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 to Nara Logics.
Jana Eggers: I’m the CEO of Nara Logics. I’m a mathematician and a computer scientist. I started in research and really enjoyed the business side of technology and science. I went into technology—most of the time, in emerging tech. That was my career. Nara and I found each other, I would say, because I was looking for an opportunity that married science and business. That was what I was excited about when I spoke with the team about the work they are doing in artificial intelligence, but also the neural science that’s behind the idea.
Our Co-founder Nathan Wilson has a background in Neuroscience and Computer Science. He was a neuroscientist at MIT and, specifically worked on neurons and how they connect to each other and transmit information. What we are doing at Nara is applying some of those ideas of how neurons connect to data, how data connects, and how connecting data will help us find the best matches for a decision.
A quick example is one that everybody knows about. It’s personalisation in retail through recommendations saying, “If you like this, you might also like this.” We can certainly do suggestions, and we have shown some very impressive results to our customers. At the core, it is a matching problem. We can do that type of matching but we can also match airplanes to gates. On the pharmaceutical side, matching discovered drugs to different diseases or issues that they weren’t researching for but might also apply because of some of the same medication. It’s pretty wide reaching. It’s very focused on a matching problem but pretty wide reaching in the use cases.
Sramana Mitra: How is your company positioned? What customers are you catering to? Is it the retail recommendation segment that you’re positioned to address?
Jana Eggers: That’s such a great question. Obviously there’s a lot of applications in retail and I know you have that background too. I also run a customizable clothing company. We both think in that way. Retail is one of those spaces, but it’s really across industries. We get asked a lot, “Aren’t you focusing on one area?” What we’re finding is we are focused on finding these matching problems. We are really good at talking to folks whether it’s in Oil & Gas, Intelligence, Pharmaceuticals, or Healthcare.
When we talk to them about the problems they’re trying to solve right now, we quickly identify a matching problem. What we’re trying to do is match doctors and patients, for example, on the healthcare side. In Oil & Gas, one of the challenges they have is they have a lot of documentation, as you can imagine, as they’re trying to discover new places to find oil and gas. With all of the documentations, they can have hundreds and thousands of documents. People are searching for information that applies to their context and who they are and really personalising that result too. Again, it’s really about matching. It’s about matching information that we have across an organisation to a specific context.