Founder's Story
The Future of AI is Private: Gavin Whyte's Bold Vision at Brew AI | Ep. 191
Tue, 18 Mar 2025
Gavin Whyte is a visionary leader with over a decade of expertise in artificial intelligence. Transitioning from a background in design and mathematics to becoming a pioneer in secure, private AI, Gavin has redefined what’s possible in enterprise technology. His journey, marked by roles as Deloitte Australia’s chief scientist and adjunct professor, underscores a relentless pursuit of innovation.HIS JOURNEYUnconventional Beginnings:Gavin’s story began in design, where his passion for aesthetics and creativity merged with a natural talent for mathematics. A pivotal shift led him to computer science, igniting his lifelong commitment to AI.Career Milestones:With significant stints at Deloitte Australia and as director of data science at KPMG, Gavin honed his skills in predictive analytics, neural networks, and deep learning. His academic and industry experience set the stage for founding Brew AI, where he now leads cutting-edge research and development.VISION & IMPACTAt Brew AI, Gavin is dedicated to building secure, private large language models that empower industries such as law, finance, and government. His work ensures data integrity while driving scalable innovation. By developing advanced AI solutions that reduce hallucinations and enhance predictive accuracy, Gavin is transforming how organizations harness data.INNOVATION & THE FUTURE OF AIGavin’s forward-thinking approach centers on integrating deep reasoning models and AI agents to create smarter, more efficient systems. His vision is to unlock the full potential of AI, enabling businesses to make better, faster decisions while safeguarding their intellectual property.CONNECTLearn more about Brew AI and explore Gavin’s transformative journey at brewai.com. Connect with Gavin on LinkedIn for further insights into the future of artificial intelligence.Our Sponsors:* Check out Avocado Green Mattress: https://www.avocadogreenmattress.com* Check out Indeed: https://indeed.com/FOUNDERSSTORY* Check out Kinsta: https://kinsta.com* Check out Plus500: https://plus500.com* Check out Rosetta Stone and use my code TODAY for a great deal: https://www.rosettastone.comAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy
Chapter 1: What is Gavin Whyte's journey into AI?
topic i think it's our most popular topic right now because i can be a little bit pessimistic a little bit optimistic but i'm going to be very curious around your thoughts uh with the future of ai but before we get into that and all the things that you're building at brew ai how did you get started with ai specifically and then why did you create brew ai
Daniel, firstly, thank you for having me here today. So how did I get started? Kind of a strange story, actually, but I'll tell you anyway. So when I was younger, I was actually a designer. So I love to design clothing. And I was really good with mathematics for whatever reason. And one day I was really angry with my parents because I was in the studio and it was there.
You know, they had a whole fashion house and they had a background in retail and clothing. And I said to them, you know, this is not quite working. I'm changing all the designs. And they got really angry with me. So they kicked me out of it and said, you're going to go to university and study mathematics. So I went to university to study mathematics, but I didn't quite take mathematics.
I took computer science and mathematics. and realized that I actually loved it. So much so that I got into it and it was amazing. I started to program and that's how I got myself into AI. I started to do statistics and write algorithms and code these algorithms at scale and I was really good at it. That's how I actually got into it. I know it's strange, but hey.
Well, it's a good thing that your parents pushed you to do that. Otherwise, you might have been in fashion, right? Although I could say, you are very fashionable, and I need some help on that end. So maybe AI, you can build me something that AI can help. So amazing story. Obviously, you've been doing it for a while. You've seen a lot of the transitions the last 10 plus years. Then why...
through AI, and I've seen what you're doing with private LLMs, which I feel like are going to be a massive part of our future.
Absolutely, Daniel. So one of my roles previous to starting as chief scientist, I was the adjunct professor at the University of Technology in Sydney. So I was teaching deep learning and mathematics to students. And some of the key areas where I did a lot of research in was in neural nets and back propagation. Not to go too technical here, so I'm going to try to keep it a bit high level.
And one of the key areas of focus was in research. How do we ingest large volumes of data? How do we predict accurately? And those days, I was doing predictive analytics with neural nets, not only just your basic predictive algorithms. And that's where the interest started quite a lot. And I got into the industry, and previous to Deloitte, I was the director of data science at KPNG.
And I started to apply this at scale in banking, et cetera, et cetera. And utilizing that background and thought process, I started to put a lot of effort into how the neural net was evolving and how we can apply it. And that's how I've led into this whole AI, into this... entire AI and neural nets and LLM area. It was around 2019 when Stanford released the paper on neural nets.
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Chapter 2: Why did Gavin Whyte create Brew AI?
So you've done all this. You create Brute AI. And there's a reason why you created it, because there's a lot of talks around corporations and data. If you use ChatGBT, for example, will somebody else see what I'm having or who... If I use deep seek, who sees that data? There's a lot of these talks around who's really having access or who can see this data.
Is that why corporations are more apt to use a private LLM to ensure that sensitive information doesn't get out?
Yeah, absolutely, Daniel. And I think one of the key areas in corporations is to keep their data safe, private. So if you start using other types of LLMs that are open, and generally I don't like to refer to their names, You know, you are sharing your data with the world. You are sharing your company's IP with the world.
Whether you ask it a question with fake data, you are sharing how you think as a company and you are training that LLM. Now, if you're in a business and you want to keep your IP safe, going to these open LLMs, as we call it, and sharing your questions and your information to the whole world will only train that brain.
That is one very large, big brain that works at scale, which is not specific to any industry. But when you start using private LLMs, we can encrypt the data, we can store it on these servers, and we can provide the same level of intelligence as you would get on the larger ones, on smaller machines, but more accurate.
Are you finding that governments around the world or government organizations, are they leaning on this?
And I'll talk a little bit about that from an Australian perspective. When we did start in Australia about three years ago, We started to grow into mine, into mining groups, into big legal firms and into government as well. And the reason for this is because governments wants to keep citizen status safe and they don't want the private LLMs to be biased.
So they want to train it and control it to allow citizens to interact with it without outside influence. Hence, Hence, that's why we have deployed it in these scenarios as well. We use it for case law, for legal companies where the LLM can analyze the cases and very specifically spit out the outcomes. And there's one thing I want to point out, Daniel.
You may have used ChatGPT and other types of GPT where you may have heard it made up information. It created information. Michael Cohen is a famous Trump lawyer where he submitted information and ChatGPT created a whole case, which was all fake, which he submitted to the judge, which he had to retract when they did find out that case was made up.
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Chapter 3: How do neural networks work in AI?
Now we call that hallucinations in the tech industry. These LLMs hallucinate. And that's okay. It depends on the level of hallucination that you want to reduce. So what we did is last year we released a global mathematical paper on how to reduce hallucinations. We were first in the globe and we were very welcomed by all scientists around the globe at this very large mathematical conference.
And we actually solved that problem. So we have a very extreme rate of hallucination reductions around above the 90s. And we've included that in this private technology program. So now we're starting to see we're in the forefront of how we get answers out from the questions you ask it without it hallucinating, which was a huge milestone for the company as well.
My LLMs hallucinate all the time. My GBTs. And I've gotten into the situation where I have used wrong information, not knowing it was wrong information. So I can see the massive value and I'm doing small things. I can imagine if it's if I'm an attorney or something. So can you can you talk to me, too, about so is a private LLM like an empty brain? and it only works as you add things?
Or does it, like if you give it to a company or a company starts using it, does it already have a certain amount of information based on what they want? How does this work?
Yeah, so there's many ways to use private LLMs. There's a lot of open source LLMs, which you can incorporate into private or you can train your own. So we do a whole mixture of all of these. You have your base models. But I think what's really important is when we do test this, for example, in legal terms, We had to give them a base model. You can train it.
There's a lot of court cases around the globe. They're all open anyway. But in the Australian setting, you cannot train it with existing client data. So we don't do that. So we want the LLM not to be biased.
So we give them base models, and these base models, they upload the data, they index it, and just by uploading a PDF or a whole folder of cases, and they can ask it all the questions, and it does a very, very good, accurate outcome, and one of the largest law firms in Australia today uses it. So we make it as easy as we can for our customers. We have these base models.
They don't need to do any more training. You can't from a legal perspective, but you can in other industries, in marketing, et cetera. You can actually utilize this LLMs at scale and further fine tune it to refine it to your outcomes as well.
Thank you for explaining that because no one has ever told me the details and the detailed differences between public and private. I can see a massive industry. I would imagine this might be the... fastest or highest grossing part of AI going forward, just because it's a huge topic around these companies using, like you said, something that is more open.
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Chapter 4: Why are private LLMs important for corporations?
Maybe can you share anything from the research you've done in terms of is there something that people don't know but probably should?
Well, there's a lot going on in AI. You know, the scientists around the world are just going to make it smarter. I will tell you this, though. The deep reasoning is going to get smarter and smarter and smarter. It's because of the way we're reordering the algorithms and the amount of volumes of data we have.
So we can ask other LLMs questions and use it to train our private LLMs without sharing info. We can do some amazing things these days in the labs with these LLMs. So we are going to get smarter at what we do. Where I do get afraid sometimes is autonomous weaponry, but that's a whole different ballgame that I'm not even going to go into, given that I worked in these industries before.
You know, that is my biggest fear. But overall, for just better health and human life, I think AI can actually help quite tremendously. It can, you know, why do we need to go to the doctor? Maybe once a year, twice a year when we get sick, why can't we track and trace our heart rate, et cetera, et cetera, using AI? And it could recommend things, improve our diet, improve our lives, our lifestyle.
I think that is important. Understand our heart rate, our anxiety levels, and help us through that process. I think AI can be useful, good, and it'll improve our lives quite tremendously.
Yeah, we just had a guest on who is building AI to help therapists because there's this mental health crisis and there's not enough therapists. And that got me excited. I was like, you know what? I love the health angles and how, like you said, We can why who knows how many times we can catch something before because we're not always very preventative when it comes to health care.
And in many countries, preventative health care doesn't even exist at all. It's very reactive. And if they get sick, wipes out their entire wealth of their family for generations. So what if we can solve these things and the cost gets lower and lower? So, Gavin, you have got me. I am going to change. I'm like more optimistic now. then pessimistic, which was the opposite before we started talking.
So I appreciate you today really diving in and explaining these things because I've been wondering all about this private LLM, how this is working, data, biases, hallucinations, everything there. If you want to get in touch with you, they want to find out more about Brew AI. I know a lot of people...
that need this so how can they do so yeah so uh our website is brewai.com uh my name gavin white you'll find me on linkedin um you know quite easily i guess everyone finds me on linkedin uh and happy to share my uh email um it's gavin.white at brewai.com uh so i'm happy to share my details as well um yeah
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Chapter 5: How are governments utilizing private AI solutions?
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an accounting and tax platform for small and medium businesses utilizing deep reasoning or generative AI. Now, this is exciting because your data is safe and you can actually talk to your accounting system or type questions in to say, give me an instant cash flow. Give me a predicted cash flow. Could you do my tax return for me? So that's very exciting.
And we're about to release that very shortly for millions of businesses in the United States. I'm really excited about that. I think LLMs and deep reasoning can help small, medium businesses to really grow their business quite significantly and not worry about all the menial task of returns or understanding the cash flow, etc.
I'm curious. This is amazing. It seems like you could solve a million problems. I mean, I could tell you 100 new products and services that Brew.ai could additionally solve just because there are so many that need to get better. Like you said, you can do so much with this. How do you hone in? Because if it was me, I have ADHD. I would just be going crazy.
Like I would have like a thousand things, which would be obviously is like impossible to do. But how do you really hone in? Just because there's it sounds like to me, Brew AI could solve and create better products and services for so many industries in so many ways.
Now, Daniel, I have that question asked quite regularly, you know, and it's great that we can do these things. But, you know, as a business and, you know, we really need to focus on specific industries. And while we grow those industries and while we grow the company revenue, we will then tackle other types of industries. You know, we are really in law. We are really in merchant banking scenarios.
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