Conntour raises $7M to build an AI search engine for security video systems; plus, Lucid Bots raises $20M for its window-washing drones

0:00This is TechCrunch. This episode is brought to you by Indeed. Stop waiting around for the perfect candidate. Instead, use Indeed Sponsored Jobs to find the right people with the right skills fast. It's a simple way to make sure your listing is the first candidate to see. According to Indeed Data, Sponsored Jobs have four times more applicants than non-sponsored jobs. Go build your dream team today. With Indeed, get a $75 Sponsored Jobs credit at Indeed.com slash podcast, Terms and Conditions Apply. Well, the surveillance tech industry is having its moment in the spotlight, but not really for the best reasons. With controversy around the U.S. Immigration and Customs Enforcement, tapping into Flocks Camera Network to surveil people and Home Camera Maker Ring, drawing criticism for building new features that would enable law enforcement to ask homeowners for footage of their neighborhoods,

1:01there's currently a broad debate around safety, privacy, and who gets to watch whom. As TechCrunch's Ram Eyre writes, controversy doesn't erase markets, though, and the continued improvement of vision-language models has only blown more wind in the sales of companies building new ways to help companies monitor what goes on in their premises. Going to Matan Goldner, co-founder and CEO of Video Surveillance Startup Contour, the ethics around this topic are important enough that he says his company is quite picky about which clients to sell to. That may not come off as sound business, since for a start-up barely two years in, but Goldner says he can afford to do this because contour already has several large government and publicly listed customers, one of which is Singapore's central narcotics bureau. Goldner told TechCrunch in an exclusive interview, the fact that we have such big customers

2:02allows us to select them and to stay in control. We are really in control of who is using it, what is the use case, and we can select what we think is moral, and of course, legal. We use all our judgment, and we make decisions based on specific customers that we're okay to work with because we know how they will use it. That traction has helped contour with more than being selective. Investors have taken note that the start-up recently raised a $7 million seed round from General Catalyst, Y Combinator, SB Angel and Liquid 2 Ventures. Goldner said the round closed within 72 hours. I think I scheduled around 90 meetings in like eight days, and just after three we started on Monday and by Wednesday afternoon, we were done. Regardless contour may be right in being picky, especially given how powerful AI tools in this space have become, the company's own video platform uses AI models to let security

3:02personnel query camera feeds using natural language to find any object, person, or situation in the footage in real time, a Google-like search engine made specifically for security video feeds. It can also monitor and detect threats on its own based on preset rules and surface alerts automatically. Unlike legacy systems that depend on preset definitions or parameters to detect specific objects, motion patterns, or behaviors, contour claims its system uses natural and vision language models, which linted a high degree of flexibility and usability. A user may ask, find instances of someone in sneakers passing a bag in the lobby. And contours system will quickly search all the recorded footage or live video feeds to return relevant results. And because the platform bakes in AI models, users can simply ask questions about the footage and get answers in text accompanied by the relevant video feeds as well as generate

4:05incident reports. The company's selling point, however, is its scalability. NER explained that the platform mainly differs from other AI video search services because it is designed to efficiently scale the systems comprising thousands of camera feeds. In fact, he said, contours system can monitor up to 50 camera feeds off a single consumer GPU like Nvidia's RTX 4090. The company does this by using multiple models and logic systems, and then identifying which models and systems the algorithm should use for each query to require the lowest amount of computing power to give users the best results. Contour claims its system can be deployed fully on premises, completely on the cloud, or a mix of both. It can plug into most security systems already in use, or it can serve as a full surveillance platform on its own. But there's been a long-running problem in the video surveillance industry. The quality of surveillance is only as good as the footage captured. It's hard to make out details from the footage of a poorly lit parking lot that was recorded

5:10by a low-resolution camera with a dirty lens, for example. Goldner says contours hedges for this inevitability by providing a confidence score along with its search results. If the source of a camera feed is not good enough quality, the system will return results with low confidence levels. Going forward, Goldner says the biggest technical problem to solve is bringing the full level of large language model capability to its system, while maintaining its efficiency. He said, we have two things that we want to do at the same time, and they contradict each other. On one hand, we want to provide full natural language flexibility, LLM style, to let you ask anything. And on the other hand, there's efficiency, so we want to make it use very few resources because, again, processing thousands of feeds is just insane. This contradiction is the biggest technical barrier and technical problem in our space, and what we're working really, really hard to solve.

6:14Andrew Asher is the founder and CEO of Window Cleaning Robot startup LucidBots, and he likes to joke that his company is the antithesis of the robotics industry right now. While many companies are trying to build human oids or tout demos of their robots dancing and doing flips, LucidBots' drones are out in the field making traditionally unsexy and dangerous work, like cleaning windows, safer and more efficient. He said, the sad truth is most are still selling a lot of hype and headlines, and we sell performance on the job site that shows up in our customers' profits and losses. We're not just in the lab and simulators, we've got dirt under our fingernails, and we're out on job sites getting work done. The Charlotte, North Carolina-based LucidBots is a full-stack robotics company that sells its Sherpa drones and lava robot to cleaning companies to help them on their job sites. The company designed and manufactures its own robots in the U.S. and just raised a $20

7:17million series B-round co-led by Cupid Capital and Idea Fund Partners. This brings its total funding to $34 million. The company plans to use the money for hiring to keep up with demand, although Asher joked that they've run out of parking spots at their manufacturing facility. Asher said, we have more requests for demos than we have hours in the day, so we need to scale up capacity and headcount. As a founder, when we don't have enough hours in the day to do all the demos, it gives me a little bit of heartburn. Demand from customers and investors wasn't there in the beginning, though. It took the company half a decade to ship its first 100 robots, and it took a fair amount of convincing to get VCs to back a robotics founder with a liberal arts background and no robotics experience. Asher got the original idea for the company while he was a junior at Davidson College studying economics in Spanish. He happened to walk by a building that was being cleaned by window washers.

8:19It was a windy day, and the workers' swing stage started to knock around and slam into the building. Watching that harrowing scene made Asher think about how technology could make that safer. He said, built infrastructure is literally the largest asset class in the world, but right now, we've got these three compounding issues. We've got aging infrastructure, the new infrastructure we're building is getting bigger and harder to maintain, and we have less and less people willing and able to do that work. We needed to start building drones and robots to bridge that gap. LucidBots was launched in 2018, and it started out as a cleaning company that took contract jobs to learn more about the industry. After a couple of years and a few cleaning chemical burns, Asher said they knew what their drone needed to be successful. LucidBots sales has gained momentum recently. It took the startup five years to sell 100 units, and now it is approaching a thousand. The company continues to improve its bots and drones in an effort to keep sales taking

9:22along. Data collected by the robots is fed back to the underlying software, which is used to improve both of LucidBots products. The company is also building a tool that will allow its bots to be used for adjacent categories, like painting, waterproofing, and sealing among others. Asher said, we recently waterproofed a massive university stadium that was starting to age, still using the same brain and frame as a Sherpa. Part of why we went there is because our existing customers were pulling us there, and we were getting, you know, probably 50 or so inbound leads a month related to painting and coding, and that was before we even began marketing that option. That's it from TechCrunch. For more stories like these, go to TechCrunch.com. Ever seen a musical so good you didn't want it to end? You could live inside it forever. Then you're going to love Schmigadun.

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