Meeting of the Minds | Jorge Torres and Adam Carrigan from MindsDB

Jorge Torres and Adam Carrigan have known each other a long time. They were roommates in college and worked on several projects together before finally founding their first company in London. And that was only the start of their collaboration.

Now they’re running their second startup, MindsDB, a company that treats machine learning as a database layer, allowing you to retrieve predictive analysis by performing a simple SQL query. Jorge is the CEO, Adam the COO.

On this edition of Founders Journey, Jorge and Adam discuss the many lessons they’ve learned in their long collaboration, including early mistakes they made in hiring and product release.

More information:

Jorge Torres (Co-founder and CEO of MindsDB) is an entrepreneur, academic, AI/machine-learning expert, and Visiting Research Scholar at the University of California, Berkeley. Before Co-Founding MindsDB Jorge was a senior software engineer at CareJourney and Skillshare and the second full-time developer at – a platform he helped to grow from a few thousand to a few million users.

Jorge specializes in creating software that transforms cultural exchange and is passionate about democratizing Machine Learning. His new venture MindsDB completed the YCombinator Accelerator program in March 2020 and recently announced their $4 million seed funding round led by OpenOceanVC.  

Adam Carrigan (Co-founder and COO of MindsDB) is an entrepreneur and former Management Consultant at Deloitte. After completing his studies at the Australian National University and the University of Queensland. Adam completed his dissertation at the University of Cambridge – specializing in the use of NLP to predict equity pricing.

With significant operational experience in various industries and extensive knowledge in finance, marketing and strategy Adam co-led his newest venture MindsDB through the YCombinator accelerator and has recently announced $4 Million in seed funding.

DISCLAIMER: Below is an AI generated transcript. There could be a few typos but it should be at least 90% accurate. Watch video or listen to the podcast for the full experience!

Jorge Torres 0:00
We did the pre seed round. The seed round was really get to know our investors well before we actually needed to raise funding to make sure that there there is that mutual fit

Alexander Ferguson 0:15
welcome everyone to UpTech Report now into our founders a journey here with Jorge Tory’s, based in San Francisco and Adam Kerrigan, based in the UK cofounders of mines dB, check out part one of our interview where we talked about their open source AI layer for databases. But I want to dive in a bit more into the story of the creation of, of this company that you guys co founders coming together? What How did it happen? And why are you guys working together in the first place? How did you meet?

Jorge Torres 0:41
So well, and I have been friends for many, many years, we met back at university in Australia at the Australian National University, I was studying finance, he was studying computer science specializing in sort of AI and machine learning. And we became very good friends lived together at college and worked on a few projects together, student politics, etc, became very good friends. And then we actually came together in London to create a previous company that we ran, which used computer vision for digital signage, so we could determine, you know, age, gender, and sort of all these demographic characteristics about individuals to target advertising to those people. It’s a terrible business to be in. And we know that now, we’re back could be a whole another episode of lessons learned. But one of the things that we did learn was that the, the process of building machine learning models is a lot of it is like building blocks. And so, you know, we were a small team limited resources. And so we had to be nimble in terms of how we could actually do this, because we couldn’t afford a data scientist that costs you know, some $300,000.

Alexander Ferguson 1:50
Cory, I’m gonna punt over to you for the development of the product itself. Curious on the kind of lessons learned there of how do you know when it’s ready to to launch it and get feedback and that whole iterative iterative process, any insights you can share?

Jorge Torres 2:07
There’s the saying that when you know, it’s ready, it’s probably too late. So we, I think that at the beginning, we were waiting for that when it was ready to kind of show it to people to go about it. But actually, going back to those very early days, the more we waited, that the more dangerous it was, and and now we understand that the product is, is never ready. And there’s always something that you can fix, there’s always something that you can improve, there’s always competition. So even if you think that at some point, it’s perfect, someone is going to have an edge that you haven’t seen before, and it just keeps you on your toes. And machine learning is such a hot topic that you know, what was, you know, the state of the arts a few months ago, today, it’s different and under for the project that we build, will by nature, and by definition, will never be ready. And the learning there is that you have to be comfortable with that. And you have to have a cadence that you feel comfortable for releasing features to your users. And also have, you know, tough skin to understand that not everything that you do will play out as you expect, but the more frequent your releases are, then the more frequency will realize that you are in the right target or not.

Alexander Ferguson 3:37
So more more frequent releases may be a smart tactic, going back over to you, Adam of funding, any common mistakes or lessons learned when it comes to seeking funding and being able to get started, that you can share.

Jorge Torres 3:52
Yeah, so I think one of the most important things is really finding an investor that shares your vision. There’s a lot of investors out there that will provide, you know, just a check. And maybe that’s what you need. And maybe maybe you’ve got expertise elsewhere. But I think really focusing and spending time to get to know your investors. And that’s one thing that we do, we did it the pre seed round review the seed round was really get to know our investors Well, before we actually needed to raise funding to make sure that there there is that mutual fit, you know, that they’re, you know, believe in the product and that you also believe in them and their ability to help you, you know, create the vision for the company and execute on that vision. So I think that is something that’s sort of not not focused on enough really about that, you know, invest found a fit, if you will.

Alexander Ferguson 4:40
Hooray, for once you get the funding actually building the right team to make this happen. There’s a lot of lessons learned when it comes to hiring the right people and building that right culture and being able to move forward. First of all, how big is the team today? We’re 14 people, 14 people and when it comes to hiring, what have you seen is common mistakes are the biggest mistake one could make would be building the team and making the making happen.

Jorge Torres 5:03
So I think that the biggest mistakes that we’ve made have been around hiring too fast. The the idea of hiring is so compelling once you have capital to hire people that you feel this rush to bring in as many people as you need, or you think that any, but assessing just the same as when you want to assess a founder, investor fit, finding a teammates, company fit is, is fine art that we are learning, and we continue to learn. But every time we do it in a kind of like a small decision of they look good on paper, and 15 minute call make sense, we start to realize that there were many things that we did not assess. And we learned those very early on in the company. And for that matter, like the team that we have right now is exactly the thing that we want. But it does take us a long time to to go over the interview process to understand that they will feel comfortable with the job that we want them to do. And and probably the best lesson that we had is to twofold. The first one is that they join initially for three months where there’s like a mutual assessment or of it. And it’s more like a longer interview time where we both understand if this is going to work in the long term. And the second part is for every job that we have. Now there is a challenge that is relevant to the work that we’re doing. Rather than making up with that like for developer or for sales for any of those are like this kind of typical challenges that you give people, but in essence are meaningless to your specific problems. We’ve learned a lot from just giving them a task that we’re actually trying to solve ourselves and see how they go about it. And and the people that do well in those are the ones that we bring in for like a three month initial engagement. And then if that goes well, then we can move into like a more permanent one. And really, that has changed dramatically. How we we’ve formed the team, since our first hires,

Alexander Ferguson 7:17
Adam, in this world of being able to create open source and then offer an enterprise that’s where the the actual business models around any insights there for others who are thinking of a similar model with their business.

Jorge Torres 7:33
Yeah, so I think the first step really is to make sure you have product market fit, right, that’s the most important, the most important thing before you start thinking kind of about pricing and, and business models, because they will change the business model that you start with almost certainly will change the first price that you know, you start your first customer will almost certainly be different to what you do 12 months and, and five years from now. So I think really, that is probably the most important thing that you know, be flexible. You know, you’ve got to have a plan. But you’ve also got to be able to adjust that plan. Because when it comes to startups, every every day is different. And you’re probably going in many different directions within the same week. So I think that’s probably the most important thing to focus on.

Alexander Ferguson 8:20
Last question, what kind of tech innovations you guys predict? We’ll see in the near term and long term coming up. For

Jorge Torres 8:28
Yeah, I think that people that are dealing with genomic data are going to see a lot of innovation throughout the cost of having whole genome sequencing information has gone from like a billion dollars to a few $100. Right now, I think you can get it for less than $100. So all the innovation around that will be overwhelming. And I guess longer term surely is going to be that that kind of like intersection of this quantum computing and machine learning AI which their companies are already are drilling into this for drug discovery and very complex machine learning problems as well as kind of computational problems. But that intersection will continue to unfold. The most fascinating solutions to problems are still, you know uncrackable.

And I think you know, they bias to you because of the industry we’re in. But really, I think automated machine learning is going to be some much more over the coming years. And we hope to be and plan to be a big part of that. We don’t plan to kind of replace humans, it’s all about sort of augmenting their decision making and, and sort of in the medium to longer term, what becomes really important is explainability. And that’s something that binds up does very well. There’s always room for improvement and in sort of 510 years time for the level of explainability that you’d be able to get from these previous sort of black box models is going to be sort of I’m amazing.

Alexander Ferguson 10:02
Well, thank you so much Adam in Hawaii for sharing both your insights over the last couple of years. And even before that I know there’s always something being able to learn. And for what you guys are doing at minds, minds TV. For those who want to learn more, definitely check it out, also on GitHub to be able to download their solution. Thank you, everyone for joining us. Our sponsor for today’s episode is TeraLeap. If your company wants to find out how to better leverage the power of video to increase sales, and marketing results, head over to and learn about the new product customer stories. We’ll see you guys next time. Thanks so much. That concludes the audio version of this episode. To see the original and more visit our UpTech Report YouTube channel. If you know a tech company, we should interview you can nominate them at UpTech Or if you just prefer to listen, make sure you subscribe to this series on Apple podcasts, Spotify or your favorite podcasting app.



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