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We're a computer vision company, we do a lot of product detection + recognition + search, primarily for retailers, but we've also got revenue in other verticals with large volumes of imagery. My co-founder and I both did our thesis' on computer vision.

In our space, the recent AI / ML advances have made things possible that were simply not realistic before.

That being said, the hype around Deep Learning is getting pretty bad. Several of our competitors have gone out of business (even though they were using the magic of Deep Learning). For example, JustVisual went under a couple of months ago ($20M+ raised) and Slyce ($50M+ raised) is apparently being sold for pennies on the dollar later this month.

Yes, Deep Learning has made some very fundamental advances, but that doesn't mean it's going to make money just as magically!


There's a lot of "DL allows us to do X so we should make a product / service using DL to do X", rather than "We think there's value in something doing Y, what allows us to do Y? <research> DL allows us to do Y better than anything else, lets use DL"

You gave the example of Slyce. Their products are cool, but I can't help but think "is DL the best way to get that end result?" for lots of the things they do.

Can you expand more on "we do a lot of product detection + recognition + search, primarily for retailers" please? Is that something like identifying products in social media images or something?

We have several products, each of which serves different departments within retailers.

The exact things we do depends entirely on which department(s) are licensing it. Basically, anywhere there's a product image (from their own inventory to mobile to social) and we can provide some kind of help, we do. Every department needs totally different things, so it varies quite a bit...but it's all leveraging our core automated detection + recognition + search APIs.

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