Sramana Mitra: I have a question in that context. There’s a lot of processing going on midstream of traffic coming in. Is it all happening in real time? How do you deal with delays and latencies? Amir Husain: First of all, we’re not blocking things until the final answer arrives. In other words, we’re not
Jimi Crawford: Another use case that we’ve been working on is simply counting cars. It seems simple. It’s not actually all that simple because the satellite images have relatively low resolution—about a meter or so per pixel. There’s not very many pixels. We’ve been able to train to actually count cars accurately in parking lots,
Using satellite images to predict trends is Orbital’s unique offering. Read on to see how they do it, and where they are finding applications. Sramana Mitra: Let’s start by introducing our audience to you as well as Orbital. Jimi Crawford: I’m originally a PhD in Artificial Intelligence. I spent the first part of my career doing
Sramana Mitra: We’ve talked in the context of retail. Is there any other finding in other verticals? Jana Eggers: I mentioned healthcare before. A quick example that everyone understands is doctor and patient matching. When someone who just moved has a specific condition that he/she wants a right doctor for, we help them by giving suggestions on
Sramana Mitra: Can you do one more level of double-clicking? You were distinguishing between clustering algorithms versus one-on-one recommendation and treating each individual as an individual. Can you explain how that works technically? Jana Eggers: We take all of the product that you have. Let’s take retail. You may have groups of products and offerings. All of those
Sramana Mitra: Where is the bulk of your business coming from? Is there a sector that dominates in your success? Jana Eggers: It’s the large enterprises. We’re talking about the Global 1000. Sramana Mitra: What industry sector? I’m trying to understand which use case is really dominating. Jana Eggers: We aren’t saying that this is
Most of the Artificial Intelligence recommendation engines are based on clustering algorithms and a limited number of parameters. Nara Logics is pushing the envelope on releasing both constraints, and going to a more granular level of personalisation. Read on to learn more. Sramana Mitra: Let’s start by introducing our audience to yourself as well as
Sramana Mitra: I wanted to ask you about location and where you’re building your team. Where is the bulk of the organisation? Dave Copps: We’re in Dallas, Texas. Sramana Mitra: Everything is in Dallas, Texas? Dave Copps: We do have satellite offices. We have people in San Francisco, DC, New York, and London. Sramana Mitra: