Become a Creator today!Start creating today - Share your story with the world!
Start for free
00:00:00
00:00:01
/digital health: rewriting the rules of care
30 Plays2 days ago
What if AI could hear what doctors can’t?
This week James welcomes, Dr. Morris Nguyen, founder and CEO of Predicate, to explore how AI is transforming healthcare by combining patient voice and vital signs to detect critical illness early. Morris shares his journey from critical care physician to tech founder, what it takes to bridge medicine and machine learning, and why empathy may be AI’s most powerful feature. Together, they unpack what it means to build predictive, equitable healthcare technology, and the surprising challenges of scaling real-world innovation.
Recommended
Transcript
Introduction to Problem-Solving and IT's Future
00:00:00
Speaker
that founder should absolutely be obsessed with solving that problem. Not a solution looking for problem.
00:00:29
Speaker
Welcome to the forward slash podcast where we lean into the future of IT by inviting fellow thought leaders, innovators and problem solvers to slash through its complexity.
Meet Dr. Morris Nguyen: Medicine Meets AI
00:00:37
Speaker
Today we're talking to Dr. Morris Nguyen.
00:00:41
Speaker
Dr. Morris Nguyen is the founder and CEO of Predicate, an AI healthcare care company using predictive technologies that combine patient voice and vital signs to detect early signs of critical illness.
00:00:52
Speaker
Clinically trained in critical care, he bridges medicine, data science, and implementation strategy to make healthcare care more proactive and equitable. He has advised ministries of health and global health systems on technology-enabled care delivery, held roles at Edwards Life Sciences and Teleflex, and serves on advisory boards for emerging health tech startups.
00:01:13
Speaker
Dr. Wynn earned his MD and MBA from the University so university of Michigan, a Bachelor of Science in Physiology from Michigan State, and completed postgraduate training in health economics at the University of Washington.
00:01:26
Speaker
A Lean Six Sigma black belt, he brings operational rigor to digital health transformation. Thank you and welcome to the show, Dr. Wynn. Thank you, James. Thanks for her inviting me as a guest.
00:01:40
Speaker
i have to ask about, I've never heard of health economics. So give me a little like like, what is, what even is that? Well, it's the study of the cost of care, really, in translating ah the effects of ah not only the problems in health care to a unit in economics, but also the treatment strategies and what are the kind of the ah ROI between those two different treatment strategies as it affects certain ah health care problems.
00:02:13
Speaker
Awesome. so Okay. Yeah, that's great. And I noticed um we've we've met before over video and I don't know if I've asked you about You've got like these surfboard looking things behind you. They look like a war. What did it what even is that? What's the surfboard? What's with the surfboards?
00:02:28
Speaker
So I'm based in Wilmington, North Carolina. I'm not a surfer. I'm actually, i like to to go underneath the water. I'm more of a diver than a surfer. Okay. But ah so the, those, both both of those surfboards are from the Coastal Entrepreneur Awards in 2024. Okay.
00:02:48
Speaker
ah Predicate received a category, the category winner for the healthcare care category for Coastal Entrepreneur entrepreneur Awards. And then we qualified to be overall.
00:03:01
Speaker
and And we ended up winning the overall Coastal Entrepreneur um Award of the Year in 2024. The so bigger one of the two surfboards behind me is a smaller replica of the full-size surfboard. its it's ah It's interesting. So when you win the Coastal Entrepreneur a manure of the Year Award,
00:03:23
Speaker
consal entrepreneur of the year award then you have this five foot surfboard that sits ah in your office or your house for a year. It's like the Stanley cup for the Cape Fair region level and and yeah for business in the Cape Fair region.
Predicate's Recognition and Journey
00:03:41
Speaker
And um so I had the big surfboard there. Kits Making It is the company locally in Wilmington that hand makes these these boards. um And so I asked them to make a smaller version. was So this year when I handed off the big surfboard to 2025 winner, um they i I now have something to get to keep.
00:04:03
Speaker
And then the other one, that one, um ah did kind of a a flat Stanley design. Got a thing with it. um I travel internationally quite a bit.
00:04:14
Speaker
Yeah. We have studies across ah seven countries and five continents. So since I won the award in 2024, I started to bring that smaller surfboard with me wherever I travel.
00:04:28
Speaker
And I would take pictures of it. um Nice. I think I added up from one year. it it Travel approximately 70,000 miles ah that board has. um every Every continent from, you one end to the other, you know, the globe. but and um And then i at the 2024, this year's Coastal Entrepreneurial Board, I showed pictures of where the board has been.
00:04:57
Speaker
um Anyways, long story, but that's that's what those are. That's cool. Yeah. The flat Stanley thing. Yeah. there There was something where... some Some thing was making its way. It was like a Flat Stanley thing.
00:05:10
Speaker
don't remember what it was, but I remember like it met its demise pretty quickly when it got to the United States. Like we're we're just awful people sometimes, you know, and all these other countries were like, hey, this is cool. i don't It was like a teddy bear. I don't know. But then like when it got to the United States, the thing was all tore up and we ruined it.
00:05:28
Speaker
Yeah. Disappointing. All right. So I i met ah you at an event in North Carolina near Raleigh. I think it was in Durham, North Carolina.
00:05:40
Speaker
And ah you were you all were pitching, doing kind of ah a startup pitch contest kind of a thing. And I was just blown away hearing the pitch. ah So, you know. Full, you know, full transparency. I'm a big fan boy more. are You know i mean? Like, I love the story. I love what you guys are doing. So and and but I did think, you know, you you ah have a very unique perspective. You are you're a medical doctor and you're you're creating a new company from scratch in this technology world. I mean, in using AI, no less. So like that's not not an easy thing in and of itself. Yeah.
00:06:12
Speaker
You just have a really cool ah perspective. And I thought our audience would enjoy ah hearing about that. So tell us about your, you know, how did you go from this, the the medical field to saying, I think I want to take this thing, do some technology stuff with with my profession.
00:06:27
Speaker
Yeah, thanks for the question. And thanks for for that recognition. ah but the For me, ah it's the hallmark of every good founder is to be able to um first understand the problem that you're solving and then become obsessed with solving that problem every
From Consultancy to AI Tech Company
00:06:45
Speaker
single day. That's the a decision between do I become an entrepreneur or do keep my day job?
00:06:53
Speaker
And for me, and because of my experiences at the bedside, that really shaped the problem that we were trying to solve. And that problem was around, um as a critical care physician, sepsis patients or patients' ah condition that we ah very commonly see.
00:07:12
Speaker
And the challenge with sepsis is that can start very early, um meaning that before they get to the ICU when the patients that I saw. um And, but the the challenge that there are very subtle um signs um that are often missed because they hide behind more common and more benign symptoms.
00:07:35
Speaker
So that started kind of my journey on asking the question, What can we do better in detecting those early signs of sepsis? And then what technologies are currently available to help us or to enable us to detect those those the subtle signs?
00:07:51
Speaker
um Although I went to business school, I never intended to become an entrepreneur. I wanted practice. That's what went to school for. That's my passion. It still is my passion.
00:08:03
Speaker
But the problem that was calling out to me was bigger than my current, um you know, my my current intent ah not to be an entrepreneur. It was pulling me and saying that in order to affect change, you have to start a company and pursue this.
00:08:20
Speaker
And many position entrepreneurs, there are so many entrepreneurs Great clinical innovators out there. We see the problem at the bedside. Oftentimes we're MacGyvering different things, sometimes off-label, but it works.
00:08:38
Speaker
It solves the problem effectively for the patient. And then it also helps um enable the provider to do their jobs. um So in order to do this, you have to take a certain path.
00:08:50
Speaker
um And ah before I became... where Predicate is today, Predicate AI Labs. Before that, it was Predicate Healthcare care Performance Group, which I started in 2018 as a healthcare care consultancy.
00:09:05
Speaker
It was a group of physicians, ah mainly critical care physicians and nurses. And we knew that our at our respective hospitals, there were a problem around substance care.
00:09:19
Speaker
um primarily relating to um quality benchmarks like ah substance mortality, readmissions, stay, costs.
00:09:30
Speaker
um This was a headache keeping up ah keeping hospital administrators up at night. And we wanted to ah change that by looking first at the different clinical pathways or processes that were causing these these problems.
00:09:45
Speaker
And Predicate created this ah Lean Six Sigma based architecture called PROOF, Process and Outcomes Optimization Framework.
00:09:57
Speaker
And through PROOF, we were able to ah use evidence-based ah protocols and technologies to improve those quality benchmarks, um saving hospitals millions of dollars um but annually.
00:10:12
Speaker
And they'll be able to replicate that in the U.S. and then globally as well. Because every region around the world, sepsis may be the kind of the common denominator, or common problem shared across different health systems.
00:10:24
Speaker
The way that you approach it vastly different. So there was a learning built in there as well. um That evolved into ah where Predic is today is ah ah we're a full tech company dedicated to building frontier AI models to predict and prevent critical health conditions like sepsis from escalating.
Adapting to the AI Tech World
00:10:50
Speaker
Gotcha. Now, when it comes to, you know, You know, youre you don't come from the technology world. ah you You come from, you you have an MBA and you you're you're a medical doctor. So what what has that experience been like? what's You know, that learning curve now trying to enter into this this AI world. how How has that experience been for you?
00:11:12
Speaker
um You know, I don't know if by i would be up to speed ah to keep up with my on my dev team if I didn't have AI tools, to be perfectly honest.
00:11:24
Speaker
Sure. I sit through a, um you know, a a scrum or I use ah use an engineering term. proud of myself on that one. Well done, well done. Yeah.
00:11:35
Speaker
Or sit through an engineering, you know, dev meeting, you have daily huddles and things like that. And I'm listening and hearing all these technical terms. Then I come up with a list and I would put into an LLM and say, what does this mean?
00:11:49
Speaker
Explain this to me, please. Yes, exactly. Yeah, it allows me to be able just be a better leader and and be able to not have to i just drive take more time out of, you know, engineering's third time is guarded and and and un limited bandwidth as it is ah to explain to me, you know, just foundations of what things are. So in that and also do a lot of reading um on on the space and trends and all those kind things.
00:12:20
Speaker
Yeah, it's, ah you know, me being a consultant, you know, kind of this LLM world, it it actually kind of hurts me because, you know, now people have that that kind of BS detector. You know what i mean? Like I can't BS my way through things anymore. So, ah you know, I got figure it out. I got to I got figure out a way to like let the LLM help me BS better. i don't know. But yeah, that's that's that's got to be tough. And I mean, even as a technologist,
00:12:46
Speaker
AI is changing so like every every week right so it's it's it's hard as as somebody that's I grew up in this field and it's hard for me to keep so I can only imagine you know coming in from with a completely different like very very deep expertise in something trying to like extract yourself out of that to to try to dive deep into something that's yeah very cool.
00:13:06
Speaker
Yeah, but it's also one thing that is is great about having such a multidisciplinary team is that think Predicate is really a, we're software company first, trying to solve a medical problem.
Using AI for Vocal and Semantic Analysis
00:13:23
Speaker
So we have the domain expertise. Yeah. There are currently three MDs, including myself, on the leadership team at Predicate. we And we have a really good bench ah for our scientific advisory board.
00:13:36
Speaker
So we have the the clinical science, the the but medicine we we get. We clearly get that. Now, it's to bridge that to the technology world and to be able to have two different disciplines be able to effectively ah communicate.
00:13:54
Speaker
um articulate the clinical problem that we want to solve, articulate the desired UX, UI that we want, then be able to translate that to a product architecture for for our technologies.
00:14:06
Speaker
ah That is where for me is the the the joy of being in my role is to to see that happen. And while we're doing that, transforming um just the the space that we're in, which is health tech AI.
00:14:20
Speaker
So the the, you know, you're talking about, you know, you're you're detecting, you're using AI to detect and and understand, like, it's very hard to understand if a patient has sepsis or is is starting to come down with this this illness or condition. What is it?
00:14:36
Speaker
um yeah Condition. term Condition. Yeah. yeah but But you guys have figured out a way using AI um and maybe it it seems from what I know, it seems like kind of a, you know has to be kind of a novel approach or you wouldn't like create a whole company about it. So tell us a little bit about what what is your approach that's that's different?
00:14:57
Speaker
I'll start by um sharing our our tagline. ah We have two main taglines. One is your voice is a vital sign. and And then the second one is your symptoms deserve a voice.
00:15:10
Speaker
And as you can hear from the taglines, it's very voice centric. ah The idea that ah being able to ah actively listen for patient reported symptoms, be empathetic, ah and and then have the AI model ah be equally empathetic is the goal that we're trying to strive for. Because the theory or the thesis here is that there are, especially with sepsis,
00:15:40
Speaker
Patients are best attuned to their bodies. and When a patient expresses that they're feeling off or there is a headache that has persisted or a fever or a shortness of breath, they can't quite put their finger on what that is.
00:15:54
Speaker
And doctors, there's the saying, when you hear hoofbeats, think of horses, not zebras. We're trained to not necessarily all the time overthink the problem.
00:16:05
Speaker
We don't have the luxury of time or the the, you know, cognitive capacity to to do that with so many patients. sure So sepsis slips through those cracks. When the patient says, I'm feeling this sort of way, and doctor says, okay, I've heard...
00:16:17
Speaker
um multiple patients today expressing the same symptoms, I have to go through my differential diagnosis and you you don't you don't think it's it's as bad as you think. um that That also, what's compounding that ah that issue is the implicit bias that clinicians have as part of our human conditions, part of our training.
00:16:37
Speaker
um So what Predicate is doing, we're we're building an intelligence layer between human language and human biology. We're turning patient voice and symptom data into real-time clinical insights ah because we believe that your voice is a vital sign.
00:16:55
Speaker
And
Proactive and Equitable Healthcare
00:16:56
Speaker
just like heart rate and respiratory rate and temperature and SpO2 and all those other things, voice is that overlooked vital sign that is so critical. And in conditions like sepsis,
00:17:07
Speaker
where the symptoms present vague, ah in especially in the beginning, it's obfuscated, that you need to have an analytics platform that is receptive to what the patient is telling you because what they're telling you may be the key so unlocking the diagnosis.
00:17:29
Speaker
And is this, um from what you're what you're explaining, it kind of sounds like, correct me if I'm wrong, that it's it's like, it's not how they're saying it. So you're not looking for like intonation or anything like that, trying to detect like waveform patterns in in the in the speech.
00:17:46
Speaker
It's really about what they're saying, what they're telling you, right? it's the It's the content, not necessarily the signal, the noise that's stopp that you're looking for, but it's what they're actually saying. It's actually a little bit of both. is it ah So initially the platform was architected by looking at the semantics, understanding what the patient is is telling us. We use NTC detection from transcribed audio directly from patient interviews.
00:18:12
Speaker
And we will get, ah we parse ah out keywords and phrases that could help um with the prediction of the onset of substance.
00:18:23
Speaker
And then we started to, once we have ah kind of created that ah NLP pipeline. We then started to include vocal biomarkers into the architecture. So now we're looking things at intonation and and ah timbre and different things to be able to use that as ah diagnostic signals as well.
00:18:49
Speaker
And the science behind vocal biomarkers is really strong right now. it is It's been shown to predict for things like Alzheimer's disease. just based on analysis of speech.
00:19:00
Speaker
Wow. So looking at the the signatures within voice. um So now we're leveraging that science to help to predict for conditions like substance.
00:19:12
Speaker
Now, is this a, I may be going down a rabbit hole here, um because I have a ah take on AI kind of being in this exoskeleton, like a per perception perception exoskeleton for us, right? So is it is it something that our brains are are kind of subconsciously picking up on these intonations? And as you said, or timbres, I had't never heard that. well What does that mean?
00:19:35
Speaker
Timbre. Timbre. it's It's kind of a part of your, the the the voice signature. It's ah how you say something. and Because the the science behind ah vocal biomarkers is about like how plurifold. So your lungs and air passes through the vocal cords and all that. The subtle signs and the very minute that may be undetectable to the human ear, likely it is, is picked up because it has such unique characteristics. And when analyzed by a vocal biomarker platform, it's ah compared to like a a good biomarker library, we say this is normal.
00:20:08
Speaker
This is something is off based on that patient's ah profile, their characteristics, demographics, that kind of thing. um So, yeah, ah what I was going to say is like, is it is it something that like our ears are able to perceive that or is it something this is beyond our even perception level? Like, is it like we're hearing it?
00:20:31
Speaker
Our ears are actually picking up the signal. Our brains are processing it in a subconscious way. Or is it one of those like that' this is just beyond our perception anyway? And is that what is that what it is? I think there is some consensual perception of it that we may say that sounds off, um especially we are familiar with that particular person or that particular speech pattern.
00:20:51
Speaker
But the difference is that it we still have that, again, that implicit bias. And is it, do I, did I hear that correctly? Do I, do I interpret it as an an illness or is that something that I've heard so many times before? It just seems normal ah relative to conditions. So what the, an AI based platform would do is it removes that implicit bias ah and it says very ah objectively that This is a whole library of of um biomarkers that we know um considered normal threshold.
00:21:26
Speaker
And here is what we're hearing now. And to be able to contextualize that with other data signals. It's never analyzed alone, ah the voice or the vital signs or anything else we're doing.
00:21:38
Speaker
It's all part of a multimodal analysis that ah one contextualizes the other. And I think that in thinking broadly about AI and healthcare, care ah it won't reach its potential until it understands human context and the story behind the data.
00:21:58
Speaker
That's where AI can be really effective and in healthcare. Yeah. the ah the The thing I was, I was just talking to this with somebody yesterday about this is like, you know, we've, as we're starting to employ AI, we were talking about imaging in in medicine. So being able to look at ah an MRI.
00:22:16
Speaker
Um, you know, what the way all of these systems have been designed is to collect data in such a way from these devices and then to be able to present on an LCD screen, right? So because for a human being's perception, our little box of perception that we can do, right, of how we can perceive the world around us.
00:22:36
Speaker
But what my theory is that if we stop constraining ourselves to our perception boundaries, I don't need to tell the, a like, present the data in ah in a visual way, in the way that we would think that we need as, as you know, human beings.
00:22:51
Speaker
I can present a whole plethora of data. Like, the easiest way to describe it would be like, infrared, the signal, like we can only perceive certain ranges on that spectrum, but there's a whole lot of other stuff that that's out there. and And if we collect all of it and give that to the model, maybe that it could see things that we can't and and and perceive things that we can't.
00:23:12
Speaker
I think we, if we we have to snap out of our own little, our our own little perception bubble and and expand our horizons a little bit and and feed these models more that we don't know, and not even know is there.
00:23:24
Speaker
Exactly. And that's the the responsible way to to to to build AI models is to be able to collect from a diverse cohort of of ah data points.
00:23:37
Speaker
And for us, since it's so nuanced, language and voice and ah dealing with ah patient reported symptoms, we have to go directly to the source.
00:23:49
Speaker
We don't just take one data set in one, one contrary region of the world and then be able to simulate ah to others. We prefer take the time, put in the effort to go directly to the source.
00:24:03
Speaker
and So conducting, um de collecting data from that particular area of the world. And statistically, when you look at sepsis, there are,
00:24:14
Speaker
Over 49 million cases of sepsis globally, responsible for one in five global deaths. If you take all the deaths from cancers globally and add them together, sepsis still claims more lives on an annual basis.
00:24:30
Speaker
And 85% of sepsis incidents occur in the global south. So this is kind of the ah technology divide that we're seeing is that in the U.S. we know that there's a mandate to have to use electronic medical records.
00:24:46
Speaker
In and the global south, where 85% of substance occurs, many of those countries do not have access to an EHR or EMR. They're using paper charting.
00:24:59
Speaker
So surveillance tools for sepsis today in the U.S. that are abstracting data and depending on data coming from those EMRs, they can't be used, or it be scaled to those environments that don't use EHRs.
00:25:15
Speaker
So it was important for us when we we architected OpenDX that it'd be a lightweight solution to be able to take where sepsis occurs in those resource-constrained settings.
00:25:27
Speaker
And this is where kind of I know in your bio, you're wanting to make health care more proactive and equitable. I would imagine that's where that equitable part's coming in, right? Exactly right. um Our mission is to be able to create a future where technologies ah like OpenDX are predictive, but also accessible.
00:25:46
Speaker
ah to improve the human condition. And that's the whole ah motivation behind this is that creating these technologies, if they don't improve the human condition for all humans on earth, then we're missing something.
Beyond Sepsis: Expanding Applications
00:26:01
Speaker
We can't limit it just to to develop countries.
00:26:05
Speaker
This is fascinating stuff. and Yeah, when I when i heard this the your pitch when that day when we were at the conference together, I was just like, wow, and this is blowing my mind kind of stuff. That's really cool. Now, sepsis, obviously, it's very important and it's, in it's um you know, it's a big problem.
00:26:21
Speaker
um But is this technology just perfectly suited for sepsis only or is this more generally applicable? ah To be honest,
00:26:32
Speaker
Using the platform, I'm more excited when we rule out sepsis than when we rule in. We are kind of finding the patients that we may suspect of sepsis. um Then it becomes, with the platform goes through it it shows a low probability of risk for sepsis.
00:26:50
Speaker
Then that's when the the clinical logic kicks and says, what else could it be? Okay. So, and also, you know, just on the other side, its all the when non-obvious patients come in and where you may think it's an acute virus or some kind of something that you've seen, you know, if you're an ER physician, seen already 20 patients already that day, you're in your shift, you're tired. It's that, okay, I've seen this dehydration, acute virus. I've seen the, you know, abdominal pain of all these things.
00:27:21
Speaker
This is not sepsis. um then that's the non-obvious sepsis that OpenDX is able to ah to to detect.
00:27:31
Speaker
That's equally exciting. But um to your question, It's intended to be, let's start with sepsis, arguably the hardest problem solved in healthcare. care And once we start to show um strong evidence behind that, extend to adjacent conditions.
00:27:52
Speaker
um On our um strategic expansion roadmap, we're we're looking at ischemic heart disease, severe respiratory conditions, um chronic ah conditions, maybe the next pandemic.
00:28:04
Speaker
but it's intended to be a really strong ah clinical clinical decision support tool. Okay. All right. um and I'm sitting here trying to think of, ah you know, how would you go about training a model like this? And and I can't help but think were a Seinfeld fan?
00:28:21
Speaker
Remember there was an episode of Seinfeld where Kramer and Mickey got themselves like a side job where they're like going to the medical students and they have to pretend to have diseases. Like Kramer comes in, he's like, well, I got gonorrhea. And they're all like, oh, that tracks.
00:28:35
Speaker
Yeah. Now, so do you, do you, I mean, you have enough like actual data of real patients to help train these models? Or do you, do you need to like do this kind of mimicry thing? is that, is that a part of your training mechanism?
00:28:49
Speaker
No, we, we absolutely have real world data. That's really important because part of it is um we are building this,
00:29:00
Speaker
what we call a biolingual data library. It is uniquely ours. These are time synchronized data points with patient vitals, voice, uh, lab history, labs and and patient history where we have it, but it's all as part our time series analysis. So and it's, it's in our library.
00:29:20
Speaker
Um, We get that, we build that library from collecting data directly from patients ah in across the world. and We have hundreds of patients already yeah in that library.
00:29:33
Speaker
And um we we know what the diagnosis for that patient is. um And then when we start to to train and validate our model, um not only do we continue to get more patients from around the world, but we also are training with our own data, not being dependent on external, you know, data from other sources.
00:29:52
Speaker
yeah i would imagine it'd be kind of hard because of the nature of what you're looking for, like like true signals of human beings. It'd be hard to make like synthetic data, like to to fake it, to to to make it actually work. um So for some other types of machine learning problems, um think of like image recognition. If you have a picture of a cat,
00:30:10
Speaker
You can flip it horizontally, you know, and and reverse it, like mirror image of it, and that's still a cat, right? So it's easy to make synthetic data, those sort of things, but I can't, it's just, there wouldn't be a good way to do that for what you're doing, I can't imagine. I mean, maybe you could...
00:30:23
Speaker
speed it up a ah smidge and slow it down. I don't know if that would, and don't know. Right. Well, we also think about like where, where the data would come from if we, you know, so if we were to simulate data, we would have to get data from somewhere, either open source data sets or um most surveillance systems are training their, um their models on healthcare data from EHRs.
00:30:45
Speaker
and But, EHR data is notoriously fragmented. It's locked in silos. It's missing context, which a big thing. yeah And in know order, the only way to truly solve for that is to go get real-world data directly from the source, directly from the patient's mouth as they're reporting their symptoms at the time of presentation.
00:31:07
Speaker
That's the the best way to do it.
00:31:11
Speaker
So I know my experience, a lot of my experience been in kind of financial services. And one of the things about AI um in like a financial services world, anytime we need to make a decision that impacts somebody's life, ah people are very leery of that right now, right? is Where where the the mission the model, the machine is saying, give this person a loan or or don't give this person a loan. They're like, wait a minute, we got to be able to explain the AI if we're going to do that. And you better be able to back up your decision.
00:31:39
Speaker
um I would assume and that that's a that's a huge problem for you all, too. It's not like you you're probably not at the point where you want to say, yes, our our system can say, start pumping this person full of antibiotics right now. Stat, you know, like that. I would imagine the FDA would probably want a higher level scrutiny. Like, what what does that look like in in that in your field?
00:32:01
Speaker
Right. So I think of like traditional UX in healthcare, which captures data but loses context. And we design OpenDX to preserve both. um so in um An important part of the platform's architecture is the um idea of clinician in the loop, human in the loop, in the agentic loop, and then, of course, the agent in the human loop as well on that interplay you between the two.
00:32:28
Speaker
ah it's i don't...
00:32:32
Speaker
Not in my lifetime. I don't foresee a world where in healthcare, care AI is fully automated. We're not there yet. We're far from it. Although have seen studies where they have done that simulated in pigs and it's it's interesting.
00:32:44
Speaker
But i think there's, you're right. i think the acceptance and and the adoption for AI in healthcare care is is still ah very far away from that. But in order to earn the trust from human clinicians, we have to involve them.
00:33:00
Speaker
in the clinician decision-making process. So I think OpenDX is a clinical decision support system. it It provides the probability of risk. It never makes a diagnosis.
00:33:12
Speaker
Gotcha. Okay. So that that final decision, that that's still a human being doing that. No, they may get... they may get kind of ah a nudge to say, hey, you might want to look over at this information. I'm seeing something here that that should be interesting or may be interesting to you. Okay, I see.
00:33:29
Speaker
Right. So we're not, you're saying, like, we're not at big Hero 6 level yet where we have the big fluffy white dude that can do, that that can be kind of our personal doctor
Challenges in AI Startup Funding
00:33:37
Speaker
or whatever. Which is funny because i love Baymax and maybe in a not so distant future we will have a Baymax.
00:33:45
Speaker
um I actually thought of naming OpenDX Baymax because I was, you know, my kids love love watching it. And it's all like Baymax is cool. Yeah. like Yeah. I love it. Yeah, exactly. It was great.
00:34:01
Speaker
Now. um So you you are going through kind of the the the startup cycles and and the the idea of funding and everything. So I think the the kind of perception, because this just seems like it in in the market, is people are just throwing sick, crazy money at anything that has the the letters a and I in it. It doesn't matter if it's just a word. If it has that in there, they're going to, here's a billion dollars, you know, like,
00:34:28
Speaker
What has been your experience? Is that the case or are you finding it to be less of it, more of an uphill battle to to get funding? What are you what's been your experience? Yeah, and it's been really, ah I've mentioned this before in um in a different setting, but I said fundraising for me, my experience has been like ah ah like trying to grow facial hair for me. it's It's slow and with ah sometimes very ah um sparse and undesirable effects. Yeah.
00:35:00
Speaker
But it's a great analogy. ah So ah the overall experience has been that there still seems and this is a learning for me and as my maturity as a founder is that.
00:35:15
Speaker
That there are circles where in certain parts of the country that there's clearly like in the Valley or in Boston or in Austin, there's Nashville. In those markets, there are, there's an an ecosystem um where VCs understand, clearly connect the value proposition.
00:35:31
Speaker
They have the risk tolerance to know that these products um require a regulated pathway that may take 12 months or more to get to market. And the the challenge that we're finding is that until you find that strategic lead investor that understands that, that funding coming in is is great to help the company survive, but not necessarily thrive.
00:35:55
Speaker
Because in order for it to thrive, you have to have the investor that um that understands and can also help to advise on getting through the different milestones. um So um short of that, it's it's it's a challenge.
00:36:09
Speaker
ah Okay. Yeah, I think that's that's like, you know, just because of all the hype and everything around it, everybody kind of thinks that it's just like, oh, I'm starting an AI company. Here you go. Here's some money, right? Like, I don't think that's really the case. You still got to work for it a little bit these days. Okay, cool. Right.
Insights on Startup Journey
00:36:24
Speaker
Yeah, absolutely. I mean, AI is certainly, it's everywhere and and defining that, you know, ah companies that use AI to improve X um with their to they but products and their solutions.
00:36:35
Speaker
ah Whereas Predicate is a company that not only uses AI to help build AI, but we are actually um building the frontier or or we're building the AI model from the ground up.
00:36:47
Speaker
And that's important to to note as well. um So we've had investors that invest in AI companies are and um and that are more horizontal, um and but not someone that's in a deep ah deeply integrated into a vertical like healthcare vertical. We are, that takes a special kind of investment.
00:37:07
Speaker
Yeah. Yeah.
00:37:09
Speaker
And are you finding, um you know, this this journey to be enjoyable? how How has your experience been as a founder? And I mean, this isn't what you went to school for. i guess it's kind of hard to go to school for that. But has this been a pleasurable experience for you, rewarding, challenge? What has your experience been like?
00:37:31
Speaker
So I've been a founder ah but after so after the years of a consultant. We filed our first patent in November 2023. So we use that as kind of the the but the bar. Two years in as a founder. And I've probably decreased my longevity by about eight years in those two years. It is stressful.
00:37:50
Speaker
There's a lot of things. There's the uncertainty from day to day. um the You know, the the the idea of going from ah ah fist pump to fetal position on a daily basis and then repeat again.
00:38:02
Speaker
That is something that nobody, um despite talking to people, reading a ton of books, nobody really prepares you until you experience it. And um you have to have the support of the the startup ecosystem or else it just cannot work.
00:38:20
Speaker
You guys have a pretty good and a vibrant startup ecosystem down there in North Carolina. Is it pretty, pretty strong? We do, specifically in Wilmington. That's something that is really sustained us and other startup founders is we all have shared experiences. We're all kind of leaning on each other.
00:38:38
Speaker
and I was at a conference just yesterday and one of the, this was but with a bunch of CIOs and one of the folks, I don't remember the exact context, but the the gist of it was, he's like, you know,
00:38:49
Speaker
if If ah a vendor is coming in with AI stuff and telling you they have it it all figured out, they're lying. So as is, and I think there are a lot of, there there is a lot of that going on in the market.
00:39:03
Speaker
Does that, how have you found that? Is that making your road harder that because there may be a few of those things I've been calling them posers because I grew up around skaters, posers in the market that that are kind of the the snake oil salesman type folks out there, whereas you are are trying to bring a true, I mean, true product that that really is a has some legs to it.
00:39:24
Speaker
Is it making your road difficult? to Have you found that to be true? Yeah, there's, there's um because of AI and and where it is today and how it's weaved into our everyday lives, that's, therein lies part of the challenge when speaking with investors, is how do we sort through that noise and say, we're not just another ai whatever that means. We don't know what means. And there's a lot of assumptions, and both um for yeah from, you know, from the, from startups to investors to, you know, outside of that, all that, those circles as well is that,
00:39:58
Speaker
that and We know the AI is here. It's here to stay. And how do we best leverage that to help ah benefit humanity? um And for us to be able to quickly and clearly articulate our value proposition that we are using it to improve patient outcomes.
00:40:16
Speaker
um that and And then to quickly cut through all that noise and say, it's just not a me too. It's not anything else. It's not a pygmy. It's it's something that is differentiated. You've obviously got kids. The pygmy thing.
00:40:28
Speaker
Yeah. like I have two daughters in the house still. I haven't ordered this out of the house, but they do that whenever they're they're annoyed with each other. Quit being such a pick-me. That's funny. All right.
00:40:41
Speaker
Okay, cool. So, um that man, this continues to be fascinating. And every every time I talk to you, I'm just like my... my gears and keep on spinning in my head. So is it's, ah as always a great conversation.
Fun Segments and Lightning Round
00:40:55
Speaker
ah We are moving on to our second segment of the show. This is our ship it or skip it. Ship or skip, ship or skip. Everybody. We got to tell us if ship or skip.
00:41:06
Speaker
So for the first ship it or skip it topic, I guess ah kind of going along with the vibe of of of the startup or founder mentality, like what would you advise folks ah to on on, you know, yeah, go ahead and quit your job and go start a startup. Is it is that what you would say? is ah is that a ship it? Like, but yeah, go do this thing. What what is your advice there?
00:41:29
Speaker
first thing I think of is definitely sleep on it. In fact, so several nights of sleeping on it, talk to other founders, ah maybe even shadow a founder. I know it sounds weird, especially if you're were more advanced in your career to think of shadowing, but these are things that I wish I would have done before going in. There's no regrets there necessarily, but there was things that maybe given me more context and just going in and knowing fully be prepared.
00:41:55
Speaker
The the Another big thing is the really acknowledging the realities of the Curtin funding landscape.
00:42:07
Speaker
So, A lot of founders, um me included, we had, you know, a part of you know funding that was set aside to start our businesses. And then we had a pathway to get additional funding outside the capital.
00:42:21
Speaker
um If you're a new founder going in extend that. In fact, double it. that That whatever you that you set aside, double it before thinking going in.
00:42:31
Speaker
Because no okay and contingency funding, you'll go right through it when you start a business. That's my advice. Gotcha. um One of the hot topics on the ah ship it or skip it has been, and and I think you've you said you've listened to few of the shows, but we're, you know we have a lot of, you know, technical folks that are actually know doing software development.
00:42:53
Speaker
But be interesting to hear your take from a kind of perspective. owner type perspective, product manager type perspective for what you're trying to build, ah vibe coding and using the AI assistance.
00:43:05
Speaker
Maybe you're not down in the weeds, actually, you know, generating code with these agents day to day, but you are a consumer of that work product that comes out of that. How has your experience been ah as ah as a product, you know, and trying to develop a product using those sort of technologies?
00:43:20
Speaker
Would you say, yeah, let's do that? um Or what's what's your perspective there? Is that a ship it or is it skip it? i think it's a ship it, in my opinion. I, LLMs, where they've been, where they are, where they're heading, it's exciting for me. I think it's something that we shouldn't shy away from.
00:43:40
Speaker
We are using AI tools to help build AI, but it's not, it's also shifted kind of my, my, our kind of our recruiting for tech talent approach. So traditionally look for the software engineers, full stacks, ah DevOps, ML ops, then our data scientists, a ML engineers.
00:44:00
Speaker
um But now we're also looking at what these engineer, what experiences these engineers have with using LLMs to help enhance their productivity building it.
00:44:12
Speaker
So absolutely use the trusted LLMs, but then also be really good as an engineer, be really good at auditing those LLM outputs.
00:44:23
Speaker
Yeah. so So if you're not, if you're not that, if you're just kind of complacently just relying on, okay, LLM told me this, then you're going to be, you going make mistakes. Yeah, that's what I feel like.
00:44:34
Speaker
Yeah, the training on seeing through, like, almost like if I'm using the the the technical term properly, but debugging the LLM. Yeah, I think you've got to kind of have that skeptical mindset and questioning everything when you're working with these LLMs.
00:44:50
Speaker
And, know, I've been on pretty consistent on the show. I am a ship it for for AI assisted development. But I think, honestly, the more and more I work with it, the more bullish I get.
00:45:01
Speaker
But I will say one of the one of the themes that we've heard a lot of the kind of folks say, now, maybe it's self-serving. I don't know. But. yeah they They kind of share that you need that senior level person. that's That's where it really, that's the sweet spot of having kind of that, someone who has that experience. Like you're saying, and I know a bad architecture when I see it, or I know a security flaw when I see it. And those are that that experiential knowledge, it needs to be there.
00:45:27
Speaker
That, of course, brings up how do we, if we don't have the juniors, how do we give them the experience to be able to be those seniors later? So that's a whole nother thing. But man, I am seeing like from from my experience, again, I've been been doing it almost 30 years as I'm using, you know, AI assisted development tools.
00:45:44
Speaker
it It just, it blows me away how much more productive I can be. So I'm very bullish on it. I'm i'm um a ship it on. I'm not a, as you said, I'm not a ship it on the, what I call YOLO to prod. Like I'm not, that's not, no, don't just say, yeah, generate stuff and, you know, let's go.
00:45:58
Speaker
No, but I think using, having experienced folks in the loop and you're just, it's, you're not going to have ten x developer. You're going to have a hundred X or thousand X developers. Yeah. And I want to just a quick comment on just um hiring best practices.
00:46:13
Speaker
This is a learning for me as well is ah recruiting tech talent. And then that quote unquote, we don't consider it entry level. I consider it just um and it's experience naive or early experience.
00:46:26
Speaker
Sure. Folks. And then the mid levels and the seniors and even the senior level engineers, not going too senior to where you have guys that think like, well, back in my day when I had like, you know, green bar reports from a dot matrix printer, that senior. Right. But actually and know that the senior, the seniority in thought.
00:46:49
Speaker
Yeah. And perspective. to say that I had been fooled once, twice, too many times by bad AI outputs and bad engineering. Now I have a lot more detailed lens to look at these things, or and use more discretion and more skeptical.
00:47:06
Speaker
Absolutely. I love it when our engineers question that, is this right? And do it over and over again. um And the the ability to problem solve. So which, the way, link onto your previous question,
00:47:18
Speaker
In addition to having extended contingency funding, that founder should absolutely be obsessed with solving that problem. Not a solution looking for a problem, which we see a lot too with founders.
00:47:31
Speaker
Absolutely. But be obsessed with that that problem and be able to solve it. That's one of the things we say at Caliberty from our, our that that is absolutely part of our recruiting mantra is like we we want to find those people that, and I'm stealing this from one of our clients, ah fall in love with the problem.
00:47:47
Speaker
Not fall in love with your solution, as you said, right? that They're not in love with their solution because you have to be malleable when you when you're kind of optimizing to that optimal solution to a problem. You have to be malleable. You might come up with a hypothesis.
00:47:59
Speaker
and say, I think this is the right way. But as you start working with it, you have to be able to say, no, maybe we can pivot and might find a better pathway to get to that optimal solution. You have to be malleable, and but you but you got to be, you know, bulldogging on that. Like just, you know, sink your teeth into that problem and don't let go until you've got to, you know, beat.
Predicate's Mission and Future Events
00:48:19
Speaker
All right. Awesome. um As always, we will go to our final segment of the show, and this is our lightning round.
00:48:49
Speaker
um and i and I don't know if you've listened to all the way to the end of our our show before, but we, this is obviously the most important part of the show. There's a book people tune in for. It's what I believe they tune in for.
00:49:01
Speaker
Very important. The hard hitting topics that, that everybody tunes into here that they want to know. I mean, okay. Yeah. Maybe you're a founder in AI, right? You know what i mean? Like this, that's, that's cool and all it's, that's pretty novel. It's, it's an interesting thing, but what the, what,
00:49:18
Speaker
our folks really want to know is these topics. What what what what makes you tick, right? And it's questions like, would you rather have invisibility or super strength?
00:49:31
Speaker
ae So I would rather have super strength because, um and this is because I'm naturally an introvert. I go to parties. I'm the guy that is holding the red solar cup in the corner. don't talk anybody.
00:49:48
Speaker
But ah so invisibility, it seems like just being an introvert, kind of have it. um But the ability to have, so i would I would extend that. Not super strength, it's in physical strength, super strength of mind.
00:50:02
Speaker
um like that. Um, what about, what was your last Halloween costume that you wore? I remembered it well. And I, there's pictures of it that I will not ever, it would be in the archives deep.
00:50:17
Speaker
Um, It was, ah so at the time, our our youngest um was two. And I was actually younger. I had her in days those ah Bjorns. So we were in the front.
00:50:31
Speaker
ah And i it was one of those things where we went to our Halloween party. Neighbor has it every year, Big Bash kind of thing. So I put on like a werewolf mask.
00:50:42
Speaker
And like, what do I do with a baby and a werewolf mask? And so we had a little red riding hood, little outfit for her. So I had a robe, a werewolf mask, and she was in front of me. And there's a picture of me like biting on the head like I was a big bad wolf.
00:50:59
Speaker
from Little Red Riding Hood. Well done. I like that one. I thought as soon as you said like ah my my kid, I'm carrying my kid like in the front, I was thinking of like the alien thing. Like you put the, yeah whatever, was it Ridley?
00:51:11
Speaker
Is that the name of the thing? Yeah, like a mask of that on her, like she's coming out of your stomach. would be hilarious. That's not a bad idea. That would be a fun one. The whole alien thing. Yeah, yeah, yeah.
00:51:22
Speaker
All right. So what's the best age? I would say 30. Hmm. and and And I would say 30 because eating in your 20s, your frontal lobe is still underdeveloped, making really bad decisions.
00:51:41
Speaker
but Maybe, ah I'll give a range, 30 to 35. 30 to 35. Okay. 35, so you're you're you're already you already have some experience in your career, likely, and you've made mistakes from having an underdeveloped prefrontal cortex. Yeah.
00:51:57
Speaker
and ah And now you're kind of in use of some life experience. But you have still the energy of youth, which I think when you get to with family and getting really deep into just life in general, into your forty s then it becomes like, okay, there's a prioritization, especially for a founder, I think that's important.
00:52:24
Speaker
It's because balancing all those different priorities in life that you want to have that that resilience, that youthful, but also informed by experience and maturity. I think 30 to 35 is really good age to be a test for, know, what do I have the tolerance and the appetite for going forward in life?
00:52:44
Speaker
Yeah, I think it's kind of a sick joke that by the time, at least for me, my experience has been, by the time I kind of get to the point where I figure out like, oh, this is kind of how how you should do things in this world.
00:52:55
Speaker
You're too old. You're too tired to do it the right way now, right? Like, it's just nice. If I knew all these things when I was 30, that would have been awesome. ah But I didn't. All right. Um...
00:53:10
Speaker
What's the maximum number of spritzes of perfume or cologne before it's too much? Oh, anything beyond a spray in the air and walk through the cloud is too much.
00:53:23
Speaker
Okay, I like that. i have And I have... Yeah. Three boys, two girls, three boys. And so i I can smell my boys.
00:53:34
Speaker
They're older and I can smell them. I'm like, oh, you know, but that's my oldest leaving. I can smell them. And I've always told them, I said, trust me, if you can't smell it,
00:53:47
Speaker
ah ah people around you would definitely smell it. ah um So it's just, ah yeah, they got nose deaf. Yeah, yeah, exactly. And now is this the, I know for a while, like all the boys, and I had all girls, so I don't i really know. I know I've heard stories, people saying like, oh my gosh, my son with that Axe body spray. So is that what they're doing or is it like cologne, cologne?
00:54:07
Speaker
It's colon cologne, cologne. Okay. It's cologne. And they, there's this thing now where don't even know, one of my boys was, ah was bartering, ah with cologne at school. Like they they buy these expensive colognes. It's like some kind of weird, like black market thing they're doing.
00:54:27
Speaker
And they just, ah but you know, the other kid had this cologne and you want this cologne. but know So one of my boys wanted that cologne that they borrowed for something else, one of their football cards for it.
00:54:38
Speaker
What is going on here? What world am I living in? Now, do they, have they ever run across, the was it Sex Panther cologne? That's that's the one that's 60% of the time it works every time from Anchorman. Is that, yeah. It's made with real bits of Juicy Panther or something. I don't know.
00:54:58
Speaker
That's right. If we get there, I will, yeah. that's i'd I'd be asking a lot more questions than how many spritzes have you had today. It's like, what? Yeah. Yeah, we need to move out of this house. It really stings the nostrils, as you said. yeah All right. da Let's see. Let's get some more good stuff going here. ah When you're journaling or if you journal, do you like to use paper or do you use some form of a digital copy for that?
00:55:23
Speaker
I've transitioned ah from paper several, like, i owe well over, I mean, dozens and dozens of paper notebooks. Well, they used to take now about...
00:55:35
Speaker
A year ago, I transitioned to remarkable. So digital remarkable notes. um And the main reason is because not the fact that I don't like writing on paper. I still do. ah But the fact that I can organize my notes and do a search for things that, you know, were recorded and ah written down. I can know like what what day would I talk to and do a search on that.
00:55:57
Speaker
Okay. L.A. or New York? New York. On scale one to ten, how much do you enjoy garlic? Seven.
00:56:09
Speaker
um I cook a lot with garlic, yeah. Yeah. It's good for you, too. Keeps those ah vampires out of your hair, you know. That's right. Do you Instagram your food? No, I don't.
00:56:22
Speaker
Okay. That was the right answer, by the way. That's the one we were looking for.
00:56:27
Speaker
Can you touch your toes without bending your knees? Used to. Not now. No. That's the thing with that that good age range in 30 and 35. Yeah. yeah Yeah. Definitely do it then. Yeah. Do it now.
00:56:41
Speaker
Yeah. I'm almost afraid to try now. I was in martial arts for many years and I'm pretty flexible. You have to be to do that. So I was able to like, you know, put my palms on the floor. Like i was very flexible. I don't know about that anymore. i don't know if I can do that.
00:56:53
Speaker
um Do you like the smell of gasoline? Not particularly, no. No. I can tolerate it, but no, I don't i don't seek it out. I wouldn't get a a cologne or, oak you know, that's gasoline inspired.
00:57:08
Speaker
Eau de shell. yeah That's right. i so That was weird that it's on here. i always, I guess I never really realized it, but like growing up and everything, like I do like the smell. Is that one of those like cilantro things where it's like very polarizing? Some people hate it. Some people like do. I do like the smell of gasoline. It's weird. There's, there's, ah and i'm I'm sure there's, there's somebody to study, you know, this the smell of the petroleum ah that that's there. um You know, for me though, the smell that I, I particularly like is going into a, like Lowe's or a Home Depot.
00:57:44
Speaker
I love the lumber yard. and love the smell of fresh cut wood. Yeah, me too. Yeah. Makes you feel like a man. You know, sawdust around, or as I call it, man glitter. Whenever I do a project, g liter man glitter. when i am makes me fun When you have daughters, you have three daughters, and you got to take those opportunities for that, you know, kind of, you know, anyway. That's right.
00:58:05
Speaker
All right. All right. Our last question, and this is, this is for all the marbles. On a scale of one to 10, how good are you at wiffle ball? Six? Six?
00:58:17
Speaker
that's that is That's pretty good, actually. I mean, on... um Among all the people we've asked that question, I think you're you're right up there, right? Yeah. On the percentiles. And I only say that because I um i played a sport um see through college. I played tennis.
00:58:33
Speaker
Okay. So having racket, you know, you're having your striking a ball, moving object. and So that's why I put it with my six. But yeah, it's the ah light weightness of the ball of a wiffle ball in the bat.
00:58:46
Speaker
I think that was probably throwing me off. That's why i'm i'm not higher. But yeah, I'd say around a six. All right. So we've got that hand-eye coordination probably. You could probably make, come even if they're doing them wicked spins, like you can do some pretty crazy stuff with a whiffle ball.
00:58:59
Speaker
Right. Okay. So we have ah folks who do the tallying of these scores for these. we'll We'll get back to you on how you did on the lightning round. Just kind of... And anecdotally speaking, i feel like you did okay.
00:59:11
Speaker
You know what I mean? Like, I feel like it's not bad, not a bad score. But we'll we'll get back to you on that. ah it's It's a whole process that we go through. um But no, just kidding.
00:59:23
Speaker
Well, this was a lot of fun. I knew that, you know when I first talked to i was like, man, I think we'd done, I'd like to get him on a podcast. I think he'd be really cool guy to talk to. And I think think the audience would love to hear from you. So,
00:59:34
Speaker
ah This has been really, really great. and ah So thank you. Thank you so much, Morris, ah for joining us on the podcast. Thank you, James. No, this was fun. And it was a good topic. and Thanks for um highlighting these important issues that we're we're working on. And the lightning round was fun.
00:59:53
Speaker
That was, you probably helped me a little do some little self-discovery. I'm like, I haven't thought about that. um but yeah. I'm going to go smell some gasoline today. Yeah, exactly. Yeah, that's funny.
01:00:05
Speaker
All right. So this this has been a fantastic discussion about ah Predicate and everything. Tell us a little bit. or Do you have anything coming up? any Any takeaways or practical advice that you'd like to share? Or are you giving any talks coming up? And how can we learn more about Predicate, like a website or something?
01:00:20
Speaker
Yeah, the ah so I have, and if you're going to be in Wilmington, North Carolina, I'm a panelist at the East Coast Healthcare Summit, and which is going to be on October 28th at the Ballast Hotel. Really um interesting talks between policymakers and healthcare care leaders and tech leaders in healthcare. care And ah as far as what's going on with Predicate is that ah we recently announced that we are partners with the Mayo Clinic and, or say with Mayo Clinic, there's no the in front of it. And I just want to kind of reemphasize what we, our mission and what we're doing is that we believe that tomorrow's diagnosis won't begin in hospitals.
01:01:03
Speaker
They'll begin in conversations. So we want to make the next wave of AI and healthcare care more diagnostics ambient and to move healthcare from episodes to continuous and from reactive to anticipatory.
01:01:15
Speaker
And our efforts in building this is that we're going to be um planning a launch of our first platform, in Q1 of 2026. So look for that.
01:01:28
Speaker
Yeah, it's going to be really exciting to hear the next step in your all's journey. Yeah, this is going be really cool. If you want to find, you know, just learn more about about Predicate, um ah please visit us at Predicate Labs.
01:01:41
Speaker
Labs.ai. Okay, will do. All right. Well, if you'd like to get in touch with us at the forward slash here, please drop us a line at the forward slash at Caliberty.com. See you next time.
01:01:52
Speaker
The forward slash podcast is created by Caliberty. Our director is Dylan Quartz, producer Ryan Wilson, with editing by Steve Berardelli. Marketing support comes from Taylor Blessing.
01:02:03
Speaker
I'm your host, James Carman. Thanks for listening.