Personal blog of Yzmir Ramirez

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 However, by the time everyone was trained, that information would largely be out-of-date.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.”

WhyLabs, a new machine learning startup that was spun out of the Allen Institute, is coming out of stealth today. Founded by a group of former Amazon machine learning engineers, Alessya Visnjic, Sam Gracie and Andy Dang, together with Madrona Venture Group principal Maria Karaivanova, WhyLabs’ focus is on ML operations after models have been trained — not on building those models from the ground up.

The team also today announced that it has raised a $4 million seed funding round from Madrona Venture Group, Bezos Expeditions, Defy Partners and Ascend VC.

Visnjic, the company’s CEO, used to work on Amazon’s demand forecasting model.

“The team was all research scientists, and I was the only engineer who had kind of tier-one operating experience,” she told me. “So I thought, “Okay, how bad could it be? I carried the pager for the retail website before. But it was one of the first AI deployments that we’d done at Amazon at scale. The pager duty was extra fun because there were no real tools. So when things would go wrong — like we’d order way too many black socks out of the blue — it was a lot of manual effort to figure out why issues were happening.”

Image Credits: WhyLabs

But while large companies like Amazon have built their own internal tools to help their data scientists and AI practitioners operate their AI systems, most enterprises continue to struggle with this — and a lot of AI projects simply fail and never make it into production. “We believe that one of the big reasons that happens is because of the operating process that remains super manual,” Visnjic said. “So at WhyLabs, we’re building the tools to address that — specifically to monitor and track data quality and alert — you can think of it as Datadog for AI applications.”

The team has brought ambitions, but to get started, it is focusing on observability. The team is building — and open-sourcing — a new tool for continuously logging what’s happening in the AI system, using a low-overhead agent. That platform-agnostic system, dubbed WhyLogs, is meant to help practitioners understand the data that moves through the AI/ML pipeline.

For a lot of businesses, Visnjic noted, the amount of data that flows through these systems is so large that it doesn’t make sense for them to keep “lots of big haystacks with possibly some needles in there for some investigation to come in the future.” So what they do instead is just discard all of this. With its data logging solution, WhyLabs aims to give these companies the tools to investigate their data and find issues right at the start of the pipeline.

Image Credits: WhyLabs

According to Karaivanova, the company doesn’t have paying customers yet, but it is working on a number of proofs of concepts. Among those users is Zulily, which is also a design partner for the company. The company is going after mid-size enterprises for the time being, but as Karaivanova noted, to hit the sweet spot for the company, a customer needs to have an established data science team with 10 to 15 ML practitioners. While the team is still figuring out its pricing model, it’ll likely be a volume-based approach, Karaivanova said.

“We love to invest in great founding teams who have built solutions at scale inside cutting-edge companies, who can then bring products to the broader market at the right time. The WhyLabs team are practitioners building for practitioners. They have intimate, first-hand knowledge of the challenges facing AI builders from their years at Amazon and are putting that experience and insight to work for their customers,” said Tim Porter, managing director at Madrona. “We couldn’t be more excited to invest in WhyLabs and partner with them to bring cross-platform model reliability and observability to this exploding category of MLOps.”

Searchable.ai wants to solve an old problem around search in the enterprise. The stealthy startup announced a $2 million seed round.

Defy Partners led the round with a slew of other participants, including Paul English, co-founder of Kayak; Wayne Chang, co-founder of Crashlytics; Brian Halligan, co-founder and CEO of HubSpot; Jonathan Kraft, president and COO of the Kraft Group and the New England Patriots; MIT Prof. Edward Roberts; Eric Dobkin, founder and chairman emeritus of Goldman Sachs Global Equity Capital Markets; and Susquehanna International Group.

The prestigious group of investors saw that Searchable.ai is trying to solve a big problem around findability. Company co-founder Brian Shin says that knowledge workers have been struggling for years trying to find a way to better utilize all of the information that exists within an organization.

“The problem we’re really solving is that there are a trillion documents created every year in Microsoft Office, Google Docs, etc., and it’s really difficult if you’re a knowledge worker to find what you need in terms of either a document, an asset like a slide or worksheet within a document or the actual answer to a question that you have,” Shin said.

The questioning part could be particularly valuable because it lets you ask a natural language question and find a specific piece of information within a document, rather than just the document itself. “Let’s say you have a giant spreadsheet, you could actually ask a question of all your spreadsheets and find the atomic unit of knowledge that you’re actually looking for,” he said.

The product itself is not quite ready for the big reveal, but if it works as described, it will be a huge boost to knowledge workers who have continually struggled to find a nugget of information they know is out there across the myriad documents in an organization.

Shin is an experienced entrepreneur who has helped launch and sell three companies. He reports he has raised $100 million in venture capital and most recently has worked as a venture capitalist himself, but he saw this opportunity and decided to jump back into the development side of things.

He admits he’s giving up a lot to go back to the startup lifestyle, but he and his co-founders decided this was worth it. “You know the draw, the compulsion to do another startup is is really what this is about. So my three other colleagues and I have have all started companies before and we’re all giving up big jobs to do this, and I’m so excited about the team and the massive opportunity.”

He promised more details about the company and the solution would be coming early next year.