works across both cloud and on-premises environments and the idea is that it complements work done by (human) security teams both inside and outside of an organization, as well as the security IT investments (in terms of software or hardware) that they have already made.
“We do not replace — we bring back the power of the expert by validating security controls and checking whether everything is working correctly to optimize a company’s security posture,” Wachsman said. “Most of the time, we find our customers are using only 20% of the capabilities that they have. The main idea is that we have become a standard.”
The company’s tools are based in part on the MITRE ATT&CK framework, a knowledge base of threats, tactics and techniques used by a number of other cybersecurity services, including a number of others building continuous validation services that compete with Cymulate. These include the likes of FireEye, Palo Alto Networks, Randori, Khosla-backed AttackIQ and many more.
Although Cymulate is optimized to help customers better use the security tools they already have, it is not meant to replace other security apps, Wachsman noted, even if the by-product might become buying fewer of those apps in the future.
“I believe my message every day when talking with security experts is to stop buying more security products,” he said in an interview. “They won’t help defend you from the next attack. You can use what you’ve already purchased as long as you configure it well.”
In his words, Cymulate acts as a “black box” on the network, where it integrates with security and other software (it can also work without integrating, but integrations allow for a deeper analysis). After running its simulations, it produces a map of the network and its threat profile, an executive summary of the situation that can be presented to management and a more technical rundown, which includes recommendations for mitigations and remediations.
Alongside validating and optimising existing security apps and identifying vulnerabilities in the network, Cymulate also has built special tools to fit different kinds of use cases that are particularly relevant to how businesses operate today. They include evaluating remote working deployments, the state of a network following an M&A process, the security landscape of an organization that links up with third parties in supply chain arrangements, how well an organization’s security architecture is meeting (or potentially conflicting) with privacy and other kinds of regulatory compliance requirements, and it has built a “purple team” deployment, where in cases where security teams do not have the resources for running separate “red teams” to stress test something, blue teams at the organization can use Cymulate to build a machine learning-based “team” to do this.
The fact that Cymulate has built the infrastructure to run all of these processes speaks to a lot of potential of what more it could build, especially as our threat landscape and how we do business both continue to evolve. Even as it is, though, the opportunity today is a massive one, with Gartner estimating that some $170 billion will be spent on information security by enterprises in 2022. That’s one reason why investors are here, too.
“The increasing pace of global cyber security attacks has resulted in a crisis of trust in the security posture of enterprises and a realization that security testing needs to be continuous as opposed to periodic, particularly in the context of an ever-changing IT infrastructure and rapidly evolving threats. Companies understand that implementing security solutions is not enough to guarantee protection against cyber threats and need to regain control,” said Klein, in a statement. “We expect Cymulate to grow very fast,” he told me more directly.
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Cognixion’s brain-monitoring headset enables fluid communication for people with severe disabilities
Of the many frustrations of having a severe motor impairment, the difficulty of communicating must surely be among the worst. The tech world has not offered much succor to those affected by things like locked-in syndrome, ALS and severe strokes, but startup Cognixion aims to with a novel form of brain monitoring that, combined with a modern interface, could make speaking and interaction far simpler and faster.
The company’s One headset tracks brain activity closely in such a way that the wearer can direct a cursor — reflected on a visor like a heads-up display — in multiple directions, or select from various menus and options. No physical movement is needed, and with the help of modern voice interfaces like Alexa, the user can not only communicate efficiently but freely access all kinds of information and content most people take for granted.
But it’s not a miracle machine, and it isn’t a silver bullet. Here’s how it got started.
Overhauling decades-old brain tech
Everyone with a motor impairment has different needs and capabilities, and there are a variety of assistive technologies that cater to many of these needs. But many of these techs and interfaces are years or decades old — medical equipment that hasn’t been updated for an era of smartphones and high-speed mobile connections.
Some of the most dated interfaces, unfortunately, are those used by people with the most serious limitations: those whose movements are limited to their heads, faces, eyes — or even a single eyelid, like Jean-Dominique Bauby, the famous author of “The Diving Bell and the Butterfly.”
One of the tools in the toolbox is the electroencephalogram, or EEG, which involves detecting activity in the brain via patches on the scalp that record electrical signals. But while they’re useful in medicine and research in many ways, EEGs are noisy and imprecise — more for finding which areas of the brain are active than, say, which sub-region of the sensory cortex or the like. And of course you have to wear a shower cap wired with electrodes (often greasy with conductive gel) — it’s not the kind of thing anyone wants to do for more than an hour, let alone all day every day.
Yet even among those with the most profound physical disabilities, cognition is often unimpaired — as indeed EEG studies have helped demonstrate. It made Andreas Forsland, co-founder and CEO of Cognixion, curious about further possibilities for the venerable technology: “Could a brain-computer interface using EEG be a viable communication system?”
He first used EEG for assistive purposes in a research study some five years ago. They were looking into alternative methods of letting a person control an on-screen cursor, among them an accelerometer for detecting head movements, and tried integrating EEG readings as another signal. But it was far from a breakthrough.
A modern lab with an EEG cap wired to a receiver and laptop — this is an example of how EEG is commonly used. Image Credits: BSIP/Universal Images Group via Getty Images
He ran down the difficulties: “With a read-only system, the way EEG is used today is no good; other headsets have slow sample rates and they’re not accurate enough for a real-time interface. The best BCIs are in a lab, connected to wet electrodes — it’s messy, it’s really a non-starter. So how do we replicate that with dry, passive electrodes? We’re trying to solve some very hard engineering problems here.”
The limitations, Forsland and his colleagues found, were not so much with the EEG itself as with the way it was carried out. This type of brain monitoring is meant for diagnosis and study, not real-time feedback. It would be like taking a tractor to a drag race. Not only do EEGs often work with a slow, thorough check of multiple regions of the brain that may last several seconds, but the signal it produces is analyzed by dated statistical methods. So Cognixion started by questioning both practices.
Improving the speed of the scan is more complicated than overclocking the sensors or something. Activity in the brain must be inferred by collecting a certain amount of data. But that data is collected passively, so Forsland tried bringing an active element into it: a rhythmic electric stimulation that is in a way reflected by the brain region, but changed slightly depending on its state — almost like echolocation.
The Cognixion One headset with its dry EEG terminals visible. Image Credits: Cognixion
They detect these signals with a custom set of six EEG channels in the visual cortex area (up and around the back of your head), and use a machine learning model to interpret the incoming data. Running a convolutional neural network locally on an iPhone — something that wasn’t really possible a couple years ago — the system can not only tease out a signal in short order but make accurate predictions, making for faster and smoother interactions.
The result is sub-second latency with 95-100% accuracy in a wireless headset powered by a mobile phone. “The speed, accuracy and reliability are getting to commercial levels — we can match the best in class of the current paradigm of EEGs,” said Forsland.
Dr. William Goldie, a clinical neurologist who has used and studied EEGs and other brain monitoring techniques for decades (and who has been voluntarily helping Cognixion develop and test the headset), offered a positive evaluation of the technology.
“There’s absolutely evidence that brainwave activity responds to thinking patterns in predictable ways,” he noted. This type of stimulation and response was studied years ago. “It was fascinating, but back then it was sort of in the mystery magic world. Now it’s resurfacing with these special techniques and the computerization we have these days. To me it’s an area that’s opening up in a manner that I think clinically could be dramatically effective.”
BCI, meet UI
The first thing Forsland told me was “We’re a UI company.” And indeed even such a step forward in neural interfaces as he later described means little if it can’t be applied to the problem at hand: helping people with severe motor impairment to express themselves quickly and easily.
Sad to say, it’s not hard to imagine improving on the “competition,” things like puff-and-blow tubes and switches that let users laboriously move a cursor right, right a little more, up, up a little more, then click: a letter! Gaze detection is of course a big improvement over this, but it’s not always an option (eyes don’t always work as well as one would like) and the best eye-tracking solutions (like a Tobii Dynavox tablet) aren’t portable.
Why shouldn’t these interfaces be as modern and fluid as any other? The team set about making a UI with this and the capabilities of their next-generation EEG in mind.
Image Credits: Cognixion
Their solution takes bits from the old paradigm and combines them with modern virtual assistants and a radial design that prioritizes quick responses and common needs. It all runs in an app on an iPhone, the display of which is reflected in a visor, acting as a HUD and outward-facing display.
In easy reach of, not to say a single thought but at least a moment’s concentration or a tilt of the head, are everyday questions and responses — yes, no, thank you, etc. Then there are slots to put prepared speech into — names, menu orders and so on. And then there’s a keyboard with word- and sentence-level prediction that allows common words to be popped in without spelling them out.
“We’ve tested the system with people who rely on switches, who might take 30 minutes to make 2 selections. We put the headset on a person with cerebral palsy, and she typed our her name and hit play in 2 minutes,” Forsland said. “It was ridiculous, everyone was crying.”
Goldie noted that there’s something of a learning curve. “When I put it on, I found that it would recognize patterns and follow through on them, but it also sort of taught patterns to me. You’re training the system, and it’s training you — it’s a feedback loop.”
“I can be the loudest person in the room”
One person who has found it extremely useful is Chris Benedict, a DJ, public speaker and disability advocate who himself has Dyskinetic Cerebral Palsy. It limits his movements and ability to speak, but doesn’t stop him from spinning (digital) records at various engagements, however, or from explaining his experience with Cognixion’s One headset over email. (And you can see him demonstrating it in person in the video above.)
Image Credits: Cognixion
“Even though it’s not a tool that I’d need all the time it’s definitely helpful in aiding my communication,” he told me. “Especially when I need to respond quickly or am somewhere that is noisy, which happens often when you are a DJ. If I wear it with a Bluetooth speaker I can be the loudest person in the room.” (He always has a speaker on hand, since “you never know when you might need some music.”)
The benefits offered by the headset give some idea of what is lacking from existing assistive technology (and what many people take for granted).
“I can use it to communicate, but at the same time I can make eye contact with the person I’m talking to, because of the visor. I don’t have to stare at a screen between me and someone else. This really helps me connect with people,” Benedict explained.
“Because it’s a headset I don’t have to worry about getting in and out of places, there is no extra bulk added to my chair that I have to worry about getting damaged in a doorway. The headset is balanced too, so it doesn’t make my head lean back or forward or weigh my neck down,” he continued. “When I set it up to use the first time it had me calibrate, and it measured my personal range of motion so the keyboard and choices fit on the screen specifically for me. It can also be recalibrated at any time, which is important because not every day is my range of motion the same.”
Alexa, which has been extremely helpful to people with a variety of disabilities due to its low cost and wide range of compatible devices, is also part of the Cognixion interface, something Benedict appreciates, having himself adopted the system for smart home and other purposes. “With other systems this isn’t something you can do, or if it is an option, it’s really complicated,” he said.
Next steps
As Benedict demonstrates, there are people for whom a device like Cognixion’s makes a lot of sense, and the hope is it will be embraced as part of the necessarily diverse ecosystem of assistive technology.
Forsland said that the company is working closely with the community, from users to clinical advisors like Goldie and other specialists, like speech therapists, to make the One headset as good as it can be. But the hurdle, as with so many devices in this class, is how to actually put it on people’s heads — financially and logistically speaking.
Cognixion is applying for FDA clearance to get the cost of the headset — which, being powered by a phone, is not as high as it would be with an integrated screen and processor — covered by insurance. But in the meantime the company is working with clinical and corporate labs that are doing neurological and psychological research. Places where you might find an ordinary, cumbersome EEG setup, in other words.
The company has raised funding and is looking for more (hardware development and medical pursuits don’t come cheap), and has also collected a number of grants.
The One headset may still be some years away from wider use (the FDA is never in a hurry), but that allows the company time to refine the device and include new advances. Unlike many other assistive devices, for example a switch or joystick, this one is largely software-limited, meaning better algorithms and UI work will significantly improve it. While many wait for companies like Neuralink to create a brain-computer interface for the modern era, Cognixion has already done so for a group of people who have much more to gain from it.
You can learn more about the Cognixion One headset and sign up to receive the latest at its site here.
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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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Analytics as a service: Why more enterprises should consider outsourcing
With an increasing number of enterprise systems, growing teams, a rising proliferation of the web and multiple digital initiatives, companies of all sizes are creating loads of data every day. This data contains excellent business insights and immense opportunities, but it has become impossible for companies to derive actionable insights from this data consistently due to its sheer volume.
According to Verified Market Research, the analytics-as-a-service (AaaS) market is expected to grow to $101.29 billion by 2026. Organizations that have not started on their analytics journey or are spending scarce data engineer resources to resolve issues with analytics implementations are not identifying actionable data insights. Through AaaS, managed services providers (MSPs) can help organizations get started on their analytics journey immediately without extravagant capital investment.
MSPs can take ownership of the company’s immediate data analytics needs, resolve ongoing challenges and integrate new data sources to manage dashboard visualizations, reporting and predictive modeling — enabling companies to make data-driven decisions every day.
AaaS could come bundled with multiple business-intelligence-related services. Primarily, the service includes (1) services for data warehouses; (2) services for visualizations and reports; and (3) services for predictive analytics, artificial intelligence (AI) and machine learning (ML). When a company partners with an MSP for analytics as a service, organizations are able to tap into business intelligence easily, instantly and at a lower cost of ownership than doing it in-house. This empowers the enterprise to focus on delivering better customer experiences, be unencumbered with decision-making and build data-driven strategies.
Organizations that have not started on their analytics journey or are spending scarce data engineer resources to resolve issues with analytics implementations are not identifying actionable data insights.
In today’s world, where customers value experiences over transactions, AaaS helps businesses dig deeper into their psyche and tap insights to build long-term winning strategies. It also enables enterprises to forecast and predict business trends by looking at their data and allows employees at every level to make informed decisions.
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Heirlume raises $1.38M to remove the barriers of trademark registration for small businesses
Platforms like Shopify, Stripe and WordPress have done a lot to make essential business-building tools — like running storefronts, accepting payments and building websites — accessible to businesses with even the most modest budgets. But some very key aspects of setting up a company remain expensive, time-consuming affairs that can be cost-prohibitive for small businesses — but that, if ignored, can result in the failure of a business before it even really gets started.
Trademark registration is one such concern, and Toronto-based startup Heirlume just raised $1.7 million CAD (~$1.38 million) to address the problem with a machine-powered trademark registration platform that turns the process into a self-serve affair that won’t break the budget. Its AI-based trademark search will flag if terms might run afoul of existing trademarks in the U.S. and Canada, even when official government trademark search tools, and even top-tier legal firms, might not.
Heirlume’s core focus is on leveling the playing field for small business owners, who have typically been significantly out-matched when it comes to any trademark conflicts.
“I’m a senior-level IP lawyer focused in trademarks, and had practiced in a traditional model, boutique firm of my own for over a decade serving big clients, and small clients,” explained Heirlume co-founder Julie MacDonell in an interview. “So providing big multinationals with a lot of brand strategy, and in-house legal, and then mainly serving small business clients when they were dealing with a cease-and-desist, or an infringement issue. It’s really those clients that have my heart: It’s incredibly difficult to have a small business owner literally crying tears on the phone with you, because they just lost their brand or their business overnight. And there was nothing I could do to help because the law just simply wasn’t on their side, because they had neglected to register their trademarks to own them.”
In part, there’s a lack of awareness around what it takes to actually register and own a trademark, MacDonell says. Many entrepreneurs just starting out seek out a domain name as a first step, for instance, and some will fork over significant sums to register these domains. What they don’t realize, however, is that this is essentially a rental, and if you don’t have the trademark to protect that domain, the actual trademark owner can potentially take it away down the road. But even if business owners do realize that a trademark should be their first stop, the barriers to actually securing one are steep.
“There was an an enormous, insurmountable barrier, when it came to brand protection for those business owners,” she said. “And it just isn’t fair. Every other business service, generally a small business owner can access. Incorporating a company or even insurance, for example, owning and buying insurance for your business is somewhat affordable and accessible. But brand ownership is not.”
Heirlume brings the cost of trademark registration down from many thousands of dollars to just under $600 for the first, and only $200 for each additional after that. The startup is also offering a very small business-friendly “buy now, pay later” option supported by Clearbanc, which means that even businesses starting on a shoestring can take the step of protecting their brand at the outset.
In its early days, Heirlume is also offering its core trademark search feature for free. That provides a trademark search engine that works across both U.S. and Canadian government databases, which can not only tell you if your desired trademark is available or already held, but also reveal whether it’s likely to be able to be successfully obtained, given other conflicts that might arise that are totally ignored by native trademark database search portals.
Image Credits: Heirlume
Heirlume uses machine learning to identify these potential conflicts, which not only helps users searching for their trademarks, but also greatly decreases the workload behind the scenes, helping them lower costs and pass on the benefits of those improved margins to its clients. That’s how it can achieve better results than even hand-tailored applications from traditional firms, while doing so at scale and at reduced costs.
Another advantage of using machine-powered data processing and filing is that on the government trademark office side, the systems are looking for highly organized, curated data sets that are difficult for even trained people to get consistently right. Human error in just data entry can cause massive backlogs, MacDonell notes, even resulting in entire applications having to be tossed and started over from scratch.
“There are all sorts of data sets for those [trademark requirement] parameters,” she said. “Essentially, we synthesize all of that, and the goal through machine learning is to make sure that applications are utterly compliant with government rules. We actually have a senior-level trademark examiner that came to work for us, very excited that we were solving the problems causing backlogs within the government. She said that if Heirlume can get to a point where the applications submitted are perfect, there will be no backlog with the government.”
Improving efficiency within the trademark registration bodies means one less point of friction for small business owners when they set out to establish their company, which means more economic activity and upside overall. MacDonell ultimately hopes that Heirlume can help reduce friction to the point where trademark ownership is at the forefront of the business process, even before domain registration. Heirlume has a partnership with Google Domains to that end, which will eventually see indication of whether a domain name is likely to be trademarkable included in Google Domain search results.
This initial seed funding includes participation from Backbone Angels, as well as the Future Capital collective, Angels of Many and MaRS IAF, along with angel investors including Daniel Debow, Sid Lee’s Bertrand Cesvet and more. MacDonell notes that just as their goal was to bring more access and equity to small business owners when it comes to trademark protection, the startup was also very intentional in building its team and its cap table. MacDonell, along with co-founders CTO Sarah Ruest and Dave McDonell, aim to build the largest tech company with a majority female-identifying technology team. Its investor make-up includes 65% female-identifying or underrepresented investors, and MacDonnell says that was a very intentional choice that extended the time of the raise, and even led to turning down interest from some leading Silicon Valley firms.
“We want underrepresented founders to be to be funded, and the best way to ensure that change is to empower underrepresented investors,” she said. “I think that we all have a responsibility to actually do something. We’re all using hashtags right now, and hashtags are not enough […] Our CTO is female, and she’s often been the only female person in the room. We’ve committed to ensuring that women in tech are no longer the only woman in the room.”
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The health data transparency movement is birthing a new generation of startups
In the early 2000s, Jeff Bezos gave a seminal TED Talk titled “The Electricity Metaphor for the Web’s Future.” In it, he argued that the internet will enable innovation on the same scale that electricity did.
We are at a similar inflection point in healthcare, with the recent movement toward data transparency birthing a new generation of innovation and startups.
Those who follow the space closely may have noticed that there are twin struggles taking place: a push for more transparency on provider and payer data, including anonymous patient data, and another for strict privacy protection for personal patient data. What’s the main difference?
This sector is still somewhat nascent — we are in the first wave of innovation, with much more to come.
Anonymized data is much more freely available, while personal data is being locked even tighter (as it should be) due to regulations like GDPR, CCPA and their equivalents around the world.
The former trend is enabling a host of new vendors and services that will ultimately make healthcare better and more transparent for all of us.
These new companies could not have existed five years ago. The Affordable Care Act was the first step toward making anonymized data more available. It required healthcare institutions (such as hospitals and healthcare systems) to publish data on costs and outcomes. This included the release of detailed data on providers.
Later legislation required biotech and pharma companies to disclose monies paid to research partners. And every physician in the U.S. is now required to be in the National Practitioner Identifier (NPI), a comprehensive public database of providers.
All of this allowed the creation of new types of companies that give both patients and providers more control over their data. Here are some key examples of how.
Allowing patients to access all their own health data in one place
This is a key capability of patients’ newly found access to health data. Think of how often, as a patient, providers aren’t aware of treatment or a test you’ve had elsewhere. Often you end up repeating a test because a provider doesn’t have a record of a test conducted elsewhere.
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Vectra AI picks up $130M at a $1.2B valuation for its network approach to threat detection and response
Cybersecurity nightmares like the SolarWinds hack highlight how malicious hackers continue to exploit vulnerabilities in software and apps to do their dirty work. Today a startup that’s built a platform to help organizations protect themselves from this by running threat detection and response at the network level is announcing a big round of funding to continue its growth.
Vectra AI, which provides a cloud-based service that uses artificial intelligence technology to monitor both on-premise and cloud-based networks for intrusions, has closed a round of $130 million at a post-money valuation of $1.2 billion.
The challenge that Vectra is looking to address is that applications — and the people who use them — will continue to be weak links in a company’s security set-up, not least because malicious hackers are continually finding new ways to piece together small movements within them to build, lay and finally use their traps. While there will continue to be an interesting, and mostly effective, game of cat-and-mouse around those applications, a service that works at the network layer is essential as an alternative line of defense, one that can find those traps before they are used.
“Think about where the cloud is. We are in the wild west,” Hitesh Sheth, Vectra’s CEO, said in an interview. “The attack surface is so broad and attacks happen at such a rapid rate that the security concerns have never been higher at the enterprise. That is driving a lot of what we are doing.”
Sheth said that the funding will be used in two areas. First, to continue expanding its technology to meet the demands of an ever-growing threat landscape — it also has a team of researchers who work across the business to detect new activity and build algorithms to respond to it. And second, for acquisitions to bring in new technology and potentially more customers.
(Indeed, there has been a proliferation of AI-based cybersecurity startups in recent years, in areas like digital forensics, application security and specific sectors like SMBs, all of which complement the platform that Vectra has built, so you could imagine a number of interesting targets.)
The funding is being led by funds managed by Blackstone Growth, with unnamed existing investors participating (past backers include Accel, Khosla and TCV, among other financial and strategic investors). Vectra today largely focuses on enterprises, highly demanding ones with lots at stake to lose. Blackstone was initially a customer of Vectra’s, using the company’s flagship Cognito platform, Viral Patel — the senior MD who led the investment for the firm — pointed out to me.
The company has built some specific products that have been very prescient in anticipating vulnerabilities in specific applications and services. While it said that sales of its Cognito platform grew 100% last year, Cognito Detect for Microsoft Office 365 (a separate product) sales grew over 700%. Coincidentally, Microsoft’s cloud apps have faced a wave of malicious threats. Sheth said that implementing Cognito (or indeed other network security protection) “could have prevented the SolarWinds hack” for those using it.
“Through our experience as a client of Vectra, we’ve been highly impressed by their world-class technology and exceptional team,” John Stecher, CTO at Blackstone, said in a statement. “They have exactly the types of tools that technology leaders need to separate the signal from the noise in defending their organizations from increasingly sophisticated cyber threats. We’re excited to back Vectra and Hitesh as a strategic partner in the years ahead supporting their continued growth.”
Looking ahead, Sheth said that endpoint security will not be a focus for the moment because “in cloud there is so much open territory”. Instead it partners with the likes of CrowdStrike, SentinelOne, Carbon Black and others.
In terms of what is emerging as a stronger entry point, social media is increasingly coming to the fore, he said. “Social media tends to be an effective vector to get in and will remain to be for some time,” he said, with people impersonating others and suggesting conversations over encrypted services like WhatsApp. “The moment you move to encryption and exchange any documents, it’s game over.”
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