artificial intelligence
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3 methodologies for automated video game highlight detection and capture
With the rise of livestreaming, gaming has evolved from a toy-like consumer product to a legitimate platform and medium in its own right for entertainment and competition.
Twitch’s viewer base alone has grown from 250,000 average concurrent viewers to over 3 million since its acquisition by Amazon in 2014. Competitors like Facebook Gaming and YouTube Live are following similar trajectories.
The boom in viewership has fueled an ecosystem of supporting products as today’s professional streamers push technology to its limit to increase the production value of their content and automate repetitive aspects of the video production cycle.
The largest streamers hire teams of video editors and social media managers, but growing and part-time streamers struggle to do this themselves or come up with the money to outsource it.
The online streaming game is a grind, with full-time creators putting in eight- if not 12-hour performances on a daily basis. In a bid to capture valuable viewer attention, 24-hour marathon streams are not uncommon either.
However, these hours in front of the camera and keyboard are only half of the streaming grind. Maintaining a constant presence on social media and YouTube fuels the growth of the stream channel and attracts more viewers to catch a stream live, where they may purchase monthly subscriptions, donate and watch ads.
Distilling the most impactful five to 10 minutes of content out of eight or more hours of raw video becomes a non-trivial time commitment. At the top of the food chain, the largest streamers can hire teams of video editors and social media managers to tackle this part of the job, but growing and part-time streamers struggle to find the time to do this themselves or come up with the money to outsource it. There aren’t enough minutes in the day to carefully review all the footage on top of other life and work priorities.
Computer vision analysis of game UI
An emerging solution is to use automated tools to identify key moments in a longer broadcast. Several startups compete to dominate this emerging niche. Differences in their approaches to solving this problem are what differentiate competing solutions from each other. Many of these approaches follow a classic computer science hardware-versus-software dichotomy.
Athenascope was one of the first companies to execute on this concept at scale. Backed by $2.5 million of venture capital funding and an impressive team of Silicon Valley Big Tech alumni, Athenascope developed a computer vision system to identify highlight clips within longer recordings.
In principle, it’s not so different from how self-driving cars operate, but instead of using cameras to read nearby road signs and traffic lights, the tool captures the gamer’s screen and recognizes indicators in the game’s user interface that communicate important events happening in-game: kills and deaths, goals and saves, wins and losses.
These are the same visual cues that traditionally inform the game’s player what is happening in the game. In modern game UIs, this information is high-contrast, clear and unobscured, and typically located in predictable, fixed locations on the screen at all times. This predictability and clarity lends itself extremely well to computer vision techniques such as optical character recognition (OCR) — reading text from an image.
The stakes here are lower than self-driving cars, too, since a false positive from this system produces nothing more than a less-exciting-than-average video clip — not a car crash.
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Advanced rider assistance systems: Tech spawned by the politics of micromobility
The desire to achieve something as simple as keeping shared electric scooters off sidewalks has driven the development of some advanced technology in the micromobility industry. Once the province of geofencing, scooter companies are so eager to get a leg up on the competition that they’re now implementing technology similar to advanced driver assistance systems (ADAS) usually found in cars.
Operators like Spin, Voi, Zipp, Bird and Superpedestrian are investing in camera-based or location-based tech that can detect and even correct poor rider behavior, sometimes going to the extent of slowing scooters to a stop if they’re riding on a sidewalk.
People riding or parking scooters on sidewalks is a big problem for cities and forms one of the main complaints from NIMBYist residents who dislike change all the more when it becomes a tripping hazard. Companies are trying to solve this problem with tech that effectively puts the onus of rider behavior on operators, which may result in cities requiring scooter operators to have this sort of ADAS tech.
Scooter ADAS is probably the most doable and cost-effective method that cities can use to prevent unwanted rider behavior. And, it’s far cheaper than trying to police rider behavior themselves, or, address the lack of protected cycling infrastructure.
“This technology comes from a need for protected bike lanes,” said Dmitry Shevelenko, co-founder and president of Tortoise, an automated vehicle positioning service for micromobility companies. “It exists in this world where riders kind of have to do things that aren’t that great for others, because they have nowhere else to go. And so that’s the true driver of the need for this.”
Cities can solve this problem for the long term by building bike lanes or creating scooter parking bays, but until that happens, operators need to reassure local administrations that micromobility is safe, compliant and a good thing for cities.
“Until cities have dedicated infrastructure for whatever new modality comes to play, you have to figure out a way to use technology to make sure things don’t mix poorly,” said Alex Nesic, co-founder and chief business officer of Drover AI, a computer vision startup that provides camera-based scooter ADAS. “That’s really what we’re after. We want to enable this kind of maturation of the industry.”
Street views versus satellite views
Drover AI works with Spin, while Luna, another computer vision company, works with Voi and Zipp to attach cameras, sensors and a microprocessor to scooters to detect lanes, sidewalks, pedestrians and other environmental surroundings.
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DataRobot CEO Dan Wright coming to TC Sessions: SaaS to discuss role of data in machine learning
Just about every company is sitting on vast amounts of data, which they can use to their advantage if they can just learn how to harness it. Data is actually the fuel for machine learning models, and with the proper tools, businesses can learn to process this data and build models to help them compete in a rapidly changing marketplace, to react more quickly to shifting customer requirements and to find insights faster than any human ever possibly could.
Boston-based DataRobot, a late-stage startup that has built a platform to help companies navigate the machine learning model lifecycle, has been raising money by the bushel over the last several years, including $206 million in September 2019 and another $300 million in July. DataRobot CEO Dan Wright will be joining us on a panel to discuss the role of data in business at TC Sessions: SaaS on October 27th.
The company covers the gamut of the machine learning lifecycle, including preparing data, operationalizing it and finally building APIs to make it useful for the organization as it attempts to build a soup-to-nuts platform. DataRobot’s broad platform approach has appealed to investors.
As we wrote at the time of the $206 million round:
The company has been catching the attention of these investors by offering a machine learning platform aimed at analysts, developers and data scientists to help build predictive models much more quickly than it typically takes using traditional methodologies. Once built, the company provides a way to deliver the model in the form of an API, simplifying deployment.
DataRobot has raised a total of $1 billion on $6.3 billion post valuation, according to PitchBook data, and it’s been putting that money to work to add to its platform of services. Most recently the company acquired Algorithmia, which helps manage machine learning models.
As the pandemic has pushed more business online, companies are always looking for an edge, and one way to achieve that is by taking advantage of AI and machine learning. Wright will be joined on the data panel by Monte Carlo co-founder and CEO Barr Moses and AgentSync co-founder and CTO Jenn Knight to discuss the growing role of data in business operations
In addition to our discussion with Wright, the conference will also include Microsoft’s Jared Spataro, Amplitude’s Olivia Rose, as well as investors Kobie Fuller and Laela Sturdy, among others. We hope you’ll join us. It’s going to be a thought-provoking lineup.
Buy your pass now to save up to $100. We can’t wait to see you in October!
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Affinity, a relationship intelligence company, raises $80M to help close deals
Relationships ultimately close deals, but long-term relationships come with a lot of baggage, i.e. email interactions, documents and meetings.
Affinity wants to take what Ray Zhou, co-founder and CEO, refers to as “data exhaust,” all of those daily interactions and communications, and apply machine learning analysis and provide insights on who in the organization has the best chance of getting that initial meeting and closing the deal.
Today, the company announced $80 million in Series C funding, led by Menlo Ventures, which was joined by Advance Venture Partners, Sprints Capital, Pear Ventures, Sway Ventures, MassMutual Ventures, Teamworthy and ECT Capital Partners’ Brian N. Sheth. The new funding gives the company $120 million in total funding since it was founded in 2014.
Affinity, based in San Francisco, is focused on industries like investment banking, private equity, venture capital, consulting and real estate, where Zhou told TechCrunch there aren’t customer relationship management systems or networking platforms that cater to the specific needs of the long-term relationship.
Stanford grads Zhou and co-founder Shubham Goel started the company after recognizing that while there was software for transactional relationships, there wasn’t a good option for the relationship journeys.
He cites data that show up to 90% of company profiles and contact information living in traditional CRM systems are incomplete or out of date. This comes as market researcher Gartner reported the global CRM software market grew 12.6% to $69 billion in 2020.
“It is almost bigger than sales,” Zhou said. “Our worldview is that relationships are the biggest industries in the world. Some would disagree, but relationships are an asset class, they are a currency that separates the winners from the losers.”
Instead, Affinity created “a new breed of CRM,” Zhou said, that automates the inputting of that data constantly and adds information, like revenue, staff size and funding from proprietary data sources, to assign a score to a potential opportunity and increase the chances of closing a deal.
Affinity people profile. Image Credits: Affinity
He intends to use the new funding to expand sales, marketing and engineering to support new products and customers. The company has 125 employees currently; Zhou expects to be over 200 by next year.
To date, the company’s platform has analyzed over 18 trillion emails and 213 million calendar events and currently drives over 500,000 new introductions and tracks 450,000 deals per month. It also has more than 1,700 customers in 70 countries, boasting a list that includes Bain Capital Ventures, Kleiner Perkins, SoftBank Group, Nike, Qualcomm and Twilio.
Tyler Sosin, partner at Menlo Ventures, said he met Zhou and Goel at a time when the firm was looking into CRM companies, but it wasn’t until years later that Affinity came up again when Menlo itself wanted to work with a more modern platform.
As a user of Affinity himself, Sosin said the platform gives him the data he cares about and “removes the manual drudgery of entry and friction in the process.” Affinity also built a product that was intuitive to navigate.
“We have always had an interest in getting CRMs to the next generation, and Affinity is defining itself in a new category of relationship intelligence and just crushing it in the private capital markets,” he said. “They are scaling at an impressive growth rate and solving a hard problem that we don’t see many other companies in the space doing.”
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Fin names former Twilio exec Evan Cummack as CEO, raises $20M
Work insights platform Fin raised $20 million in Series A funding and brought in Evan Cummack, a former Twilio executive, as its new chief executive officer.
The San Francisco-based company captures employee workflow data from across applications and turns it into productivity insights to improve the way enterprise teams work and remain engaged.
Fin was founded in 2015 by Andrew Kortina, co-founder of Venmo, and Facebook’s former VP of product and Slow Ventures partner Sam Lessin. Initially, the company was doing voice assistant technology — think Alexa but powered by humans and machine learning — and then workplace analytics software in 2020. You can read more about Fin’s origins at the link below.
The new round was led by Coatue, with participation from First Round Capital, Accel and Kleiner Perkins. The original team was talented, but small, so the new funding will build out sales, marketing and engineering teams, Cummack said.
“At that point, the right thing was to raise money, so at the end of last year, the company raised a $20 million Series A, and it was also decided to find a leadership team that knows how to build an enterprise,” Cummack told TechCrunch. “The company had completely pivoted and removed ‘Analytics’ from our name because it was not encompassing what we do.”
Fin’s software measures productivity and provides insights on ways managers can optimize processes, coach their employees and see how teams are actually using technology to get their work done. At the same time, employees are able to manage their workflow and highlight areas where there may be bottlenecks. All combined, it leads to better operations and customer experiences, Cummack said.
Graphic showing how work is really done. Image Credits: Fin
Fin’s view is that as more automation occurs, the company is looking at a “renaissance of human work.” There will be more jobs and more types of jobs, but people will be able to do them more effectively and the work will be more fulfilling, he added.
Particularly with the use of technology, he notes that in the era before cloud computing, there was a small number of software vendors. Now with the average tech company using over 130 SaaS apps, it allows for a lot of entrepreneurs and adoption of best-in-breed apps so that a viable company can start with a handful of people and leverage those apps to gain big customers.
“It’s different for enterprise customers, though, to understand that investment and what they are spending their money on as they use tools to get their jobs done,” Cummack added. “There is massive pressure to improve the customer experience and move quickly. Now with many people working from home, Fin enables you to look at all 130 apps as if they are one and how they are being used.”
As a result, Fin’s customers are seeing metrics like 16% increase in team utilization and engagement, a 25% decrease in support ticket handle time and a 71% increase in policy compliance. Meanwhile, the company itself is doubling and tripling its customers and revenue each year.
Now with leadership and people in place, Cummack said the company is positioned to scale, though it already had a huge head start in terms of a meaningful business.
Arielle Zuckerberg, partner at Coatue, said via email that she was part of a previous firm that invested in Fin’s seed round to build a virtual assistant. She was also a customer of Fin Assistant until it was discontinued.
When she heard the company was pivoting to enterprise, she “was excited because I thought it was a natural outgrowth of the previous business, had a lot of potential and I was already familiar with management and thought highly of them.”
She believed the “brains” of the company always revolved around understanding and measuring what assistants were doing to complete a task as a way to create opportunities for improvement or automation. The pivot to agent-facing tools made sense to Zuckerberg, but it wasn’t until the global pandemic that it clicked.
“Service teams were forced to go remote overnight, and companies had little to no visibility into what people were doing working from home,” she added. “In this remote environment, we thought that Fin’s product was incredibly well-suited to address the challenges of managing a growing remote support team, and that over time, their unique data set of how people use various apps and tools to complete tasks can help business leaders improve the future of work for their team members. We believe that contact center agents going remote was inevitable even before COVID, but COVID was a huge accelerant and created a compelling ‘why now’ moment for Fin’s solution.”
Going forward, Coatue sees Fin as “a process mining company that is focused on service teams.” By initially focusing on customer support and contact center use case — a business large enough to support a scaled, standalone business — rather than joining competitors in going after Fortune 500 companies where implementation cycles are long and there is slow time-to-value, Zuckerberg said Fin is better able to “address the unique challenges of managing a growing remote support team with a near-immediate time-to-value.”
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Tape It launches an AI-powered music recording app for iPhone
Earlier this year, Apple officially discontinued Music Memos, an iPhone app that allowed musicians to quickly record audio and develop new song ideas. Now, a new startup called Tape It is stepping in to fill the void with an app that improves audio recordings by offering a variety of features, including higher-quality sound, automatic instrument detection, support for markers, notes and images, and more.
The idea for Tape It comes from two friends and musicians, Thomas Walther and Jan Nash.
Walther had previously spent three and a half years at Spotify, following its 2017 acquisition of the audio detection startup Sonalytic, which he had co-founded. Nash, meanwhile, is a classically trained opera singer, who also plays bass and is an engineer.
They’re joined by designer and musician Christian Crusius, previously of the design consultancy Fjord, which was acquired by Accenture.
The founders, who had played in a band together for many years, were inspired to build Tape It because it was something they wanted for themselves, Walther says. After ending his stint at Spotify working in their new Soundtrap division (an online music startup Spotify also bought in 2017), he knew he wanted to work on a project that was more focused on the music-making side of things. But while Soundtrap worked for some, it wasn’t what either Walther or his friends had needed. Instead, they wanted a simple tool that would allow them to record their music with their phone — something that musicians often do today using Apple’s Voice Memos app and, briefly, Music Memos — until its demise.
Image Credits: Tape It
“Regardless of whether you’re an amateur or even like a touring professional…you will record your ideas with your phone, just because that’s what you have with you,” Walther explains. “It’s the exact same thing with cameras — the best camera is the one you have with you. And the best audio recording tool is the one you have with you.”
That is, when you want to record, the easiest thing to do is not to get out your laptop and connect a bunch of cables to it, then load up your studio software — it’s to hit the record button on your iPhone.
The Tape It app allows you to do just that, but adds other features that make it more competitive with its built-in competition, Voice Memos.
When you record using Tape It, the app leverages AI to automatically detect the instrument, then annotate the recording with a visual indication to make those recordings easier to find by looking for the colorful icon. Musicians can also add their own markers to the files right when they record them, then add notes and photos to remind themselves of other details. This can be useful when reviewing the recordings later on, Walther says.
Image Credits: Tape It
“If I have a nice guitar sound, I can just take a picture of the settings on my amplifier, and I have them. This is something musicians do all the time,” he notes. “It’s the easiest way to re-create that sound.”
Another novel, but simple, change in Tape It is it that breaks longer recordings into multiple lines, similar to a paragraph of text. The team calls this the “Time Paragraph,” and believes it will make listening to longer sessions easier than the default — which is typically a single, horizontally scrollable recording.
Image Credits: Tape It
The app has also been designed so it’s easier to go back to the right part of recordings, thanks to its smart waveforms, in addition to the optional markers and photos. And you can mark recordings as favorites so you can quickly pull up a list of your best ideas and sounds. The app offers full media center integration as well, so you can play back your music whenever you have time.
However, the standout feature is Tape It’s support for “Stereo HD” quality. Here, the app takes advantage of the two microphones on devices like the iPhone XS, XR, and other newer models, then improves the sound using AI technology and other noise reduction techniques, which it’s developed in-house. This feature is part of its $20 per year premium subscription.
Over time, Tape It intends to broaden its use of AI and other IP to improve the sound quality further. It also plans to introduce collaborative features and support for importing and exporting recordings into professional studio software. This could eventually place Tape It into the same market that SoundCloud had initially chased before it shifted its focus to becoming more of a consumer-facing service.
But first, Tape It wants to nail the single-user workflow before adding on more sharing features.
“We decided that it’s so important to make sure it’s useful, even just for you. The stuff that you can collaborate on — if you don’t like using it yourself, you’re not going to use it,” Walther says.
Tape It’s team of three is based in Stockholm and Berlin and is currently bootstrapping.
The app itself is a free download on iOS and will later support desktop users on Mac and Windows. An Android version is not planned.
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Meet retail’s new sustainability strategy: Personalization
We have been raised to believe in recycling, but it has mostly been a sham — only 9% of all plastic waste produced in 2018 was recycled. The beauty industry produces over 120 billion units of packaging every year, little of which is recycled. Globally, an estimated 92 million tons of textile waste ends up in landfills.
Reducing waste is key to meeting environmental milestones, and some retail firms have narrowed in on a unique approach to minimize what their customers throw away: personalization. Accurate personalization can guide consumers to the right products, reducing waste while increasing conversion and loyalty.
Reducing waste is key to meeting environmental milestones, and some retail firms have narrowed in on a unique approach to minimize what their customers throw away: personalization.
For big brands and retailers, personalization is expected to be the top category for tech investment this year. Moreover, personalization holds high appeal, with 80% of survey respondents indicating they are more likely to do business with a company if it offers personalized experiences and 90% indicating that they find personalization appealing, according to a survey by Epsilon.
Startups that deliver sustainable personalization solutions that also improve business for retailers and brands fall into three categories:
- AR virtual try-on with shade matching.
- Advanced virtual fitting rooms with VR/AR for fashion.
- Smart packaging with IoT and distributed ledger technology.
AR virtual try-on with shade matching
Faces are easy to map, since it’s not difficult to virtually place a lipstick color on a face, but using AR and AI to recommend skin-tone-matching makeup products has been challenging for many AR virtual try-on companies. “I’ve been searching for an intuitive foundation-shade-finder tool since launching Cult Beauty in 2008, and nothing has lived up to the experience of having a professional match you in daylight until I discovered MIME,” says Alexia Inge, founder of Cult Beauty. “There are so many variables like light, skin tones, prevalent undertones, device, screen, OS, formula density, formula oxidation, as well as preferences for coverage levels, finish, brand and skin type,” she says.
MIME founder and CEO Christopher Merkle said, “Virtual try-on has exploded in the past few years, but for color cosmetics, the technology doesn’t help solve the primary customer pain point: shade matching. From day one, I decided to focus our company’s R&D efforts exclusively on color accuracy. I want to make sure that when the consumer receives their foundation or concealer in the mail, it’s the perfect shade once applied to their skin.”
MIME’s Shade Finder AI allows consumers to take a photo of themselves, answer a few questions, then get matched with a makeup color that pairs with their skin tone. MIME helps retailers and brands increase their online and in-store purchase conversion by up to five times. More than 22% of beauty returns are due to poor customer color purchases, but Merkle says MIME can get returns as low as 0.1%.
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AI as a service to solve your business problems? Guess again
SaaS, PaaS – and now AIaaS: Entrepreneurial, forward-thinking companies will attempt to provide customers of all types with artificial intelligence-powered plug-and-play solutions for myriad business problems.
Industries of all types are embracing off-the-shelf AI solutions. According to industry experts, global AI software revenue — most of it online artificial intelligence as a service software (AIaaS) — is set to grow by an astounding annual rate of 34.9%, with the market reaching over $100 billion by 2025. It sounds like a great idea, but there is a caveat — “one-size-fits-all” syndrome.
Companies seeking to use AI as a differentiating technology in order to gain business advantages — and not merely doing it because that’s what everyone else is doing — require planning and strategy, and that almost always means a customized solution.
In the words of Sepp Hochreiter (inventor of LSTM, one of the world’s most famous and successful AI algorithms), “the ideal combination for the best time to market and lowest risk for your AI projects is to slowly build a team and use external proven experts as well. No one can hire the best talent quickly, and even worse, you cannot even judge the quality during hiring but will only find out years later.”
That’s a far cry from what most online off-the-shelf AI services offer today. The artificial intelligence technology offered by AIaaS comes in two flavors — and the predominant one is a very basic AI system that claims to provide a “one-size-fits-all” solution for all businesses. Modules offered by AI service providers are meant to be applied, as-is, to anything from organizing a stockroom to optimizing a customer database to preventing anomalies in production of a multitude of products.
There are several companies that claim to provide AIaaS for automated industrial production. Most of the successful data presented by these providers is based on individual case studies, with problems involving limited data sets and limited, generic objectives. But generic AI solutions are going to produce generic results.
For example, the process to train algorithms to detect wear and tear would be different for factories that produce different products; after all, a shoe is not a smartphone is not a bicycle. Thus, for “real” AI work — where intelligent modules actually managed and changed production in response to environmental and other factors — the companies developed customized solutions for their clients.
Many customers who were “burned” by bad experience with AIaaS will be more hesitant to try it again, feeling it is a waste of time. And use cases that did require heavier AI processing did not yield the results expected — or promised. Some have even accused the cloud companies of deliberately misleading customers — giving them the impression that off-the-shelf AI is a viable solution, when they know very well that it isn’t. And if a technology doesn’t work enough times, chances are that those who could potentially benefit from real AI solutions will give up before they even start.
The objective is to standardize a solution that performs well almost immediately and does not require extensive know-how. AIaaS’ success so far has been in enabling researchers to run complex experiments without requiring the services of an entire IT team to figure out how to manage the necessary infrastructure.
In the future, AIaaS will hopefully enable individuals who are not AI experts to utilize the system to get the desired results. That said, online automated AI services even at their current levels can greatly benefit industrial production — if it is done right.
AI properly done could provide great benefits for industry. Instead of giving up on AI, companies should do a deep dive on the AI services they are thinking of utilizing. Does the solution provide for customization? What kind of support does the service provide? How is the algorithm trained to handle data specific to your use case? These are the questions that companies need to ask when shopping around for AI services. Providers that can furnish substantial answers — and back up their claims with real data on success rates — are the ones companies should work with.
Like all new developments that enhance business activity, AI applications require a high level of expertise. The engineers who work for the big cloud companies indeed have that expertise — which means that they could be providing much more value for customers by helping them develop customized solutions. Whether that can be done “as a service” needs to be examined — but the system in place right now is not the answer.
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Extra Crunch roundup: Options pool rules, voice tech hurdles, keeping employees engaged
“In today’s cash-rich environment, options are more valuable than cash,” says Allen Miller, a principal at Oak HC/FT. “In turn, managing your option pool may be the most effective action you can take to ensure you can recruit and retain talent.”
In an article squarely aimed at early-stage founders, Miller shares best practices for protecting your option pool, lists the mistakes many founders make and offers multiple tips for course-correcting “if you made mistakes early on.”
As we’re just returning from the Labor Day holiday, today’s newsletter is quite brief. We have much more planned for this week, so thanks very much for reading.
Walter Thompson
Senior Editor, TechCrunch
@yourprotagonist
Full Extra Crunch articles are only available to members.
Use discount code ECFriday to save 20% off a one- or two-year subscription.
To commercialize, voice tech must first solve its ‘cocktail party problem’
Image Credits: Karnet / Getty Images
Voice and speech recognition is expected to be a $26.8 billion global market by 2025, but there’s still a long way to go before voice can be fully commercialized.
Developers are deploying natural language processing and conversational AI to overcome current limitations, but “solving these problems requires voice tech to meet the human standard for voice and match the complexities of the human auditory system.”
How engaged are your employees?
Image Credits: katleho Seisa (opens in a new window) / Getty Images
According to a recent survey, more than 70% of workers are actively hunting for a new job or are giving the matter serious consideration.
In a startup environment, employee development takes a back seat to priorities like scaling growth. As a result, few managers have any experience or interest in helping employees acquire new skills or advance their careers.
Don’t wait to be blindsided: Put an action plan in place to assess employee engagement. Remember, seven out of the next 10 people you see on a video call might be polishing their resumes.
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Virtual meeting platform Vowel raises $13.5M, aims to cure meeting fatigue
Meetings are an inevitable part of the work day, but as workplaces became more distributed over the past 18 months, Vowel CEO Andy Berman says we are steadily moving toward “death by meeting.”
His virtual meeting platform is the latest to receive venture capital funding — $13.5 million — with the goal of making meetings more useful before, during and after.
Vowel is launching a meeting operating system with tools like real-time transcription; integrated agendas, notes and action items; meeting analytics; and searchable, on-demand recordings of meetings. The company has a freemium business model and will also be rolling out a business plan this fall for $16 per user per month. Extra features will include advanced integrations, security and admin controls.
The Series A was led by David Hornik of Lobby Capital, who was joined by existing investors Amity Ventures and Box Group and a group of individual investors, including Calendly CEO Tope Awotona, Intercom co-founder Des Traynor, Slack VP Ethan Eismann, former Yammer executive Viviana Faga, former InVision president David Fraga and Okta co-founder Frederic Kerrest.
Prior to starting Vowel, Berman was one of the founders of baby monitor company Nanit. The company had teams spread out around the world, and communication was tough as a result. In 2018, the company went looking for a tool that would work for synchronous and asynchronous meetings, but there were still a lot of time zones to manage, he said.
Taking a cue from Nanit’s own baby monitors that were streaming video over 17 hours a day, the idea for Vowel was born, and the company began to focus on the hypothesis that distributed work would be prevalent.
“People initially thought we were crazy, but then the pandemic hit, and everyone was learning how to work remotely,” Berman told TechCrunch. “As we now go back to hybrid work, we see this as an opportunity.”
In 2017, Harvard Business Review reported that executives spent 23 hours in meetings each week. Berman now estimates that the average worker spends half of their time each week in meetings.
Vowel is out to bring Slack, Figma and GitHub components to meetings by recording audio and video that can be paused at any time. Users can add notes and see where those notes fall within a real-time transcription that enables people who arrive late or could not make the meeting to catch up easily. After meetings are over, they can be shared, and Vowel has a search function so that users can go back and see where a particular person or topic was discussed.
The new funding will enable the company to grow its team in product, design and engineering. Vowel plans to hire up to 30 new people over the next year. The company recently closed its beta test and has amassed a 10,000-person waitlist. The public launch will happen in the fall, Berman said.
Workplace productivity and office communication tools are not new concepts, but as Berman explained, became increasingly important when homes became offices over the past 18 months.
Competitors took different approaches to solving these problems: focusing on video conferencing or audio or meeting management with plugins. Berman says an area where many have not succeeded yet is integrating meetings into the typical workflow. That’s where Vowel comes in with its “meeting OS,” he added.
“Our goal is to make meetings more inclusive and worthwhile, which includes the prep, the meeting and the follow-up,” Berman said. “We see the future will be about knowledge management, so the difference between what we are doing is ensuring you can catch up quickly and keep that knowledge base. A Garner report said that 75% of workplace meetings will be recorded by 2025, and that is a trend we are reinventing from the ground up.”
David Hornik, founding partner at Lobby Capital, said he became acquainted with Vowel from its existing investor Amity Ventures. Hornik, who sits on the GitLab board, said GitLab was one of the largest distributed companies in the tech space, prior to the pandemic, and saw first-hand the challenge of making distributed teams functionable.
When Hornik heard about Vowel, he said he “jumped quickly” on the opportunity. His firm typically invests in platform businesses that have the capacity to transform business spaces. Many are pure software, like Splunk or GitLab, while others are akin to Bill.com, which transformed how small businesses manage financial operations, he added.
All of those combine into a company, like Vowel, especially given the company’s vision for a meeting OS to transform a meeting space that hadn’t moved forward in decades, he said.
“This was quickly obvious to me because my day is meetings — an eight-Zoom day is a normal day — I just wish I could remember everything,” Hornik said. “Speaking with early customers using the product, when I asked them what they would do if this ever went away, the first thing they said was ‘cry,’ and, because there was no alternative, would return to Zoom or other tools, but it would be a big setback.”
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