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Read AI, a startup creating an AI-powered platform for meeting metrics, today emerged from stealth with $10 million in seed funding from Madrona Venture Group with participation from PSL Ventures and person investors. With the funding, cofounder and CEO David Shim says that Read will be capable to make its service offered for free of charge on Zoom and by way of Zoom’s app marketplace, as nicely as by means of a Google Calendar integration.
Video calls have turn out to be a bigger aspect of day-to-day life compared with just before the pandemic. For instance, in the previous 12 months, Zoom has seen a 3,000% enhance in meeting minutes, and the top rated 4 video conferencing platforms have crossed more than 500 million day-to-day active participants combined.
But virtual meetings are not best. For instance, when broadcasting to a massive group of persons, presenters can typically only see thumbnails of a couple of participants off to the side of their deck, requiring them to devote time scrolling to see participants’ reactions. Beyond this, when it comes to recurring meetings, priorities can shift more than time, producing the similar hour-extended sync no longer a excellent use of the workday for some attendees.
“We’ve all been in that meeting, where we know it is about to go downhill, yet we hesitate on taking any action as we might have the wrong read. The current feedback mechanism is an emoji or a message in chat that more often distracts than improves the conversation,” Shim, previously CEO at Foursquare, told VentureBeat by way of e mail. “Read is ‘feedback 2.0,’ where in real time, all meeting attendees have a shared dashboard that encourages collaboration by validating intuition with data.”
Read was founded in 2020 by Shim, who teamed up with longtime colleagues Rob Williams and Elliott Waldron to construct the prototype. The 3 previously launched place analytics organization Placed, which was acquired by Snapchat in 2017 and spun out into Foursquare in 2019.
Read leverages AI, personal computer vision, and organic language processing models to energy its meeting analytics backend. The models, which Shim says had been educated on an “internationally and demographically diverse set of training examples,” provide actual-time sentiment, engagement and participation metrics encouraging collaboration amongst attendees, and aggregate metrics across meetings.
A host of startups plug into meeting platforms to analyze the content with AI. For instance, Fireflies recognizes concerns, assigned tasks, key subjects, and basic sentiment in meetings. Kronologic is creating what it describes as “calendar monetization” tools, also powered by AI. As for Read, Williams says it can be “a second set of eyes and ears,” seeing an whole audience in a meeting and supplying aggregate reactions in actual time.
“Read provides that longitudinal awareness and can help … [identify] where engagement and sentiment drop-off, allowing [companies] to adjust the content or attendee list improving both operational efficiency as well as morale,” Williams stated by way of e mail. “In an enterprise sales setting, Read augments an account executive’s ability to read the room. [The product] provides real-time feedback on if the pitch is resonating, if the audience is interested in the new features, and if the pricing is well received. This type of feedback is not only beneficial to the executive, but to the prospect as it allows the seller to make adjustments to deliver a more productive meeting in real time.”
While ostensibly benign, Read’s tracking capabilities could not sit nicely with all personnel, a lot of of whom have had to contend with elevated workplace surveillance in current years. A 2018 Gartner survey located that 17% of organizations are monitoring work personal computer usage, whilst 16% are tracking Microsoft Outlook and calendar appointments. Moreover, even although 62% of executives say that their organizations are applying new technologies to gather information on personnel, fewer than a third say that they really feel confident that they’re applying the information responsibly, according to a current survey by Accenture.
There’s also the possible for AI bias to skew the meeting metrics. While Shim insists that Read’s models had been educated on a sufficiently diverse dataset, even tech giants like Google and Facebook have historically deployed flawed models into production. For instance, Zoom’s virtual backgrounds and Twitter’s automatic photo-cropping tool have been shown to disfavor persons with darker-colored skin. And some specialists argue that accurately assessing a person’s sentiment by analyzing their face is premised on shaky proof.
But Shim insists that Read’s platform is “built on the foundation of privacy and transparency.” In meetings, Read notifies attendees that it is listening in on the conversation and supplies access to meeting metrics for all attendees. Read also gives an opt-out function to participants in the meeting and does not provide meeting playback in the type of recorded or transcribed conversations.
“The future of meeting analytics isn’t recordings and transcripts. Rather, it’s real time, understanding how the meeting is going and what are the moments that make or break an interaction,” Shim added.
Read anticipates doubling the size of its group to more than 30 persons in the next six months.