Superfish Aims to Dominate Visual Search, One Product at a Time
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the algorithms—and combinations of algorithms—that would allow them to accurately search for photos. Though machine-vision technology was a huge piece of the puzzle, the team pulled algorithms from various sectors, including DNA search. “Today the visual search engine is a very complex machine,” Pinhas says. “We have many algorithms that are running in parallel to find similar content. It’s not just, ‘what is the big secret or the one algorithm?’”
Starting from scratch takes time, but it also takes a lot of money. Superfish didn’t release its first product— WindowShopper— until two and half years ago, five years after the company was founded. “It’s not the kind of thing where you create a company, and six months later you release a product,” he says. “Any company that thought, ‘In two years we will have something,’ two years later they understood it’s too complex. We weren’t even close to solving this two years in.” Spending five years developing a core technology requires a lot of money and very patient investors.
Luckily for Pinhas, he found some in Draper Fisher Jurvetson (DFJ), a VC firm that invests in early-stage technologies, and has funded companies like Skype, SpaceX, and Tesla. “We had a rough demo, and they understood it’s going to take time and require a lot of money, but it was interesting to them and they agreed something like this could be huge,” says Pinhas.
To date, Superfish has raised about $20 million, mostly from DFJ and Vintage Investment Partners, with some smaller investors participating in the seed round.
The company became profitable about a year and a half ago, thanks to WindowShopper, which currently has 100 million monthly users, and a high conversion to sale rate for soft goods, which include non-hardware items, like clothing jewelry and furniture. When consumers buy something through WindowShopper, Superfish gets paid affiliate fees, which vary based on type of good and country of origin, somewhere between two and 20 percent. On media items, like DVDs, for example, the rate is around two percent, whereas luxury items like watches will score the company around 20 percent.
Superfish isn’t Pinhas’s first company. In 1998, he founded Vigilant Technology, which developed servers that collected video from surveillance cameras. Though the company used some similar technology, it solved a very different problem—storing video for two to eight weeks and helping the monitoring team find information like license plate numbers —and counted big institutions like airports, city centers, casinos, and the New York City Police Department among its customers. For Vigilant, a typical product developed for a particular institution collected video from 3,000 cameras.
Though the user base and core technology behind the two companies was very different—and a lot had changed in machine learning since 1998—the new company seemed like a natural pivot to Pinhas, who has a master’s in machine learning. It was a good fit for his Superfish co-founder as well; Pinhas met Michael Chertok when he came to work at Vigilant.
Superfish has leapt several big hurdles. The company has developed a core technology that works. It has used that technology to create products that are now bringing enough revenues to turn a profit. But despite these successes, Superfish still finds that there are more obstacles in its path. Pinhas’s current challenge is convincing potential partners like Samsung that it makes sense to add image search within a connected camera. After all, search functions bring ad revenue, and the more accessible they make it, the more people will use it.
Pinhas believes such image searches will be an indispensable part of the future. “Imagine scenario where you order a dessert, take a picture, and get the recipe,” Pinhas says. “Or you can find which friend uploaded similar dessert pictures to Facebook.” Or you take a picture of your kids in a park and find similar play structures in your area. In his mind, these capabilities aren’t far off. “In two to three years, visual search is probably going to be everywhere,” he says. “Part of every camera, part of every browser. You’re going to be able to search and find similar content in every category, across every type of image.”
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