Product analytics startup Kubit lands $18M in fresh capital

Showing that the product analytics industry is alive and well, Kubit today announced that it raised $18 million in a Series A funding round led by Insight Partners, bringing its total capital raised to $24 million. Kubit says that it’ll put the fresh cash toward growing its team and expanding its platform, which helps customers manage their data quality.

CEO Alex Li says that he founded Kubit in 2018 to solve what he believes is one of the biggest pain points of the product analytics space: losing data control and lack of transparency. Previously, Li spent 10 years building mobile apps and used product analytics tools at companies including Smule, Booyah and eBay. These tools fell short of his expectations, he says, in that they often required sending data to third parties and created siloed analytics practices.

“Product analytics has proven its significance in many large enterprises’ successes. [But as] an industry, product analytics is still young and has become more open and transparent,” Li told TechCrunch via email. “No enterprise wants to be locked into a siloed blackbox solution that demands control of a customer’s precious data.”

As a refresher, product analytics is the process of analyzing how users engage with a product — whether an app, website or subscription service — to allow companies to track and analyze user engagement data. The idea is to use the data to improve and optimize the product, and identify bugs as they crop up in the deployment process.

Kubit’s platform is designed to work with existing cloud data warehouses (i.e. central repositories of business data) without the need to transform, normalize or convert product analytics data. In this way, Kubit eliminates the need for batch jobs and the duplication of data, Li argues, potentially reducing cloud computing costs.

“We help our customers to analyze their users’ behavior patterns through their user data. Kubit doesn’t store or process any personally identifiable information and most of the user data is already anonymized,” Li said. “With our integration[s], our customers have full control of their data and can make changes whenever they want, including deletion.”

Li sees Amplitude, June and Mixpanel as direct competitors in the product analytics segment. Mixpanel and Amplitude have formidable warchests. But Li tells TechCrunch that both Kubit’s revenue and headcount (13 people) is projected to triple this year, driven by a growing customer base that includes “several largest enterprises in entertainment, social and education fields.”

“[I]t seems that the consumer segment has recovered very well [from the pandemic] and the surviving enterprises are the winners who really see the needs of product analytics to keep their growth going strong,” Li said. “Moreover, the down time [during the pandemic] gave many data teams the opportunity to reinforce their modern data stack and realize the significance of data control and transparency.”

Showing that the product analytics industry is alive and well, Kubit today announced that it raised $18 million in a Series A funding round led by Insight Partners, bringing its total capital raised to $24 million. Kubit says that it’ll put the fresh cash toward growing its team and expanding its platform, which helps customers

PassiveLogic, which creates digital twins of building systems, raises $15M

PassiveLogic, a startup developing a platform to autonomously control building systems, today announced that it raised $15 million from Brookfield Growth, the investment arm of asset management firm Brookfield. CEO Troy Harvey says that the new capital will be put toward growing PassiveLogic’s team and “launching an ecosystem of products that enable autonomy.”

Analysts at McKinsey (among others) predict the pandemic will spur an interest in more comfortable, sustainable working spaces. But assuming that comes to pass, legacy industrial building controls threaten to make such projects daunting. According to market research agency ARC Advisory Group, there are roughly $65 billion worth of distributed building control systems nearing their end of life, with many of those systems over 25 years old.

“Right now, building controls utilize very limited information to make sure the building works accordingly,” Harvey told TechCrunch via email. “To get to the future of real estate, there needs to be a digital platform that can aggregate building data, [allowing] building managers to customize automation controls and act upon it in real-time.”

PassiveLogic provides this solution, Harvey claims, built around what he calls the “Hive” controller. PassiveLogic’s product is designed to enable autonomous control of legacy building systems by interfacing with them using a combination of sensors, equipment and devices that don’t need cloud connectivity.

Harvey founded Salt Lake City, Utah-based PassiveLogic in 2016 with Jeremy Fillingim. Harvey was previously the CEO of Heliocentric, an engineering firm that worked with clients to architect “next-generation” buildings. Fillingim was a partner at Mote Systems, where he designed a touchscreen universal remote control.

PassiveLogic hosts a software environment, Autonomy Studio, where customers can create building system models from CAD or 3D models. The software uses these models to generate a “physics-based digital twin” that conforms to a descriptive standard called Quantum. Applications written in Quantum can be deployed within PassiveLogic’s control hardware — the aforementioned Hive.

Harvey asserts that Quantum provides “virtual analogs” to real-world objects via algorithms that attempt to understand how a building’s equipment and systems interact. Based on these predictions, PassiveLogic makes control and management decisions for maintenance and operation.

“PassiveLogic is creating [a] platform for generalized autonomy. The aim is to empower anyone to effortlessly design their own custom applications — without requiring a team of Ph.D.s,” Harvey said. “[There’s] no programming required to understand how a building’s equipment and systems interact.”

Digital twin technologies, which digitally models real-world systems, aren’t new. GE, AWS and other companies offer products that allow customers to model digital twins of machines. London-based SenSat creates digital twin models of locations for construction, mining and energy projects. Meanwhile, startups like Lacuna and Nexar are building digital twins of entire cities.

But digital twin technologies share the same limitations, chief among them inaccurate modeling in the presence of inaccurate data. Indeed, the models are only as good as the data that’s used to develop them. As Gartner notes in a report: “It is difficult to anticipate the nature of the simulation models, data types, and data analysis of sensor data that might be necessary to support the design, introduction, and service life of the digital twins’ physical counterparts. While 3D geometry is sufficient to communicate the digital twin visually and how parts fit together, the geometric model may not be able to perform simulations of the behavior of the physical counterpart in use or operation. At the same time, the geometric model may not be able to analyze data if it is not enriched with additional information.”

Another potential pitfall is the proprietary nature of digital twin platforms. Digital twins with long life cycles, like those of building systems, might extend well beyond the lifespans of the software and hardware used to create and maintain them.

Harvey says that PassiveLogic’s Hive hardware is engineered to combat data inaccuracy, and he challenges the idea that PassiveLogic will ever leave its customers without the necessary software updates.

PassiveLogic, a startup developing a platform to autonomously control building systems, today announced that it raised $15 million from Brookfield Growth, the investment arm of asset management firm Brookfield. CEO Troy Harvey says that the new capital will be put toward growing PassiveLogic’s team and “launching an ecosystem of products that enable autonomy.” Analysts at

EvolutionIQ secures $21M to streamline insurance claims processing with AI

Processing claims at scale presents a challenge for insurers, particularly where the claims entail factors like complex underlying health conditions. According to data from the National Association of Insurance Commissioners, the second-most common complaint that insurance customers made in 2021 was claim delays, ranked only after unsatisfactory settlement offers.

The pandemic placed an additional strain on insurers, with an RGA survey finding that claims acceptance rates for permanent disability, critical illness and long-term care have been minimal over the past two years. Even with reduced claims requirements, the average end-to-end time for claims rose from 34 days pre-pandemic to 43 days.

A growing cohort of startups, including Alan, Tractable and Snapsheet, offers tools to help customers navigate the insurance claims process. But former Google AI tech leader Tomas Vykruta is taking a different tack with EvolutionIQ, which works with insurers to analyze claimant data and third-party information to identify “high-opportunity” claims — specifically those involving bodily injury.

EvolutionIQ today announced that it raised $21 million in a Series A round led by Brewer Lane Ventures with participation from FirstRound Capital, FirstMark Capital, Foundation Capital, Altai Ventures, Asymmetric Ventures and insurance carriers Reliance Standard Life, New York Life Ventures, Guardian Life and Sedgwick. It brings the company’s total capital raised to $26.1 million at a “north of” $150 million valuation, following small seed and venture rounds in 2019 and 2020.

“EvolutionIQ assists insurance professionals in improving claims handling through insights uncovered by analyzing historical claims data,” Vykruta told TechCrunch via email. “With our decision intelligence platform, claims teams can recoup lost time and streamline processes. Our software allows front line operators to make more informed decisions and focus their energy on high-potential claims. For managers, we’re able to identify claims blocks that need further investigation and those that are easily resolved — and then provide guidance to make it happen.”

Accelerating insurance claims

Vykruta has a long and fascinating career in the tech industry. He’s the founder of two video game studios, Antartica and Electrolab, and was one of the lead console software architects at Surreal Software, a Warner Bros. Interactive Entertainment subsidiary. In 2008, Vykruta took a job at Microsoft as a senior engineer at the advanced technology group, where he worked on software for the Xbox 360. And in 2016, he joined Waymo, Google parent company Alphabet’s autonomous car division, as a machine learning engineer.

Vykruta spent the last roughly decade of his career at various Google machine learning teams prior to co-founding EvolutionIQ with Jonathan Lewin and Michael Saltzman, one of the founders of on-campus furniture rental business Roomie.

“While adoption of AI in a traditional industry can be challenging, [it can be] overcome by building ‘explainable AI’ systems in tight collaboration with the users,” Vykruta said. “There [must be] two systems: one that makes predictions [and] one that explains the forecasted outcomes in [plain] language.”

With EvolutionIQ, Vykruta, Lewin and Saltzman set out to design a platform that evaluates the history of claims — specifically health-related claims — up until today to answer questions like “Is this a claim on which we can take action?” and “Is there going to be an outcome that makes sense?” Using EvolutionIQ, Vykruta claims that a claims adjuster, who may have hundreds of cases active at a time, can better understand short-term disability, long-term disability, workers comp and property and casualty claims; identify the most actionable opportunities; and see the results in the context of new data and events.

“[C]laims are really incredibly complicated because you’ve got these narratives that are open for many years. Some of these claims are 15 or 20 years old. They have as many as 30 to 40 medical diagnoses. The insurance industry can’t solve this comprehensively,” Vykruta explained. “What this has led to is just incredible waste in unnecessary payments. Examiners [may] look at the last couple of pages of notes and generally, they don’t take any action at all.”

Informed by its predictive algorithms, EvolutionIQ spotlights dozens of claims from tens of thousands that are most likely to have the greatest outcome for claimants, carriers and clients. EvolutionIQ also monitors open claims to guide workers to those that require more attention or new actions, including claims that have potentially fallen through the cracks.

“Claims examiners want to focus on the cases that have the greatest impact on customers and carriers. But claims handling involves archaic and manual processes that require examiners to review too much information or to evaluate data on their own, despite the fact that each claim involves multiple people and systems,” Vykruta said. “Therefore, we built a decision intelligence platform that acts as an AI-enabled copilot to identify high-opportunity claims early in the lifecycle and cases most likely to be referred to adjusters.”

Market opportunity

The way Vykruta sees it, EvolutionIQ’s value proposition is reducing overpayment, waste and longer claim durations. Insurance carriers’ teams are stretched thin by rising claim volumes, and the systems responsible for making management scaleable actually compound the problem, he asserts — forcing examiners to make tradeoffs that lead to slower decisions and mistakes.

“Claims can last years, many worth six figures, and involve hundreds of pages and many formats (structured and unstructured) constantly flowing in … We actually give time back to claims organizations by identifying the cases that don’t need to be addressed and can be set to be resolved,” Vykruta said. “[We’re working with] the top disability carriers, property-casualty carriers and third-party administrators including Reliance Standard, Principal, Sun Life, Argo Group, Matrix Absence Management and FullscopeRMS.”

Of course, AI has a well-known bias problem, and reporting throughout the years has exposed how supposedly “fair” algorithms in insurance can perpetuate different forms of discrimination. Algorithms have led insurance companies to charge minority communities higher premiums than white communities, for example — even when the risks are the same. Asked how EvolutionIQ mitigates the potential for this sort of bias, Vykruta says that the company invests in “explainability” approaches that make it clear what sort of factors lead to the platform’s decisions.

“EvolutionIQ is not just taking the raw weight from the model. They translate the system that makes sense to [claims adjusters],” Vykruta said. “[The platform is] only showing them [items] that are actionable.”

Risks aside, EvolutionIQ isn’t alone in tackling the opportunity. Among its rivals are CCC Intelligent Solutions, a technology solutions provider for the automotive and insurance industries that relies on data science to expedite claims processing. Riskcovry is another startup in the nascent space, with customers including banks, fintechs and supply chain brands.

But the insurtech market is flush with cash, with one report estimating that global investments in insurance tech startups in 2021 topped $10 billion, up from $3.1 billion in 2020.

Vykruta says that EvolutionIQ — which was cash flow-positive in 2021 — will spend the bulk of the new capital on expanding its engineering, data science, product and customer success teams. Customer acquisition will also be a focus going forward, he says, as EvolutionIQ explores new and emerging categories of insurance.

Processing claims at scale presents a challenge for insurers, particularly where the claims entail factors like complex underlying health conditions. According to data from the National Association of Insurance Commissioners, the second-most common complaint that insurance customers made in 2021 was claim delays, ranked only after unsatisfactory settlement offers. The pandemic placed an additional strain

Box is adding free whiteboarding tool for collaborating on visual content

When you talk to folks about what they have missed most about the office since we moved to work from home in 2020, people often point to whiteboarding in a conference room with colleagues, something they have said was hard to do in a digital context. Many companies have tried to fill that void, including startups Mural and Miro, the latter of which had a fat $17.5 billion valuation in its most recent round.

Today, Box is entering the fray with the announcement of Box Canvas, a tool that lets you do virtual whiteboard-style brainstorming, but also gives you a place to collaborate on various types of visual content such as a product workflow or a brick and mortar merchandising plan.

Box CEO and co-founder Aaron Levie says that his product integrates with the Microsoft and Google office suites for structured document collaboration, and it also has its own native tooling with Box Notes, but the idea is to bring that ability to collaborate from the realm of structured documents to more visual kinds of content.

“With Canvas, we’re bringing that same kind of experience, but more to visual collaboration, so that kind of virtual whiteboard type experience. I think even though it’s a space that has seen great innovation from many companies, I still believe it’s actually extremely early in this market. And I think we’re only just starting to see the kind of potential of what work is going to look like in this hybrid way of working.”

While Box will bundle Canvas into its new suite offering at no additional charge, it also intends to make it available for free as a standalone offering for anyone who wants to use it with no limits. He said the intention is to never charge for this capability moving forward.

“There are a bunch of core activities that we think every user on the planet is going to want to do with their content. You’re going to want to store it, share it, collaborate around it, get it signed. And so we want to make as many of those core capabilities available to the widest number of people — and then we’ll have advanced features based on your ability to govern that data or make it compliant for a specific industry. Those will be very advanced capabilities that we continue to have more and more of over time [and we will charge companies for those capabilities],” he said.

Product-led growth has worked for the company from its earliest days when it began offering a free tier of Box, and Levie sees offering Canvas for free as an extension of that thinking, something the company intends to fully embrace moving forward.

As for those other companies producing similar software, Levie says this isn’t about competing with them because it’s a free add-on. “I don’t really think about it as having to compete with anyone, frankly, because it’s for our customers. So if you’re a Box customer and you’re using a Box for managing your most important content, this is just another really valuable way to get your work done.”

Box Canvas will be available in the fall, according to the company.

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