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 >>>

How an IT Services Startup Wants to Disrupt Itself in the AI Era: Sanjay Jupudi, CEO of Qentelli (Part 1)

If you haven’t already, please study our Bootstrapping Course and Investor Introductions page. 

AI will disrupt outsourcing services companies in a very big way. We talk to Sanjay on how he plans to prepare.

Sramana Mitra: Let’s go back to the very beginning of your personal journey. Tell me where you’re from, where were you born, raised, and in what kind of background. Since this is a co-founder story, it would be great if you could also bring a little bit of Prasanna’s story.

Sanjay Jupudi: I’m from India.

Sramana Mitra: What part of India? >>>

1Mby1M Virtual Accelerator Investor Forum: With Ankit Jain of Gradient Ventures (Part 6)

Sramana Mitra: It’s fascinating, isn’t it? It’s a really exciting field. Are you chasing unicorns?

Ankit Jain: Given that we’re investing $1 million to $10 million, we’re looking at a lot of companies that we hope will show their true market potential. We hope some of them get to that stage. We strongly believe many of them will get there.

Sramana Mitra: AI, as you said, is applicable to every domain. If you understand the tools of AI, you can apply it in creative ways to solve problems in every single vertical. Not all of these are billion-dollar TAM opportunities. Some of them are specialized. How do you parse these opportunities? >>>

1Mby1M Virtual Accelerator Investor Forum: With Ankit Jain of Gradient Ventures (Part 5)

Sramana Mitra: In the case of Algorithmia, what kinds of use cases are they? It sounds like it’s a horizontal platform that could be applied to all sorts of use cases. You said it’s a Fortune 500 target market. What kinds of use cases are they going after?

Ankit Jain: The simplest way to describe Algorithmia is in the context of a company that has hundreds of algorithm developers. There’s a lot of companies that are doing this, especially in banking and finance where different people are trying out different models. That institutional knowledge doesn’t get shared across a company.

Often, in companies that have internal code-sharing infrastructure, everyone publishes their code on GitHub. Let’s say you and I work at >>>