Adeptmind raises $4.5M from Fidelity to bring smarter search to retailers

Matthew Lynley
If you've ever searched for a product on any website that's not Amazon or Google, you've probably had a bad time trying to find something — and then go straight back to Google or Amazon.

If you've ever searched for a product on any website that's not Amazon or Google, you've probably had a bad time trying to find something — and then go straight back to Google or Amazon.

That's a significant problem for retailers, which need to ensure that potential customers that are already signaling a lot of interest in buying something will actually be able to find those products and end up buying them. That's why G Wu and Artzi started Adeptmind, a tool that gives retailers a way to implement a smarter search engine on their sites by collecting related data to all of their products and zero in on what customers are actually looking for. To do that, Adeptmind said that it has raised $4.5 million in a financing round from Fidelity.

"A lot of times NLP companies will have fairly 'comprehensive' knowledge graphs where you do internal labeling, but most of the data comes from the product catalog," Wu, the CEO, said. "As such anything not in the product catalog will not be understood. There's no free lunch when it comes to machine learning. We target crawl a large portion of the web. Based on the web we do targeted crawling so any relevant information we ingest."

Here's an example they gave: when searching for "ripped jeans" on a website like Diesel, you might not end up with the right results and a lot of regular jeans because they're just not recognizing the "ripped" modifier is something that's meant to exclude results. Adeptmind crawls around the internet in various places, such as even forums, to determine what products various potential customers are cross-referencing when related to the phrase "ripped jeans" and then uses that to narrow down the list of products to what customers actually want.

Those queries, as a result, can theoretically get as complicated as the ones you might rattle off to a service like Hound or Siri just to test the limits of its capabilities. You might go to some kind of a jacket website and stretch the search out to an extremely narrow subset of products and demographics, and Adeptmind's pitch is that it'll still be able to turn up the proper results based on its efforts to build a language graph around products that's more robust than just keyword search.

That's the pitch for the company when they walk into an office and try to sell into larger businesses, where you have to be able to pull out a laptop and show that the technology actually works. The goal, eventually, would be to be able to offer retailers the way to simply say "give me a search engine" and plug directly into Adeptmind right away as it begins chugging away at building a language graph around those products.

To be sure, it's not entirely clear that major retailers would end up buying into this, especially after they've negatively trained consumers to just pop over to Google or Amazon to find a product because of poor janky search engines. It's an uphill battle, and because the data is grabbed from around the web, there may be other companies that look to build a similar kind of language graph around products that they could sell into retailers. The goal for Adeptmind, Artzi said, is to just convince those retailers that the unsupervised nature of the product will end up giving them the best results — and, also, that they're first to get into those retailers.

"A lot of times NLP services tend to be consulting in nature," Artzi said. "You build out a system with people spending three or four months, and then you have to do another store and spend another three or four months. Eventually, you're bounded by linear growth. You don't have to spend a lot of effort if your system is able to support them through unsupervised learning. We ingest the catalog and get to very high accuracy very quickly. That was harder to do pre-deep learning, so we're catching the front end of deep learning and NLP."

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