Jana Eggers is CEO of Nara Logics. 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 personalization.
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