Aug
22
2019
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Remediant lands $15M Series A to disrupt privileged access security

Remediant, a startup that helps companies secure privileged access in a modern context, today announced a $15 million Series A led by Dell Technologies Capital and ForgePoint Capital.

Remediant’s co-founders, Paul Lanzi and Tim Keeler, worked in biotech for years and saw a problem first-hand with the way companies secured privileged access. It was granted to certain individuals in the organization carte blanche, and they believed if you could limit access, it would make the space more secure and less vulnerable to hackers.

Lanzi says they started the company with two core concepts. “The first concept is the ability to assess or detect all of the places where privileged accounts exist and what systems they have access to. The second concept is to strip away all of the privileged access from all of those accounts and grant it back on a just-in-time basis,” Lanzi explained.

If you’re thinking that could get in the way of people who need access to do their jobs, as former IT admins, they considered that. Remediant is based on a Zero Trust model where you have to prove you have the right to access the privileged area. But they do provide a reasonable baseline amount of time for users who need it within the confines of continuously enforcing access.

“Continuous enforcement is part of what we do, so by default we grant you four hours of access when you need that access, and then after that four hours, even if you forget to come back and end your session, we will automatically revoke that access. In that way all of the systems that are protected by SecureOne (the company’s flagship product) are held in this Zero Trust state where no one has access to them on a day-to-day basis,” Lanzi said.

Remediant SecureONE Dashboard

Remediant SecureONE Dashboard (Screenshot: Remediant)

The company has bootstrapped until now, and has actually been profitable, something that’s unusual for a startup at this stage of development, but Lanzi says they decided to take an investment in order to shift gears and concentrate on growth and product expansion.

Deepak Jeevankumar, managing director at investor Dell Technologies Capital, says it’s not easy for security startups to rise above the noise, but he saw something in Remediant’s founders. “Tim and Paul came from the practitioner’s viewpoint. They knew the actual problems that people face in terms of privileged access. So they had a very strong empathy towards the customer’s problem because they lived through it,” Jeevankumar told TechCrunch.

He added that the privileged access market hasn’t really been updated in two decades. “It’s a market ripe for disruption. They are combining the just-in-time philosophy with the Zero Trust philosophy, and are bringing that to the crown jewel of administrative access,” he said.

The company’s tools are installed on the customer’s infrastructure, either on-prem or in the cloud. They don’t have a pure cloud product at the moment, but they have plans for a SaaS version down the road to help small and medium-sized businesses solve the privileged access problem.

Lanzi says they are also looking to expand the product line in other ways with this investment. “The basic philosophies that underpin our technology are broadly applicable. We want to start applying our technology in those other areas as well. So as we think toward a future that looks more like cloud and more like DevOps, we want to be able to add more of those features to our products,” he said.

Aug
20
2019
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H2O.ai announces $72.5M Series D led by Goldman Sachs

H2O.ai‘s mission is to democratize AI by providing a set of tools that frees companies from relying on teams of data scientists. Today it got a bushel of money to help. The company announced a $72.5 million Series D round led by Goldman Sachs and Ping An Global Voyager Fund.

Previous investors Wells Fargo, Nvidia and Nexus Venture Partners also participated. Under the terms of the deal, Jade Mandel from Goldman Sachs will be joining the H2O.ai board. Today’s investment brings the total raised to $147 million.

It’s worth noting that Goldman Sachs isn’t just an investor. It’s also a customer. Company CEO and co-founder Sri Ambati says the fact that customers Wells Fargo and Goldman Sachs have led the last two rounds is a validation for him and his company. “Customers have risen up from the ranks for two consecutive rounds for us. Last time the Series C was led by Wells Fargo where we were their platform of choice. Today’s round was led by Goldman Sachs, which has been a strong customer for us and strong supporters of our technology,” Ambati told TechCrunch.

The company’s main product, H2O Driverless AI, introduced in 2017, gets its name from the fact it provides a way for people who aren’t AI experts to still take advantage of AI without a team of data scientists. “Driverless AI is automatic machine learning, which brings the power of a world-class data scientists in the hands of everyone. lt builds models automatically using machine learning algorithms of every kind,” Ambati explained.

They introduced a new recipe concept today, which provides all of the AI ingredients and instructions for building models for different business requirements. H2O.ai’s team of data scientists has created and open-sourced 100 recipes for things like credit risk scoring, anomaly detection and property valuation.

The company has been growing since its Series C round in 2017, when it had 70 employees. Today it has 175 and has tripled the number of customers since the prior round, although Ambati didn’t discuss an exact number. The company has its roots in open source and has 20,000 users of its open-source products, according to Ambati.

He didn’t want to discuss valuation and wouldn’t say when the company might go public, saying it’s early days for AI and they are working hard to build a company for the long haul.

Aug
19
2019
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The five technical challenges Cerebras overcame in building the first trillion-transistor chip

Superlatives abound at Cerebras, the until-today stealthy next-generation silicon chip company looking to make training a deep learning model as quick as buying toothpaste from Amazon. Launching after almost three years of quiet development, Cerebras introduced its new chip today — and it is a doozy. The “Wafer Scale Engine” is 1.2 trillion transistors (the most ever), 46,225 square millimeters (the largest ever), and includes 18 gigabytes of on-chip memory (the most of any chip on the market today) and 400,000 processing cores (guess the superlative).

CS Wafer Keyboard Comparison

Cerebras’ Wafer Scale Engine is larger than a typical Mac keyboard (via Cerebras Systems).

It’s made a big splash here at Stanford University at the Hot Chips conference, one of the silicon industry’s big confabs for product introductions and roadmaps, with various levels of oohs and aahs among attendees. You can read more about the chip from Tiernan Ray at Fortune and read the white paper from Cerebras itself.

Superlatives aside though, the technical challenges that Cerebras had to overcome to reach this milestone I think is the more interesting story here. I sat down with founder and CEO Andrew Feldman this afternoon to discuss what his 173 engineers have been building quietly just down the street here these past few years, with $112 million in venture capital funding from Benchmark and others.

Going big means nothing but challenges

First, a quick background on how the chips that power your phones and computers get made. Fabs like TSMC take standard-sized silicon wafers and divide them into individual chips by using light to etch the transistors into the chip. Wafers are circles and chips are squares, and so there is some basic geometry involved in subdividing that circle into a clear array of individual chips.

One big challenge in this lithography process is that errors can creep into the manufacturing process, requiring extensive testing to verify quality and forcing fabs to throw away poorly performing chips. The smaller and more compact the chip, the less likely any individual chip will be inoperative, and the higher the yield for the fab. Higher yield equals higher profits.

Cerebras throws out the idea of etching a bunch of individual chips onto a single wafer in lieu of just using the whole wafer itself as one gigantic chip. That allows all of those individual cores to connect with one another directly — vastly speeding up the critical feedback loops used in deep learning algorithms — but comes at the cost of huge manufacturing and design challenges to create and manage these chips.

CS Wafer Sean

Cerebras’ technical architecture and design was led by co-founder Sean Lie. Feldman and Lie worked together on a previous startup called SeaMicro, which sold to AMD in 2012 for $334 million (via Cerebras Systems).

The first challenge the team ran into, according to Feldman, was handling communication across the “scribe lines.” While Cerebras’ chip encompasses a full wafer, today’s lithography equipment still has to act like there are individual chips being etched into the silicon wafer. So the company had to invent new techniques to allow each of those individual chips to communicate with each other across the whole wafer. Working with TSMC, they not only invented new channels for communication, but also had to write new software to handle chips with trillion-plus transistors.

The second challenge was yield. With a chip covering an entire silicon wafer, a single imperfection in the etching of that wafer could render the entire chip inoperative. This has been the block for decades on whole-wafer technology: due to the laws of physics, it is essentially impossible to etch a trillion transistors with perfect accuracy repeatedly.

Cerebras approached the problem using redundancy by adding extra cores throughout the chip that would be used as backup in the event that an error appeared in that core’s neighborhood on the wafer. “You have to hold only 1%, 1.5% of these guys aside,” Feldman explained to me. Leaving extra cores allows the chip to essentially self-heal, routing around the lithography error and making a whole-wafer silicon chip viable.

Entering uncharted territory in chip design

Those first two challenges — communicating across the scribe lines between chips and handling yield — have flummoxed chip designers studying whole-wafer chips for decades. But they were known problems, and Feldman said that they were actually easier to solve than expected by re-approaching them using modern tools.

He likens the challenge to climbing Mount Everest. “It’s like the first set of guys failed to climb Mount Everest, they said, ‘Shit, that first part is really hard.’ And then the next set came along and said ‘That shit was nothing. That last hundred yards, that’s a problem.’ ”

And indeed, the toughest challenges, according to Feldman, for Cerebras were the next three, since no other chip designer had gotten past the scribe line communication and yield challenges to actually find what happened next.

The third challenge Cerebras confronted was handling thermal expansion. Chips get extremely hot in operation, but different materials expand at different rates. That means the connectors tethering a chip to its motherboard also need to thermally expand at precisely the same rate, lest cracks develop between the two.

As Feldman explained, “How do you get a connector that can withstand [that]? Nobody had ever done that before, [and so] we had to invent a material. So we have PhDs in material science, [and] we had to invent a material that could absorb some of that difference.”

Once a chip is manufactured, it needs to be tested and packaged for shipment to original equipment manufacturers (OEMs) who add the chips into the products used by end customers (whether data centers or consumer laptops). There is a challenge though: Absolutely nothing on the market is designed to handle a whole-wafer chip.

CS Wafer Inspection

Cerebras designed its own testing and packaging system to handle its chip (via Cerebras Systems).

“How on earth do you package it? Well, the answer is you invent a lot of shit. That is the truth. Nobody had a printed circuit board this size. Nobody had connectors. Nobody had a cold plate. Nobody had tools. Nobody had tools to align them. Nobody had tools to handle them. Nobody had any software to test,” Feldman explained. “And so we have designed this whole manufacturing flow, because nobody has ever done it.” Cerebras’ technology is much more than just the chip it sells — it also includes all of the associated machinery required to actually manufacture and package those chips.

Finally, all that processing power in one chip requires immense power and cooling. Cerebras’ chip uses 15 kilowatts of power to operate — a prodigious amount of power for an individual chip, although relatively comparable to a modern-sized AI cluster. All that power also needs to be cooled, and Cerebras had to design a new way to deliver both for such a large chip.

It essentially approached the problem by turning the chip on its side, in what Feldman called “using the Z-dimension.” The idea was that rather than trying to move power and cooling horizontally across the chip as is traditional, power and cooling are delivered vertically at all points across the chip, ensuring even and consistent access to both.

And so, those were the next three challenges — thermal expansion, packaging and power/cooling — that the company has worked around-the-clock to deliver these past few years.

From theory to reality

Cerebras has a demo chip (I saw one, and yes, it is roughly the size of my head), and it has started to deliver prototypes to customers, according to reports. The big challenge, though, as with all new chips, is scaling production to meet customer demand.

For Cerebras, the situation is a bit unusual. Because it places so much computing power on one wafer, customers don’t necessarily need to buy dozens or hundreds of chips and stitch them together to create a compute cluster. Instead, they may only need a handful of Cerebras chips for their deep-learning needs. The company’s next major phase is to reach scale and ensure a steady delivery of its chips, which it packages as a whole system “appliance” that also includes its proprietary cooling technology.

Expect to hear more details of Cerebras technology in the coming months, particularly as the fight over the future of deep learning processing workflows continues to heat up.

Aug
19
2019
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Ally raises $8M Series A for its OKR solution

OKRs, or Objectives and Key Results, are a popular planning method in Silicon Valley. Like most of those methods that make you fill in some form once every quarter, I’m pretty sure employees find them rather annoying and a waste of their time. Ally wants to change that and make the process more useful. The company today announced that it has raised an $8 million Series A round led by Accel Partners, with participation from Vulcan Capital, Founders Co-op and Lee Fixel. The company, which launched in 2018, previously raised a $3 million seed round.

Ally founder and CEO Vetri Vellore tells me that he learned his management lessons and the value of OKR at his last startup, Chronus. After years of managing large teams at enterprises like Microsoft, he found himself challenged to manage a small team at a startup. “I went and looked for new models of running a business execution. And OKRs were one of those things I stumbled upon. And it worked phenomenally well for us,” Vellore said. That’s where the idea of Ally was born, which Vellore pursued after selling his last startup.

Most companies that adopt this methodology, though, tend to work with spreadsheets and Google Docs. Over time, that simply doesn’t work, especially as companies get larger. Ally, then, is meant to replace these other tools. The service is currently in use at “hundreds” of companies in more than 70 countries, Vellore tells me.

One of its early adopters was Remitly . “We began by using shared documents to align around OKRs at Remitly. When it came time to roll out OKRs to everyone in the company, Ally was by far the best tool we evaluated. OKRs deployed using Ally have helped our teams align around the right goals and have ultimately driven growth,” said Josh Hug, COO of Remitly.

Desktop Team OKRs Screenshot

Vellore tells me that he has seen teams go from annual or bi-annual OKRs to more frequently updated goals, too, which is something that’s easier to do when you have a more accessible tool for it. Nobody wants to use yet another tool, though, so Ally features deep integrations into Slack, with other integrations in the works (something Ally will use this new funding for).

Since adopting OKRs isn’t always easy for companies that previously used other methodologies (or nothing at all), Ally also offers training and consulting services with online and on-site coaching.

Pricing for Ally starts at $7 per month per user for a basic plan, but the company also offers a flat $29 per month plan for teams with up to 10 users, as well as an enterprise plan, which includes some more advanced features and single sign-on integrations.

Aug
19
2019
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Simon Data hauls in $30M Series C to continue building customer data platform

As businesses use an increasing variety of marketing software solutions, the goal around collecting all of that data is to improve customer experience. Simon Data announced a $30 million Series C round today to help.

The round was led by Polaris Partners . Previous investors .406 Ventures and F-Prime Capital also participated. Today’s investment brings the total raised to $59 million, according to the company.

Jason Davis, co-founder and CEO, says his company is trying to pull together a lot of complex data from a variety of sources, while driving actions to improve customer experience. “It’s about taking the data, and then building complex triggers that target the right customer at the right time,” Davis told TechCrunch. He added, “This can be in the context of any sort of customer transaction, or any sort of interaction with the business.”

Companies tend to use a variety of marketing tools, and Simon Data takes on the job of understanding the data and activities going on in each one. Then based on certain actions — such as, say, an abandoned shopping cart — it delivers a consistent message to the customer, regardless of the source of the data that triggered the action.

They see this ability to pull together data as a customer data platform (CDP). In fact, part of its job is to aggregate data and use it as the basis of other activities. In this case, it involves activating actions you define based on what you know about the customer at any given moment in the process.

As the company collects this data, it also sees an opportunity to use machine learning to create more automated and complex types of interactions. “There are a tremendous number of super complex problems we have to solve. Those include core platform or infrastructure, and we also have a tremendous opportunity in front of us on the predictive and data science side as well,” Davis said. He said that is one of the areas where they will put today’s money to work.

The company, which launched in 2014, is based in NYC. The company currently has 87 employees, and that number is expected to grow with today’s announcement. Customers include Equinox, Venmo and WeWork. The company’s most recent funding was a $20 million round in July 2018.

Aug
15
2019
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Incorta raises $30M Series C for ETL-free data processing solution

Incorta, a startup founded by former Oracle executives who want to change the way we process large amounts of data, announced a $30 million Series C today led by Sorenson Capital.

Other investors participating in the round included GV (formerly Google Ventures), Kleiner Perkins, M12 (formerly Microsoft Ventures), Telstra Ventures and Ron Wohl. Today’s investment brings the total raised to $75 million, according to the company.

Incorta CEO and co-founder Osama Elkady says he and his co-founders were compelled to start Incorta because they saw so many companies spending big bucks for data projects that were doomed to fail. “The reason that drove me and three other guys to leave Oracle and start Incorta is because we found out with all the investment that companies were making around data warehousing and implementing advanced projects, very few of these projects succeeded,” Elkady told TechCrunch.

A typical data project involves ETL (extract, transform, load). It’s a process that takes data out of one database, changes the data to make it compatible with the target database and adds it to the target database.

It takes time to do all of that, and Incorta is trying to make access to the data much faster by stripping out this step. Elkady says that this allows customers to make use of the data much more quickly, claiming they are reducing the process from one that took hours to one that takes just seconds. That kind of performance enhancement is garnering attention.

Rob Rueckert, managing director for lead investor Sorenson Capital, sees a company that’s innovating in a mature space. “Incorta is poised to upend the data warehousing market with innovative technology that will end 30 years of archaic and slow data warehouse infrastructure,” he said in a statement.

The company says revenue is growing by leaps and bounds, reporting 284% year over year growth (although they did not share specific numbers). Customers include Starbucks, Shutterfly and Broadcom.

The startup, which launched in 2013, currently has 250 employees, with developers in Egypt and main operations in San Mateo, Calif. They recently also added offices in Chicago, Dubai and Bangalore.

Aug
14
2019
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Every TC Sessions: Enterprise 2019 ticket includes a free pass to Disrupt SF

Shout out to all the savvy enterprise software startuppers. Here’s a quick, two-part money-saving reminder. Part one: TC Sessions: Enterprise 2019 is right around the corner on September 5, and you have only two days left to buy an early-bird ticket and save yourself $100. Part two: for every Session ticket you buy, you get one free Expo-only pass to TechCrunch Disrupt SF 2019.

Save money and increase your ROI by completing one simple task: buy your early-bird ticket today.

About 1,000 members of enterprise software’s powerhouse community will join us for a full day dedicated to exploring the current and future state of enterprise software. It’s certainly tech’s 800-pound gorilla — a $500 billion industry. Some of the biggest names and brightest minds will be on hand to discuss critical issues all players face — from early-stage startups to multinational conglomerates.

The day’s agenda features panel discussions, main-stage talks, break-out sessions and speaker Q&As on hot topics including intelligent marketing automation, the cloud, data security, AI and quantum computing, just to name a few. You’ll hear from people like SAP CEO Bill McDermott; Aaron Levie, Box co-founder; Jim Clarke, director of Quantum Hardware at Intel and many, many more.

Customer experience is always a hot topic, so be sure to catch this main-stage panel discussion with Amit Ahuja (Adobe), Julie Larson-Green (Qualtrics) and Peter Reinhardt (Segment):

The Trials and Tribulations of Experience Management: As companies gather more data about their customers and employees, it should theoretically improve their experience, but myriad challenges face companies as they try to pull together information from a variety of vendors across disparate systems, both in the cloud and on prem. How do you pull together a coherent picture of your customers, while respecting their privacy and overcoming the technical challenges?

TC Sessions: Enterprise 2019 takes place in San Francisco on September 5. Take advantage of this two-part money-saving opportunity. Buy your early-bird ticket by August 16 at 11:59 p.m. (PT) to save $100. And score a free Expo-only pass to TechCrunch Disrupt SF 2019 for every ticket you buy. We can’t wait to see you in September!

Interested in sponsoring TC Sessions: Enterprise? Fill out this form and a member of our sales team will contact you.

Aug
13
2019
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Clumio raises $51M to bring enterprise backup into the 21st century

Creating backups for massive enterprise deployments may feel like a solved problem, but for the most part, we’re still talking about complex hardware and software setups. Clumio, which is coming out of stealth today, wants to modernize enterprise data protection by eliminating the on-premise hardware in favor of a flexible, SaaS-style cloud-based backup solution.

For the first time, Clumio also today announced that it has raised a total of $51 million in a Series A and B round since it was founded in 2017. The $11 million Series A round closed in October 2017 and the Series B round in November 2018, Clumio founder and CEO Poojan Kumar told me. Kumar’s previous company, storage startup PernixData, was acquired by Nutanix in 2016. It doesn’t look like the investors made their money back, though.

Clumio is backed by investors like Sutter Hill Ventures, which led the Series A, and Index Ventures, which drove the Series B together with Sutter Hill. Other individual investors include Mark Leslie, founder of Veritas Technologies, and John Thompson, chairman of the board at Microsoft .

2019 08 12 1904

“Enterprise workloads are being ‘SaaS-ified’ because IT can no longer afford the time, complexity and expense of building and managing heavy on-prem hardware and software solutions if they are to successfully deliver against their digital transformation initiatives,” said Kumar. “Unlike legacy backup vendors, Clumio SaaS is born in the cloud. We have leveraged the most secure and innovative cloud services available, now and in the future, within our service to ensure that we can meet customer requirements for backup, regardless of where the data is.”

In its current iteration, Clumio can be used to secure data from on-premise, VMware Cloud for AWS and native AWS service workloads. Given this list, it doesn’t come as a surprise that Clumio’s backend, too, makes extensive use of public cloud services.

The company says that it already has several customers, though it didn’t disclose any in today’s announcement.

Aug
13
2019
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Rimeto lands $10M Series A to modernize the corporate directory

The notion of the corporate directory has been around for many years, but in a time of more frequent turnover and shifting responsibilities, the founders of Rimeto, a three-year-old San Francisco startup, wanted to update it to reflect those changes.

Today, the company announced a $10 million Series A investment from USVP, Bow Capital, Floodgate and Ray Dalio, founder of Bridgewater Associates.

Co-founder Ted Zagat says that the founders observed shifting workplace demographics and changes in the way people work. They believed it required a better way to locate people inside large organizations, which typically used homegrown methods or relied on Outlook or other corporate email systems.

“On one hand, we have people being asked to work much more collaboratively and cross-functionally. On the other, is an increasingly fragmented workplace. Employees really need help to be able to understand each other and work together effectively. That’s a real challenge for them,” Zagat explained.

Rimeto has developed a richer directory by sitting between various corporate systems like HR, CRM and other tools that contain additional details about the employee. It of course includes a name, title, email and phone like the basic corporate system, but it goes beyond that to find areas of expertise, projects the person is working on and other details that can help you find the right person when you’re searching the directory.

Rimeto product version 1 1

Rimeto directory on mobile and web (Screenshot: Rimeto)

Zagat says that by connecting to these various corporate systems and layering on a quality search tool with a variety of filters to narrow the search, it can help employees connect to others inside an organization more easily, something that is often difficult to do in large companies.

The tool can be accessed via web or mobile app, or incorporated into a company intranet. It also could be accessed from a tool like Slack or Microsoft Teams.

The three founders — Zagat, Neville Bowers and Maxwell Hayman — all previously worked at Facebook. Unlike a lot of early-stage startups, the company has paying customers (although it won’t share exactly how many) and reports that it’s cash-flow positive. Up to this point, the three founders had bootstrapped the company, but they wanted to go out and raise some capital to begin to expand more rapidly.

Aug
12
2019
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Polarity raises $8.1M for its AI software that constantly analyzes employee screens and highlights key info

Reference docs and spreadsheets seemingly make the world go ’round, but what if employees could just close those tabs for good without losing that knowledge?

One startup is taking on that complicated challenge. Predictably, the solution is quite complicated, as well, from a tech perspective, involving an AI solution that analyzes everything on your PC screen — all the time — and highlights text onscreen for which you could use a little bit more context. The team at Polarity wants its tech to help teams lower the knowledge barrier to getting stuff done and allow people to focus more on procedure and strategy than memorizing file numbers, IP addresses and jargon.

The Connecticut startup just closed an $8.1 million “AA” round led by TechOperators, with Shasta Ventures, Strategic Cyber Ventures, Gula Tech Adventures and Kaiser Permanente Ventures also participating in the round. The startup closed its $3.5 million Series A in early 2017.

Interestingly, the enterprise-centric startup pitches itself as an AR company, augmenting what’s happening on your laptop screen much like a pair of AR glasses could.

The startup’s computer vision software that uses character recognition to analyze what’s on a user’s screen can be helpful for enterprise teams importing things like a company Rolodex so that bios are always collectively a click away, but the real utility comes from team-wide flagging of things like suspicious IP addresses that will allow entire teams to learn about new threats and issues at the same time without having to constantly check in with their co-workers. The startup’s current product has a big focus on analysts and security teams.

Polarity before and after two

via Polarity

Using character recognition to analyze a screen for specific keywords is useful in itself, but that’s also largely a solved computer vision problem.

Polarity’s big advance has been getting these processes to occur consistently on-device without crushing a device’s CPU. CEO Paul Battista says that for the average customer, Polarity’s software generally eats up about 3-6% of their computer’s processing power, though it can spike much higher if the system is getting fed a ton of new information at once.

“We spent years building the tech to accomplish [efficiency], readjusting how people think of [object character recognition] and then doing it in real time,” Battista tells me. “The more data that you have onscreen, the more power you use. So it does use a significant percentage of the CPU.”

Why bother with all of this AI trickery and CPU efficiency when you could pull this functionality off in certain apps with an API? The whole deliverable here is that it doesn’t matter if you’re working in Chrome, or Excel or pulling up a scanned document, the software is analyzing what’s actually being rendered onscreen, not what the individual app is communicating.

When it comes to a piece of software analyzing everything on your screen at all times, there are certainly some privacy concerns, not only from the employee’s perspective but from a company’s security perspective.

Battista says the intent with this product isn’t to be some piece of corporate spyware, and that it won’t be something running in the background — it’s an app that users will launch. “If [companies] wanted to they could collect all of the data on everybody’s screens, but we don’t have any customers doing that. The software is built to have a user interface for users to interact with so if the user didn’t choose to subscribe or turn on a metric, then [the company] wouldn’t be able to force them to collect it in the current product.”

Battista says that teams at seven Fortune 100 companies are currently paying for Polarity, with many more in pilot programs. The team is currently around 20 people and with this latest fundraise, Battista wants to double the size of the team in the next 18 months as they look to scale to larger rollouts at major companies.

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