Thought Leaders in Artificial Intelligence: Matthew Sappern, CEO of PeriGen (Part 1)

I have been talking about the applications of AI on Healthcare IT problems. Here is a great case study.

Sramana Mitra: Let’s start by having you introduce yourself as well as PeriGen to our audience.

Matthew Sappern: I’m the CEO of of PeriGen. PeriGen is a software developer. We make software as a medical device. We’re FDA-cleared. Our primary goal is to build software that helps clinicians prevent adverse outcomes in childbirth, which is a pretty important task.

Sramana Mitra: Double-click down on that and explain what exactly are we talking about. Give us a use case and talk us through how this works. >>>

Thought Leaders in Artificial Intelligence: Grant Ingersoll, CTO of Lucidworks (Part 5)

Sramana Mitra: As I’m listening to you, I’m trying to answer the same question in this context. As we go along, the notion of composite search becomes critical.

Grant Ingersoll: Could you define that a little bit? How are you defining it?

Sramana Mitra: Context-specific things that are not just finding the data but really highly personalized, context-specific, actionable search.

Grant Ingersoll: Exactly. That is the goal here. Take working with your own email. There’s this case where you’re in this mode of getting through this. You also often have this mode where you’re searching and looking for related information. You want search and search-related things >>>

Thought Leaders in Artificial Intelligence: Grant Ingersoll, CTO of Lucidworks (Part 4)

Sramana Mitra: What kind of infrastructure are your clients running these days? I imagine you’re going after the larger e-commerce sites because the smaller ones don’t have the infrastructure to run these kinds of stuff.

Grant Ingersoll: We scale pretty nicely with the size of the organization. A good chunk of the top 100 or top 250 retailers in the US are powered off of either our open source software or our commercial product. Many of them are in a migration right now, going from on-premise up to one of the big public cloud environments. We’re starting to see more of this running on Kubernetes and Docker. It really depends on the organizations and where they’re at.

Most of the really large retailers that we support have moved to public cloud. They are now choosing the one that is most competitive for them. In >>>

Thought Leaders in Artificial Intelligence: Grant Ingersoll, CTO of Lucidworks (Part 3)

Sramana Mitra: The nitty gritty of this is very interesting to me. To the audience, it would be very interesting to know to what extent did you achieve this personalization. What I told you earlier about my own experience, where we were doing clothing search, you have many cluster-based personalization opportunities.

For example, size is one of them. Then there is also color, different hair colors, eye colors that match better or worse with different types of clothing. We had all of that in our taxonomy and in our rules. This particular area is very rich for personalization and search. Does this all fall within your purview?

Grant Ingersoll: It definitely does. I would say that big change from what you’re describing is a large majority of that is automated and learn >>>

Thought Leaders in Artificial Intelligence: Grant Ingersoll, CTO of Lucidworks (Part 2)

Grant Ingersoll: In the case of one of our large telecom providers that we power ecommerce for, if a user comes in and searches for iPhone and they’ve never done business with that company, then you want to show them the latest iPhone. If you happen to know that that user is already logged in and they’ve already bought the latest iPhone and the query is iPhone again, the chances are they’re looking for support or accessories. It’s that kind of behavior that machine learning can really help move the needle for with retailers.

The same data that makes for more relevant search is also the same data that makes for recommendations, personalization, and queries. That is all of that user feedback loop. Then these days, a lot of the work done is shoving all of that into tools and essentially creating a much smarter ranking function that then returns those results out to the user. >>>

Thought Leaders in Artificial Intelligence: Grant Ingersoll, CTO of Lucidworks (Part 1)

A fantastic discussion on the future of search, virtual concierges, and so on.

Sramana Mitra: Let’s start by introducing our audience to yourself as well as to the company.

Grant Ingersoll: I’m the CTO and Co-Founder of Lucidworks. I have a background in search, machine learning, and natural language processing. I have been a long-time contributor to the Apache Solar and Apache Lucene search engine projects, which are both open sourced. Then I also wrote a book called Taming Text. We’re focused on solving key problems for people on the customer experience side.

On the retail side, we focus really on how we can help users or consumers, both pre and post sales, to find and engage with companies and their >>>

How an IT Services Startup Wants to Disrupt Itself in the AI Era: Sanjay Jupudi, CEO of Qentelli (Part 3)

Sramana Mitra: How big is the work force?

Sanjay Jupudi: We just touched 250 people.

Sramana Mitra: How many customers are you servicing?

Sanjay Jupudi: About 30 customers. The good part of what we have done is when we go to customers, we are doing a lot of work for customers instead of just touching the surface.

Sramana Mitra: That’s great. Landing a customer and growing the customer is a good way to build a business. I think it’s good 

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How an IT Services Startup Wants to Disrupt Itself in the AI Era: Sanjay Jupudi, CEO of Qentelli (Part 2)

Sramana Mitra: Tell me a little bit about this process of brainstorming about what you were going to focus on in the next company. What was the process with coming up with where you were going to place your bet?

Sanjay Jupudi: There was a quality assurance company that was hiring people to test applications. If you need testing services, I have 10 people and they’ll manually write all the test cases. When Agile started, a lot of clients were saying, “Testing function is slowing us down. Our people are developing code and then we have to wait three weeks for the testing to be complete.”

What they did was they wrote code to test code. Once the user story is developed, all the testers get together and they start testing in the waterfall way. After that, the code goes into production. You’re losing time and there’s a lot of frustration. It’s an inefficient process. There is potential to release faster >>>