Sep
17
2021
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Defy Partners leads $3M round into sales intelligence platform Aircover

Aircover raised $3 million in seed funding to continue developing its real-time sales intelligence platform.

Defy Partners led the round with participation from Firebolt Ventures, Flex Capital, Ridge Ventures and a group of angel investors.

The company, headquartered in the Bay Area, aims to give sales teams insights relevant to closing the sale as they are meeting with customers. Aircover’s conversational AI software integrates with Zoom and automates parts of the sales process to lead to more effective conversations.

“One of the goals of launching the Zoom SDK was to provide developers with the tools they need to create valuable and engaging experiences for our mutual customers and integrations ecosystem,” said Zoom’s CTO Brendan Ittelson via email. “Aircover’s focus on building sales intelligence directly into the meeting, to guide customer-facing teams through the entire sales cycle, is the type of innovation we had envisioned when we set out to create a broader platform.”

Aircover’s founding team of Andrew Levy, Alex Young and Andrew’s brother David Levy worked together at Apteligent, a company co-founded and led by Andrew Levy, that was sold to VMware in 2017.

Chatting about pain points on the sales process over the years, Levy said it felt like the solution was always training the sales team more. However, by the time everyone was trained, that information would largely be out-of-date.

Instead, they created Aircover to be a software tool on top of video conferencing that performs real-time transcription of the conversation and then analysis to put the right content in front of the sales person at the right time based on customer issues and questions. This means that another sales expert doesn’t need to be pulled in or an additional call scheduled to provide answers to questions.

“We are anticipating that knowledge and parsing it out at key moments to provide more leverage to subject matter experts,” Andrew Levy told TechCrunch. “It’s like a sales assistant coming in to handle any issue.”

He considers Aircover in a similar realm with other sales team solutions, like Chorus.ai, which was recently scooped up by ZoomInfo, and Gong, but sees his company carving out space in real-time meeting experiences. Other tools also record the meetings, but to be reviewed after the call is completed.

“That can’t change the outcome of the sale, which is what we are trying to do,” Levy added.

The new funding will be used for product development. Levy intends to double his small engineering team by the end of the month.

He calls what Aircover is doing a “large interesting problem we are solving that requires some difficult technology because it is real time,” which is why the company was eager to partner with Bob Rosin, partner at Defy Partners, who joins Aircover’s board of directors as part of the investment.

Rosin joined Defy in 2020 after working on the leadership teams of Stripe, LinkedIn and Skype. He said sales and customer teams need tools in the moment, and while some are useful in retrospect, people want them to be live, in front of the customer.

“In the early days, tools helped before and after, but in the moment when they need the most help, we are not seeing many doing it,” Rosin added. “Aircover has come up with the complete solution.”

 

Aug
13
2021
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ThirdAI raises $6M to democratize AI to any hardware

Houston-based ThirdAI, a company building tools to speed up deep learning technology without the need for specialized hardware like graphics processing units, brought in $6 million in seed funding.

Neotribe Ventures, Cervin Ventures and Firebolt Ventures co-led the investment, which will be used to hire additional employees and invest in computing resources, Anshumali Shrivastava, Third AI co-founder and CEO, told TechCrunch.

Shrivastava, who has a mathematics background, was always interested in artificial intelligence and machine learning, especially rethinking how AI could be developed in a more efficient manner. It was when he was at Rice University that he looked into how to make that work for deep learning. He started ThirdAI in April with some Rice graduate students.

ThirdAI’s technology is designed to be “a smarter approach to deep learning,” using its algorithm and software innovations to make general-purpose central processing units (CPU) faster than graphics processing units for training large neural networks, Shrivastava said. Companies abandoned CPUs years ago in favor of graphics processing units that could more quickly render high-resolution images and video concurrently. The downside is that there is not much memory in graphics processing units, and users often hit a bottleneck while trying to develop AI, he added.

“When we looked at the landscape of deep learning, we saw that much of the technology was from the 1980s, and a majority of the market, some 80%, were using graphics processing units, but were investing in expensive hardware and expensive engineers and then waiting for the magic of AI to happen,” he said.

He and his team looked at how AI was likely to be developed in the future and wanted to create a cost-saving alternative to graphics processing units. Their algorithm, “sub-linear deep learning engine,” instead uses CPUs that don’t require specialized acceleration hardware.

Swaroop “Kittu” Kolluri, founder and managing partner at Neotribe, said this type of technology is still early. Current methods are laborious, expensive and slow, and for example, if a company is running language models that require more memory, it will run into problems, he added.

“That’s where ThirdAI comes in, where you can have your cake and eat it, too,” Kolluri said. “It is also why we wanted to invest. It is not just the computing, but the memory, and ThirdAI will enable anyone to do it, which is going to be a game changer. As technology around deep learning starts to get more sophisticated, there is no limit to what is possible.”

AI is already at a stage where it has the capability to solve some of the hardest problems, like those in healthcare and seismic processing, but he notes there is also a question about climate implications of running AI models.

“Training deep learning models can be more expensive than having five cars in a lifetime,” Shrivastava said. “As we move on to scale AI, we need to think about those.”

 

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