AI for Preventive Care - with Shaji Nair | Epi.28 | Active Action Podcast

In this episode, Dr. Nazif interviews Shaji Nair, the founder and CEO of Friska AI. Shaji shares his journey from a career in technology to developing Friska AI, an innovative platform that employs advanced artificial intelligence and mobile technology to assist with preventive healthcare. The discussion covers how Friska AI provides personalized meal plans, health alerts, and activity recommendations to both patients and physicians, helping to ensure continuous health management. Additionally, Shaji talks about the unique approaches and engines behind Friska AI, such as the Nutri AI engine and clinical decision engine, and the platform’s potential to democratize healthcare access at low or no cost.

What You’ll Learn:

What Friska AI is and how it supports both patients and healthcare providers
The role of AI in revolutionizing preventive healthcare and chronic disease management
How Friska AI uses its own in-house large language models (LLMs) and engines like Nutri AI and Clinical Decision Support
Real-life examples of how Friska AI personalizes care through wearables, glucose monitoring, and meal plans
The importance of making healthcare accessible, especially in underserved areas
How Friska AI’s upcoming voice companion feature is designed to support lonely or isolated patients
What makes Friska AI unique from other health apps and why it stands out in the AI-health landscape

Be sure to check the webpage of Shaji at the Active Action Podcast Website to learn more about his work, and ways to connect with his.

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[00:00:00] - Episode Highlights

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00:00:00 --> 00:00:03 Friska AI highlights this commitment by providing
00:00:03 --> 00:00:06 an innovative platform that employs advanced
00:00:06 --> 00:00:10 artificial intelligence and mobile technology
00:00:10 --> 00:00:13 to empower people to collaborate with their physicians
00:00:13 --> 00:00:17 and take control of their health journey. We
00:00:17 --> 00:00:20 want to make healthcare, expertise healthcare,
00:00:20 --> 00:00:25 available in the hands of people at an affordable
00:00:25 --> 00:00:29 or as near close to. zero cost as possible. The
00:00:29 --> 00:00:34 next iteration of our engine would give the capability
00:00:34 --> 00:00:39 for the patient to input data via audio, right?
00:00:39 --> 00:00:43 So you can say, hey, I ate this morning. I had
00:00:43 --> 00:00:46 a toast with scrambled egg and a banana. Then
00:00:46 --> 00:00:50 the application will get all the micronutrients.
00:00:50 --> 00:00:53 And what they ate, this guy is to solve some
00:00:53 --> 00:00:56 of the challenges in the health space. So your
00:00:56 --> 00:00:59 listeners, if... they have a challenge or they
00:00:59 --> 00:01:02 think there is a problem they would like to address.
00:01:02 --> 00:01:04 I'm not promising a solution, but definitely
00:01:04 --> 00:01:24 we would like to take a stab at it. where every
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00:01:48 --> 00:01:52 activeaction .shop. Now sit back, relax, and
00:01:52 --> 00:01:59 enjoy this episode. Dear listeners, welcome back
00:01:59 --> 00:02:01 to another episode of the Active Action Podcast.
00:02:01 --> 00:02:05 It's me, your host, Dr. Nazif, to talk to you
00:02:05 --> 00:02:08 regarding a wonderful topic today, both related
00:02:08 --> 00:02:12 to artificial intelligence and health. Before
00:02:12 --> 00:02:14 that, I just want to take this moment to appreciate
00:02:14 --> 00:02:17 our dear audiences for sticking up with us and
00:02:17 --> 00:02:21 providing your valuable feedback. I just wanted
00:02:21 --> 00:02:23 to let you guys know we did start our Premium
00:02:23 --> 00:02:25 Supporters Program. where if you become one of
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00:02:31 --> 00:02:33 and professional development. You can do that
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00:02:50 --> 00:02:58 .shop. introduce to you Shaji Nair. So Shaji
00:02:58 --> 00:03:04 is the founder and CEO of Friska AI and as you
00:03:04 --> 00:03:08 can see our topic for today is AI or artificial
00:03:08 --> 00:03:13 intelligence for preventive care. So before just
00:03:13 --> 00:03:15 digging into the podcast I want to introduce
00:03:15 --> 00:03:19 to you a bit about Shaji Nair. So Shaji Nair
00:03:19 --> 00:03:22 is the founder and CEO of several successful
00:03:22 --> 00:03:27 ventures including hwfl company friska ai and
00:03:27 --> 00:03:31 calorie corp throughout his 30 years career shaji
00:03:31 --> 00:03:34 nair has focused on leveraging technology and
00:03:34 --> 00:03:38 human networks to solve most pressing challenges
00:03:38 --> 00:03:43 in healthcare his latest venture friska ai highlights
00:03:43 --> 00:03:46 this commitment by providing an innovative platform
00:03:46 --> 00:03:49 that employs advanced artificial intelligence
00:03:50 --> 00:03:54 and mobile technology to empower people to collaborate
00:03:54 --> 00:03:57 with their physicians and take control of their
00:03:57 --> 00:04:01 health journey. Very intriguing just to learn
00:04:01 --> 00:04:04 about Friska and what it does, how that helps
00:04:04 --> 00:04:08 with preventive care. So dear Shaji Nair, thank
00:04:08 --> 00:04:10 you so much for joining again today's podcast.
00:04:10 --> 00:04:13 How are you doing this evening? Very good, Dr.
00:04:13 --> 00:04:16 Nassif. Thank you. It's nice to meet you and
00:04:16 --> 00:04:21 excited to talk to you. Very nice to meet you
00:04:21 --> 00:04:24 too, Shaji. And I'm very excited to learn from
00:04:24 --> 00:04:28 your knowledge about AI and how that articulates
00:04:28 --> 00:04:30 with preventive care. Would you like to provide
00:04:30 --> 00:04:33 a bit of a background about yourself apart from
00:04:33 --> 00:04:37 what I just said to the audience? Thank you.
00:04:38 --> 00:04:41 I am from a technology background like a lot
00:04:41 --> 00:04:46 of Indians who graduated in 1990s. and the opportunities
00:04:46 --> 00:04:51 in computers, technology, software. So I've worked
00:04:51 --> 00:04:54 in primarily as a consulting side of the technology,
00:04:55 --> 00:04:59 identifying how to build solution or customers,
00:05:00 --> 00:05:04 identifying challenges. And for the last, I think
00:05:04 --> 00:05:06 the last two decades, primarily I've been working
00:05:06 --> 00:05:10 in the health or health tech space, both the
00:05:10 --> 00:05:12 US federal government and some large players
00:05:12 --> 00:05:16 within the. U .S. federal government space. That
00:05:16 --> 00:05:21 brought me into looking at the largest piece
00:05:21 --> 00:05:24 of U .S. economy, which is the health care and
00:05:24 --> 00:05:27 health care expenses, which currently stands
00:05:27 --> 00:05:31 at 17 -18 % of the U .S. GDP, roughly close to
00:05:31 --> 00:05:35 $5 trillion. I've seen this grow exponentially,
00:05:35 --> 00:05:39 but it also provided not so good outcome for
00:05:39 --> 00:05:43 the U .S. health, with all this money. So I've
00:05:43 --> 00:05:47 been looking at how do we find solutions that
00:05:47 --> 00:05:50 would address some of these challenges. It's
00:05:50 --> 00:05:53 primarily you're spending more money, but you're
00:05:53 --> 00:05:55 getting less return. Where are the challenges?
00:05:55 --> 00:06:00 So we started working in the data analytics and
00:06:00 --> 00:06:02 intelligence space. And so that's the background
00:06:02 --> 00:06:07 which I brought back to looking at my own entrepreneurial
00:06:07 --> 00:06:11 journey to how to build solution to address those.
00:06:14 --> 00:06:17 Thank you for that wonderful introduction, Shaji.
00:06:17 --> 00:06:21 So I want to ask you out of curiosity, what actually
00:06:21 --> 00:06:25 led you to come into this healthcare space? What
00:06:25 --> 00:06:27 actually intrigued you to begin your journey
00:06:27 --> 00:06:32 in this space? Yeah, initially it was more of
00:06:32 --> 00:06:35 opportunities in terms of my career. I ended
00:06:35 --> 00:06:39 up working in tech companies. Those were focused
00:06:39 --> 00:06:42 on healthcare or healthcare -related federal
00:06:42 --> 00:06:46 contracts. But once I started working in those
00:06:46 --> 00:06:51 projects, it became a passion. Because the problems
00:06:51 --> 00:06:54 are huge. Whenever there is a big problem, I
00:06:54 --> 00:06:58 am excited to see if I can add value, if I can
00:06:58 --> 00:07:01 solve some of those challenges, trying to understand
00:07:01 --> 00:07:05 bigger. And there's nothing as big as the healthcare
00:07:05 --> 00:07:08 challenge, whether it's largest economy in the
00:07:08 --> 00:07:12 world like U .S. or even a small place. So healthcare
00:07:12 --> 00:07:18 is a unique set of challenges. So it became a
00:07:18 --> 00:07:22 passion from career opportunities to a passion
00:07:22 --> 00:07:27 to solve. Okay, that's wonderful. So your passion
00:07:27 --> 00:07:30 actually led you to work in the field of artificial
00:07:30 --> 00:07:33 intelligence and how you blended that with healthcare
00:07:33 --> 00:07:36 coming to preventive care as well. Can I know
00:07:36 --> 00:07:40 a little bit about your innovation? If I say
00:07:40 --> 00:07:45 Friska AI, what is actually Friska AI? Friska
00:07:45 --> 00:07:50 is a, I've always been a big fan of the Scandinavian
00:07:50 --> 00:07:54 countries and Friska is a Nordic language for
00:07:54 --> 00:07:58 healthy. And so it's an artificial intelligence
00:07:58 --> 00:08:02 to build a healthy individual or a healthy society.
00:08:03 --> 00:08:06 And so that's the story behind the name and the
00:08:06 --> 00:08:11 company. Friska was more of an incident that
00:08:11 --> 00:08:15 happened in our journey of business. We initially
00:08:15 --> 00:08:18 built, we were building, as I mentioned to you,
00:08:18 --> 00:08:21 looking at data, looking at data points to see
00:08:21 --> 00:08:23 where are the challenges, how do we build capabilities
00:08:23 --> 00:08:26 to analyze the data and provide intelligence
00:08:26 --> 00:08:31 to leadership. One such area we found is endocrinology.
00:08:31 --> 00:08:35 Endocrinologists deal with people with comorbidities,
00:08:35 --> 00:08:39 and that comorbidity is what is driving most
00:08:39 --> 00:08:43 of the cost out there. So we started working
00:08:43 --> 00:08:46 with the endocrinologists, looking at the challenges
00:08:46 --> 00:08:50 they have, and from starting off with their electronic
00:08:50 --> 00:08:53 medical record or their operational system. So
00:08:53 --> 00:08:57 we built an EMR. specifically focused for endocrinologists
00:08:57 --> 00:09:01 called EndocPR. And once we deployed, we had
00:09:01 --> 00:09:04 the consulting of around 50 doctors, including
00:09:04 --> 00:09:08 one of my co -founder, Dr. George Grunberger,
00:09:08 --> 00:09:11 who was past president of the American Association
00:09:11 --> 00:09:15 of Ethical Endocrinologists. When we built and
00:09:15 --> 00:09:18 deployed, we came up on another problem, which
00:09:18 --> 00:09:21 is that patients get the recommendation from
00:09:21 --> 00:09:25 the physician, you make lifestyle changes. You
00:09:25 --> 00:09:27 may be active, and there's so many recommendations.
00:09:28 --> 00:09:31 But once the patient leaves that room, now suddenly
00:09:31 --> 00:09:34 they have to start finding, where do I find,
00:09:34 --> 00:09:37 how do I manage my nutrition? How do I manage
00:09:37 --> 00:09:41 my day? What kind of activity? I mean, you as
00:09:41 --> 00:09:43 a physician, you don't have a lot of time to
00:09:43 --> 00:09:46 spend with the patient, right? Maybe 10 minutes,
00:09:46 --> 00:09:49 maybe 20 minutes, or maximum. So there is time.
00:09:49 --> 00:09:53 So the doctors can't provide these services or
00:09:53 --> 00:09:56 consultation for this. patient on an ongoing
00:09:56 --> 00:10:01 basis as much as it is required. So the Friska
00:10:01 --> 00:10:06 AI is an outcome from it to find, build capabilities
00:10:06 --> 00:10:12 that augments a doctor or physician and provides
00:10:12 --> 00:10:16 this continuum of care and resources and knowledge
00:10:16 --> 00:10:20 for the patient so that it influence them to
00:10:20 --> 00:10:23 make better decisions, better choices. provide
00:10:23 --> 00:10:29 the necessary knowledge and tools to have a healthier
00:10:29 --> 00:10:33 and more robust life. Okay, very interesting
00:10:33 --> 00:10:37 to know and also the hardcore capabilities of
00:10:37 --> 00:10:41 the Fisker AI, how that can impact and support
00:10:41 --> 00:10:44 people who are seeking help with their preventive
00:10:44 --> 00:10:47 care and health as well. Can I ask you, in the
00:10:47 --> 00:10:50 topic of preventive care, why Fisker AI is an...
00:10:50 --> 00:10:52 important development in preventive care and
00:10:52 --> 00:10:57 how does this actually help people? Yeah, I mean,
00:10:57 --> 00:11:02 you probably as a physician know that there's
00:11:02 --> 00:11:05 so many systems a patient uses. So you might
00:11:05 --> 00:11:08 have a primary care physician, they might go
00:11:08 --> 00:11:11 to a pulmonologist, a patient goes to... So all
00:11:11 --> 00:11:14 these data are in different systems, in silos.
00:11:14 --> 00:11:17 They don't communicate with each other. So when
00:11:17 --> 00:11:20 these data are in silos, it's almost like a blind
00:11:20 --> 00:11:23 spot for the physician. They don't have the whole
00:11:23 --> 00:11:27 view of the patient. So Friska solves some of
00:11:27 --> 00:11:32 those challenges to bring data visibility of
00:11:32 --> 00:11:36 the patients, the overall health picture into
00:11:36 --> 00:11:40 a view for the physicians, one. Second, Friska
00:11:40 --> 00:11:45 as an AI engine. with multiple facets, analyzes
00:11:45 --> 00:11:49 the data, provides more personalized, aware of
00:11:49 --> 00:11:53 the patient, recommended treatment plans to the
00:11:53 --> 00:11:55 doctor so that the doctor can use that knowledge
00:11:55 --> 00:11:59 that the AI generates to provide a more personalized,
00:12:00 --> 00:12:03 impactful care to the patient. But it also provides
00:12:04 --> 00:12:07 resources to the patient to manage those conditions
00:12:07 --> 00:12:11 because of this combination of this so you identify
00:12:11 --> 00:12:15 you let's say somebody has a condition either
00:12:15 --> 00:12:17 they are diabetic or let's say somebody says
00:12:17 --> 00:12:21 you are no pre -diabetic you provide capabilities
00:12:21 --> 00:12:25 resources so that the patient is able to manage
00:12:25 --> 00:12:28 the condition and they don't develop more chronic
00:12:28 --> 00:12:32 So being more sick with more comorbidities, but
00:12:32 --> 00:12:35 even if you have comorbidities, helping the patient
00:12:35 --> 00:12:38 to manage it better and for the physicians to
00:12:38 --> 00:12:42 identify patients who are at risk early so that
00:12:42 --> 00:12:45 they can intervene to help their patient to be
00:12:45 --> 00:13:01 more healthier. to the process of starting from
00:13:01 --> 00:13:04 beginning to using the app or using the program,
00:13:04 --> 00:13:07 how does it go through? What does a typical user
00:13:07 --> 00:13:10 needs to do when he or she encounters with Friska
00:13:10 --> 00:13:17 AI? Yeah. Friska AI is a precision -driven application.
00:13:18 --> 00:13:21 A typical use case would be a patient comes to
00:13:21 --> 00:13:26 see a doctor. Doctor evaluates the patient. And
00:13:26 --> 00:13:29 says, no, probably you would be get help with
00:13:29 --> 00:13:33 Friska. We provide you more recommendations in
00:13:33 --> 00:13:36 how to manage your meal plan, get you health
00:13:36 --> 00:13:39 alerts, connects with your wearables, whether
00:13:39 --> 00:13:42 it's a continuous glucose monitor or Apple Health,
00:13:42 --> 00:13:46 Google Health. It also acts as an interface to
00:13:46 --> 00:13:48 the physician so that they can have a continuum
00:13:48 --> 00:13:51 of care. The patient says, okay, I would like
00:13:51 --> 00:13:55 to use it. Then the doctor's office. It's the
00:13:55 --> 00:13:57 place where the patient is enrolled into Friska.
00:13:57 --> 00:14:01 There's no direct patient to Friska. So then
00:14:01 --> 00:14:05 once the patient is enrolled, they get login
00:14:05 --> 00:14:07 credentials directly from the doctor's office,
00:14:07 --> 00:14:10 automatically generated to their email. And they
00:14:10 --> 00:14:13 can download the application, create their health
00:14:13 --> 00:14:17 profile, connect with Apple Health or Google
00:14:17 --> 00:14:21 Health, connect with Dexcom, continuous glucose
00:14:21 --> 00:14:24 monitors. Then it starts to give them based on
00:14:24 --> 00:14:28 their health condition, based on the input and
00:14:28 --> 00:14:32 the variables and the treatment plans, daily
00:14:32 --> 00:14:35 meal plan. It is automatically created, recommended
00:14:35 --> 00:14:38 meal plan, and they can customize their meal
00:14:38 --> 00:14:41 plan choices, health alerts if your heart rate
00:14:41 --> 00:14:44 goes up, or even based on their activities and
00:14:44 --> 00:14:47 the variable data, it starts to customize their
00:14:47 --> 00:14:50 meal plan, gives them daily recommended set of
00:14:50 --> 00:14:54 activities throughout the day. And also sometimes
00:14:54 --> 00:14:57 maybe somebody has an issue sleeping or stress.
00:14:57 --> 00:15:01 And so activities to manage stress, improve sleep.
00:15:01 --> 00:15:07 So it becomes companion. Such a clever and wonderful
00:15:07 --> 00:15:10 innovation by you and your team, Shahjana, that
00:15:10 --> 00:15:13 both actually is very helpful for physicians,
00:15:13 --> 00:15:16 but also for the patients to keep them in track,
00:15:16 --> 00:15:18 to let them know the best practices, the important
00:15:18 --> 00:15:21 information. It's very helpful to have all that
00:15:21 --> 00:15:25 in your hand or all that accessible in one place
00:15:25 --> 00:15:29 and to follow those. I know artificial intelligence
00:15:29 --> 00:15:32 and in this era has come a very long way. Studies
00:15:32 --> 00:15:36 show that preventive care is essential for promoting
00:15:36 --> 00:15:40 longevity, reducing health care costs and minimizing
00:15:40 --> 00:15:43 visits. But preventive medicine is not widely
00:15:43 --> 00:15:46 practiced. So what do you feel from your experiences?
00:15:46 --> 00:15:51 What barriers stand in the way? of broader adoption
00:15:51 --> 00:15:57 of preventive health? Oh, okay. So that's a fantastic
00:15:57 --> 00:16:01 question. I think it's the knowledge, access
00:16:01 --> 00:16:06 to knowledge, access to information. And this
00:16:06 --> 00:16:10 might sound a little taboo, but healthcare or
00:16:10 --> 00:16:13 healthcare providers are, after the lawyers,
00:16:13 --> 00:16:17 not the most conduit for innovation. So they
00:16:17 --> 00:16:21 study. certain set of principles and they follow
00:16:21 --> 00:16:24 that. Doctors are not very innovative on their
00:16:24 --> 00:16:27 day -to -day work, right? They follow regulations
00:16:27 --> 00:16:30 and certain set of principles, which is fantastic.
00:16:30 --> 00:16:34 But if nothing has in human history have changed,
00:16:34 --> 00:16:36 unless there is an innovative out -of -the -box
00:16:36 --> 00:16:39 approach to problems. But in our experience,
00:16:39 --> 00:16:44 we found a lot of doctors and practices. who
00:16:44 --> 00:16:47 are willing to look at more innovative ways of
00:16:47 --> 00:16:51 approaching it. And our goal has been to provide
00:16:51 --> 00:16:55 capabilities that's technology -driven, scalable,
00:16:55 --> 00:16:58 so that with a small number of people, you can
00:16:58 --> 00:17:00 actually deliver impact for a more population.
00:17:03 --> 00:17:06 Also, no tricks. It's very simple. And everybody
00:17:06 --> 00:17:09 knows you eat better and be active. And there
00:17:09 --> 00:17:11 is a positive outcome from it. You don't do any
00:17:11 --> 00:17:16 magic pill or any shock. All we do is well -known
00:17:16 --> 00:17:19 knowledge using the power of technology and AI
00:17:19 --> 00:17:24 to market personalized health care in the fingertip
00:17:24 --> 00:17:28 of the patient. Yes, indeed. Preventive health
00:17:28 --> 00:17:31 practices should be... in broader implementation.
00:17:31 --> 00:17:34 And it's very important for physicians and health
00:17:34 --> 00:17:36 professionals to keep up with the best practices.
00:17:37 --> 00:17:39 It's also sometimes very challenging because
00:17:39 --> 00:17:42 they are busy individuals. They have a lot of
00:17:42 --> 00:17:46 responsibilities. It's very difficult for sometimes
00:17:46 --> 00:17:49 to keep up with the best practices. But also
00:17:49 --> 00:17:52 we know there are sometimes continuing competence
00:17:52 --> 00:17:55 requirements that all regular professions like
00:17:55 --> 00:17:58 sometimes need to follow. So that is one sort
00:17:58 --> 00:18:00 of keeping an oversight, having the accountability
00:18:00 --> 00:18:04 to follow up or having to stay updated with the
00:18:04 --> 00:18:07 best practices in the specific health world.
00:18:07 --> 00:18:09 We are talking about artificial intelligence
00:18:09 --> 00:18:13 a lot today. I want to ask you, you are in the
00:18:13 --> 00:18:15 artificial intelligence realm. You are working
00:18:15 --> 00:18:19 with integrating AI with preventive care in the
00:18:19 --> 00:18:23 health realm. Can you let me know how is artificial
00:18:23 --> 00:18:27 intelligence AI is transforming? healthcare and
00:18:27 --> 00:18:31 preventive care in particular? Yeah, because
00:18:31 --> 00:18:35 there is so much data in healthcare. It's starting
00:18:35 --> 00:18:39 to build more and more data. A patient might
00:18:39 --> 00:18:45 have digital heart monitors, digital health wearable
00:18:45 --> 00:18:49 devices, continuous glucose monitors, insulin
00:18:49 --> 00:18:54 pump. to the tracking other activities automatically
00:18:54 --> 00:18:59 captured. All this have impact in how patients'
00:19:00 --> 00:19:04 health outcomes are or how that improve. And
00:19:04 --> 00:19:08 it is humanly impossible to digest all the information.
00:19:08 --> 00:19:12 This is where the power of AI comes in to analyze
00:19:12 --> 00:19:16 individual health data, whether they come from
00:19:16 --> 00:19:21 multiple sources to analyze. personalize and
00:19:21 --> 00:19:26 build health capabilities. So this acts as a
00:19:26 --> 00:19:30 tool helping the physicians to understand their
00:19:30 --> 00:19:34 patient better so that they design the treatment
00:19:34 --> 00:19:37 plan or provide the treatment in a more intelligent,
00:19:37 --> 00:19:40 more informative way so that they have these
00:19:40 --> 00:19:42 summaries. So for example, if a patient comes
00:19:42 --> 00:19:45 and the doctor asking the question, what have
00:19:45 --> 00:19:47 you done in the last three months or six months?
00:19:47 --> 00:19:50 What's been some of your challenge? You look
00:19:50 --> 00:19:53 at the chart, you suddenly see the AI has shown
00:19:53 --> 00:19:56 these are the times the patient had a higher
00:19:56 --> 00:19:59 heart rate or glucose elevated levels. Are they
00:19:59 --> 00:20:02 primarily in the mornings or evenings? So they
00:20:02 --> 00:20:04 could ask more intelligent questions. It looks
00:20:04 --> 00:20:07 like in March, this period, your heart rate or
00:20:07 --> 00:20:10 blood glucose was high. What were you doing at
00:20:10 --> 00:20:13 that time? It's much more intelligent from that
00:20:13 --> 00:20:16 aspect. It empowers the doctors to understand
00:20:16 --> 00:20:19 the patient much better. but also a continuation
00:20:19 --> 00:20:22 of care where the patient gets these recommendations
00:20:22 --> 00:20:25 on a daily basis. So this cycle is going there.
00:20:26 --> 00:20:31 So where I think the AI would help or move is
00:20:31 --> 00:20:37 what we call this no desert of care. In any country
00:20:37 --> 00:20:41 you go, metropolis one way or the other, or in
00:20:41 --> 00:20:43 cities you have doctors. But there are a lot
00:20:43 --> 00:20:45 of people who live far away from the cities.
00:20:46 --> 00:20:48 they can't find a specialist or a care partner
00:20:48 --> 00:20:52 as as easy as you find in city so we are also
00:20:52 --> 00:20:56 looking at those people population whether they
00:20:56 --> 00:20:58 are in a developed country or in places like
00:20:58 --> 00:21:02 no where i originally came from india where there
00:21:02 --> 00:21:04 is so many people who may not have access to
00:21:04 --> 00:21:08 healthcare so but we want to make healthcare
00:21:08 --> 00:21:12 expertise healthcare available in the hands of
00:21:12 --> 00:21:18 people at an affordable or as near close to zero
00:21:18 --> 00:21:21 cost as possible. And I think AI would be that
00:21:21 --> 00:21:26 driving force in making the health equity or
00:21:26 --> 00:21:30 access to health care for everyone. That's wonderful,
00:21:30 --> 00:21:34 Shaji, and a lot of food for thought on how AI
00:21:34 --> 00:21:38 can actually complement and help health care
00:21:38 --> 00:21:42 and the health perspectives among patients, but
00:21:42 --> 00:21:45 also at the same time. support physicians and
00:21:45 --> 00:21:47 like these individuals helping to understand
00:21:47 --> 00:21:51 what the patient is going through and how actually
00:21:51 --> 00:21:53 that coordinates with the patients we are talking
00:21:53 --> 00:21:56 about friska ai today just from name we understand
00:21:56 --> 00:22:00 friska ai is a artificial intelligence model
00:22:00 --> 00:22:04 and a tool so how does friska ai actually leverages
00:22:04 --> 00:22:08 artificial intelligence to help with preventive
00:22:08 --> 00:22:17 care yeah so Friska is a platform which leverages
00:22:17 --> 00:22:22 AI to deliver personalized healthcare for individuals.
00:22:23 --> 00:22:26 When I say platform, it has many components to
00:22:26 --> 00:22:30 it and we call them individual engines. So you
00:22:30 --> 00:22:34 could have one engine, which we call it NutriAI,
00:22:35 --> 00:22:38 Friska NutriAI. So what Friska NutriAI does
00:22:38 --> 00:22:43 is it provides day -to -day meal plan for individuals
00:22:43 --> 00:22:47 based on their health conditions their all the
00:22:47 --> 00:22:50 various factors the medicine they take blood
00:22:50 --> 00:22:53 work they've done vitamin deficiency based on
00:22:53 --> 00:22:56 this and obviously people now have allergies
00:22:56 --> 00:23:03 or preferred cuisine vegetarian vegan all the
00:23:03 --> 00:23:07 variations of it and our nutri ai engine will
00:23:07 --> 00:23:11 provide them automatically a meal plan on a daily
00:23:11 --> 00:23:14 basis so for tomorrow's meal plan we deliver
00:23:14 --> 00:23:17 the meal plan today so by afternoon you get a
00:23:17 --> 00:23:19 meal plan which will help you to plan what your
00:23:19 --> 00:23:22 meals should be for tomorrow so that's our new
00:23:22 --> 00:23:26 3ai engine then on the other side we also have
00:23:26 --> 00:23:29 what is called a clinical decision engine so
00:23:29 --> 00:23:32 which analyzes the patient data from various
00:23:32 --> 00:23:35 no data points for the patient if they have gone
00:23:35 --> 00:23:40 to a cardiologist etc that brings the data to
00:23:40 --> 00:23:43 the doctors when the doctor is seeing the patient
00:23:43 --> 00:23:46 in the patient chart it shows know what the what
00:23:46 --> 00:23:49 has been the result of their ophthalmologist
00:23:49 --> 00:23:52 consultation do they have an issue with their
00:23:52 --> 00:23:57 no eye or any impact from if somebody is diabetic
00:23:57 --> 00:24:01 regular eye exam there is a podiatrist consultation
00:24:01 --> 00:24:04 he says there been an audiologist consultation
00:24:04 --> 00:24:06 have they given any prescription medication so
00:24:06 --> 00:24:10 that It provides the doctor, whether it's a family
00:24:10 --> 00:24:12 practice or endocrinologist, or it could be any
00:24:12 --> 00:24:15 other doctor, looking at the patient chart, the
00:24:15 --> 00:24:18 AI generates these notes or recommendations to
00:24:18 --> 00:24:23 say, recently patient had a higher heart rate
00:24:23 --> 00:24:25 and the doctor cardiologist has prescribed this
00:24:25 --> 00:24:30 medication. And also to say to the patient, because
00:24:30 --> 00:24:32 you have higher heart rate and the glucose, you
00:24:32 --> 00:24:35 might want to keep a snack near your bed before
00:24:35 --> 00:24:38 you go to sleep. this is one side it's a recommendation
00:24:38 --> 00:24:41 to the patient to manage their health on the
00:24:41 --> 00:24:45 other side is providing intelligent notes and
00:24:45 --> 00:24:47 summary of the patient condition to the doctor
00:24:47 --> 00:24:50 on an ongoing basis or when the patient is coming
00:24:50 --> 00:24:54 for the consultation right now the next iteration
00:24:54 --> 00:24:58 of our engine would give the capability for the
00:24:58 --> 00:25:03 patient to input data via audio right so you
00:25:03 --> 00:25:07 can say hey i ate this morning I had a toast
00:25:07 --> 00:25:11 with scrambled egg and a banana, then the application
00:25:11 --> 00:25:14 will get all the micronutrients and what they
00:25:14 --> 00:25:17 ate, then it will track their intake and provide
00:25:17 --> 00:25:21 personalized accommodations. And also it provides
00:25:21 --> 00:25:24 reminders when to take their prescription medications.
00:25:24 --> 00:25:27 And as a physician, a lot of complication for
00:25:27 --> 00:25:30 those with chronic conditions is that they miss
00:25:30 --> 00:25:33 medications. And sometimes those medications,
00:25:33 --> 00:25:36 we don't take them. builds more complexity so
00:25:36 --> 00:25:40 prescription reminders or to tell them it's time
00:25:40 --> 00:25:44 to nap or walk instead of that we do is provide
00:25:44 --> 00:25:47 them a specific set of exercises to do like you've
00:25:47 --> 00:25:49 been sitting for the last 30 minutes why don't
00:25:49 --> 00:25:52 we do a 30 second stretch exercise or maybe going
00:25:52 --> 00:25:55 before going to bed you want to have a greeting
00:25:55 --> 00:25:57 exercise just so that we calm down and get ready
00:25:57 --> 00:26:00 for the bed so these are all instructions and
00:26:00 --> 00:26:02 sometimes it's video sometimes it's more audio
00:26:03 --> 00:26:07 But this becomes a companion. And as I mentioned,
00:26:07 --> 00:26:09 next month onwards, when we release, it will
00:26:09 --> 00:26:13 be a kind of a voice companion for the user.
00:26:13 --> 00:26:17 Prisca will talk to the individual. And that
00:26:17 --> 00:26:21 we started working on through the last year.
00:26:21 --> 00:26:24 We had a lot of feedback. A lot of our patients
00:26:24 --> 00:26:29 are lonely. And especially as you live in Canada
00:26:29 --> 00:26:33 or even if it's winter. It's very tough when
00:26:33 --> 00:26:36 we don't just go out somewhere just for a drive
00:26:36 --> 00:26:39 because it's cold. So there are many months you
00:26:39 --> 00:26:42 are lonely and you have very limited interaction.
00:26:43 --> 00:26:46 So we wanted the app to be like a companion where
00:26:46 --> 00:26:49 they can ask questions, get answers, and kind
00:26:49 --> 00:26:53 of build that interaction. For example, if I
00:26:53 --> 00:26:55 am the patient to ask, how was your food? Did
00:26:55 --> 00:26:57 you like your meal plan yesterday? What could
00:26:57 --> 00:27:00 we change? Did you like that recommendation or
00:27:00 --> 00:27:03 not? So the AI can start. picking clues from
00:27:03 --> 00:27:06 that conversation and it can start building more
00:27:06 --> 00:27:10 personalized recommendations including some simple
00:27:10 --> 00:27:15 tips like know how to prevent fall at your home
00:27:15 --> 00:27:17 like oh yeah let's say you get up in the night
00:27:17 --> 00:27:20 and you're trying to go i'm going to say hey
00:27:20 --> 00:27:24 taji no you just woke up from the bed do you
00:27:24 --> 00:27:27 want to take a two minute to 10 second breather
00:27:27 --> 00:27:29 before you start walking to the restroom right
00:27:29 --> 00:27:33 So those kinds of things, the AI as a tool and
00:27:33 --> 00:27:38 capability can be a good companion, more like
00:27:38 --> 00:27:41 health companion. I can envision in the next
00:27:41 --> 00:27:45 five, 10 years, AI would be a predominant capability
00:27:45 --> 00:27:49 for taking care of people, their health, and
00:27:49 --> 00:27:53 even a future of AI looks like it would be a
00:27:53 --> 00:27:55 health assistant for those who need at their
00:27:55 --> 00:28:00 home. Indeed. I just want to let Shaji just say
00:28:00 --> 00:28:02 from hearing you about Friska, this is a very
00:28:02 --> 00:28:06 intelligent model. And I can just imagine that
00:28:06 --> 00:28:09 the amount of work that went into making it and
00:28:09 --> 00:28:13 training it to react in the way that it is doing
00:28:13 --> 00:28:15 right now. And all the engines that you spoke
00:28:15 --> 00:28:18 of, it's very helpful. Even having the nutrient
00:28:18 --> 00:28:21 engine, the clinical engine, supporting both
00:28:21 --> 00:28:24 patients, but also the physicians generating
00:28:24 --> 00:28:27 information. and recommendations automatically.
00:28:28 --> 00:28:31 And also the last part I really liked when you
00:28:31 --> 00:28:35 mentioned that now it would act as a voice companion
00:28:35 --> 00:28:39 with patients and just some extraordinary features
00:28:39 --> 00:28:44 that Friska AI possesses. There are many health
00:28:44 --> 00:28:47 applications. There are some health AI platforms
00:28:47 --> 00:28:51 out there in the market, in the app store. There
00:28:51 --> 00:28:53 are mobile fitness apps and other help sets.
00:28:53 --> 00:28:56 I actually support with... some vital functions
00:28:56 --> 00:29:00 like measuring heart rate, hydration, steps,
00:29:00 --> 00:29:05 sleep, diet, all these things. So how is Friska
00:29:05 --> 00:29:08 AI different from all these other platform or
00:29:08 --> 00:29:13 tools or what actually makes this a unique platform
00:29:13 --> 00:29:19 for women and health? Oh, thank you. So if you
00:29:19 --> 00:29:23 notice it, there are primarily... two sets of
00:29:23 --> 00:29:27 applications available in the market, or at least
00:29:27 --> 00:29:30 within the space. One is what I would call mostly
00:29:30 --> 00:29:35 monitoring devices, right? So these are heart
00:29:35 --> 00:29:38 rate monitors or blood pressure monitors, or
00:29:38 --> 00:29:43 you have BMI or blood oxygen, these kind of variable
00:29:43 --> 00:29:47 monitoring devices, including CG apps, right?
00:29:47 --> 00:29:51 They provide you information. to the patients,
00:29:51 --> 00:29:56 and the patient has to make, based on the information,
00:29:56 --> 00:30:00 delegate choice. And the other side of it is
00:30:00 --> 00:30:04 there's a lot of these health applications, which
00:30:04 --> 00:30:07 are fitness apps, recommendations. There are
00:30:07 --> 00:30:10 what is called the AI Rack. They are the front
00:30:10 --> 00:30:13 end of it, but back end, they are using platforms
00:30:13 --> 00:30:18 like ChatGPT or some of the other tools. And
00:30:18 --> 00:30:21 they wrap them around a unique use case and they
00:30:21 --> 00:30:24 deliver. And they're fantastic, right? In our
00:30:24 --> 00:30:27 case, our engines are our own. So that means
00:30:27 --> 00:30:30 we build the engine. Our LLMs, we are not connected
00:30:30 --> 00:30:33 to any other third party. So we trained and we
00:30:33 --> 00:30:36 built the engine. So this continuously learns
00:30:36 --> 00:30:40 and evolves through that. Second is, there is
00:30:40 --> 00:30:44 a lot of monitoring capability out there. As
00:30:44 --> 00:30:46 two of the biggest companies in the world, both
00:30:46 --> 00:30:49 Apple and Google are building more of these capabilities.
00:30:49 --> 00:30:53 So what we want to do is build those monitoring
00:30:53 --> 00:30:56 capability. That's one set of input for the patient.
00:30:56 --> 00:30:59 The other set is the consultations that doctors
00:30:59 --> 00:31:02 provides and the recommendation. Then there is
00:31:02 --> 00:31:05 no sets of lab results. There are pharmacies,
00:31:05 --> 00:31:09 the prescription. The patient using all those
00:31:09 --> 00:31:13 monitoring. descriptive and physician consultation,
00:31:13 --> 00:31:17 expert consultation, everything into then using
00:31:17 --> 00:31:21 AI to build more personalized healthcare and
00:31:21 --> 00:31:24 preventive care capabilities using our own labs.
00:31:24 --> 00:31:26 So that's why we have different pieces of this
00:31:26 --> 00:31:30 engine, nutrition engine, clinical decision engine.
00:31:30 --> 00:31:34 And we are also working on a diagnosis capability.
00:31:34 --> 00:31:37 That means we are testing out the hypothesis
00:31:37 --> 00:31:42 around. based on your imaging can we detect possible
00:31:42 --> 00:31:45 cancer or some other possibilities but that's
00:31:45 --> 00:31:48 much further down the line that requires a tremendous
00:31:48 --> 00:31:51 amount of testing but at least that's if even
00:31:51 --> 00:31:54 if it not as somebody else would definitely be
00:31:54 --> 00:31:57 doing that but that is these engines what we
00:31:57 --> 00:32:00 develop is becoming more smarter and more efficient
00:32:00 --> 00:32:06 and faster so that means i can deliver a personalized
00:32:06 --> 00:32:10 meal plan for a million people in few seconds
00:32:10 --> 00:32:14 or in less than a minute. Imagine the level of
00:32:14 --> 00:32:18 effort and the nutritionist, the amount of consultation
00:32:18 --> 00:32:20 you need to generate, just that piece of it.
00:32:20 --> 00:32:23 So I think the uniqueness has come from bringing
00:32:23 --> 00:32:28 all the monitoring capability that exists in
00:32:28 --> 00:32:31 the market and building intelligence from it.
00:32:32 --> 00:32:34 but building our own engine to have a comprehensive
00:32:34 --> 00:32:38 view of individual's health and providing guidance
00:32:38 --> 00:32:42 to the individuals to how to manage it, but providing
00:32:42 --> 00:32:44 that intelligence to the doctors in terms of
00:32:44 --> 00:32:47 making better clinical decision and diagnostic
00:32:47 --> 00:32:52 decisions. Thank you so much for very wonderfully
00:32:52 --> 00:32:55 explaining that, Shachi, and letting me know
00:32:55 --> 00:32:59 and our users know how actually Fiskeye is a
00:32:59 --> 00:33:01 very unique platform. health platform that actually
00:33:01 --> 00:33:06 stands out from the other health platforms or
00:33:06 --> 00:33:09 AI models that is currently available in the
00:33:09 --> 00:33:13 space. I'm really impressed to know what capabilities
00:33:13 --> 00:33:17 Friska actually possess. And it's very intriguing
00:33:17 --> 00:33:20 how it is helping patients and the different
00:33:20 --> 00:33:23 engines that you're speaking of. Very impressive
00:33:23 --> 00:33:27 that this complete system is built by your own
00:33:27 --> 00:33:31 team. And just like you mentioned, only generating
00:33:31 --> 00:33:35 a nutrition model or a diet plan, the amount
00:33:35 --> 00:33:38 of work that went into it is, you can imagine
00:33:38 --> 00:33:41 like how much work that was put into make it
00:33:41 --> 00:33:44 a perfectionist. Can I ask you, so this model,
00:33:44 --> 00:33:48 is this available to patients right now? Oh,
00:33:48 --> 00:33:51 yes. Yes. We, as I mentioned, we deliver this
00:33:51 --> 00:33:54 through, you know, physicians. In our case, we
00:33:54 --> 00:33:56 are only deploying through endocrinologists.
00:33:57 --> 00:34:00 Yes, there are physicians and practices using.
00:34:00 --> 00:34:04 We are also working with a couple of large EMRs
00:34:04 --> 00:34:07 in the U .S. So Fisker will be available. Part
00:34:07 --> 00:34:11 of the EMRs, the doctors or the patients are
00:34:11 --> 00:34:13 using. So that would be our next scalable deployment.
00:34:14 --> 00:34:17 We would have a few large population deployments
00:34:17 --> 00:34:21 in the next few months. That's very exciting.
00:34:21 --> 00:34:25 Can you tell me, how are patients actually reacting
00:34:25 --> 00:34:29 to Fisker AI at this moment? Oh, should this
00:34:29 --> 00:34:32 just, no, this is what, I'm not sure it's the
00:34:32 --> 00:34:35 right comparison. It is almost like going on
00:34:35 --> 00:34:38 a date and you first, the patient, this is like,
00:34:39 --> 00:34:42 okay, I'm interested. What is this? There's some
00:34:42 --> 00:34:46 keywords, stuff like AI application, personalized
00:34:46 --> 00:34:49 care. Then the question becomes like, I know
00:34:49 --> 00:34:52 how much it's going to cost me. Our model is
00:34:52 --> 00:34:55 patient doesn't pay anything. It's built to an
00:34:55 --> 00:34:58 insurance. So there's no cost directly to the
00:34:58 --> 00:35:01 patient. The only qualifier for a Friska is the
00:35:01 --> 00:35:05 patient is qualified to get that service or they
00:35:05 --> 00:35:07 have to have comorbidities. Yeah, correct. So
00:35:07 --> 00:35:11 it's a determination made by the physician and
00:35:11 --> 00:35:14 the doctor. So once they're enrolled, initially,
00:35:14 --> 00:35:18 anyone who has not done this kind of activities
00:35:18 --> 00:35:21 for their life, they would have questions how
00:35:21 --> 00:35:24 to use it, how to connect with the variables.
00:35:24 --> 00:35:28 how to track, and when you get the meal plans.
00:35:29 --> 00:35:32 I mean, I was surprised. A lot of patients called
00:35:32 --> 00:35:36 to say, I am getting my meal plan on my email
00:35:36 --> 00:35:39 also. Are you going to charge me more for it?
00:35:40 --> 00:35:43 We have to say, no, no. I mean, we stand by the
00:35:43 --> 00:35:45 statement, you have no cost to it. There's nothing
00:35:45 --> 00:35:48 hidden here. And it is not typically built because
00:35:48 --> 00:35:52 you have chronic condition. This is no program
00:35:52 --> 00:35:54 to help you manage your chronic condition. So
00:35:54 --> 00:35:57 somebody said, I don't want meal plans sent to
00:35:57 --> 00:35:59 me every day and you're billing me this much
00:35:59 --> 00:36:01 end of the month. I said, no, there's no bill
00:36:01 --> 00:36:05 for you. It's part of the physician's deployment.
00:36:05 --> 00:36:09 So it's been both learning and educating. So
00:36:09 --> 00:36:11 we are continuing to educate the patients the
00:36:11 --> 00:36:14 value and how to use it. But then for us, there
00:36:14 --> 00:36:17 are also a lot of value change, right? We initially
00:36:17 --> 00:36:20 planned. Some, we have a, we, the platform offers
00:36:20 --> 00:36:23 a lot of live sessions with yoga instructors
00:36:23 --> 00:36:26 or fitness instructors, or even with the nutrition.
00:36:26 --> 00:36:28 Yeah, there are live sessions also. You can join
00:36:28 --> 00:36:31 the session, ask questions through the platform.
00:36:31 --> 00:36:34 So it's interactive, but it also has video. So
00:36:34 --> 00:36:38 when people ask, then we learn that some of them
00:36:38 --> 00:36:41 don't have time during the weekdays. So they
00:36:41 --> 00:36:44 will have weekends. And sometimes we learn a
00:36:44 --> 00:36:47 lot of teachers. go to work very early. If you're
00:36:47 --> 00:36:50 teaching in high school, you're probably, you
00:36:50 --> 00:36:52 know, leave, if you're a little away from your
00:36:52 --> 00:36:55 school, you're probably leaving like 5, 5 .30
00:36:55 --> 00:36:58 from your home to reach the school. So they want
00:36:58 --> 00:37:01 to have sessions early modeling. So we kind of
00:37:01 --> 00:37:04 started to iterate. So we are learning both of
00:37:04 --> 00:37:07 it. I mean, I'm not saying we figured out everything
00:37:07 --> 00:37:11 from a delivery model, but I think what patients
00:37:11 --> 00:37:14 who are using it would say is that we will do
00:37:14 --> 00:37:17 it. anything to make them happy, excited, be
00:37:17 --> 00:37:21 flexible enough to meet their time goals. So
00:37:21 --> 00:37:24 we learn, we have this system, what we call anything
00:37:24 --> 00:37:27 we do, we get a feedback mechanism. So like even
00:37:27 --> 00:37:30 the meal plan, when we deliver, there is a portion
00:37:30 --> 00:37:33 for them to provide us a feedback. So we are
00:37:33 --> 00:37:36 continuing to learn from the patients and iterating
00:37:36 --> 00:37:39 how we do, how we deliver timings and all those
00:37:39 --> 00:37:43 things. So the patients appreciate that. But
00:37:43 --> 00:37:46 there are also patients who are learning how
00:37:46 --> 00:37:49 to use that, right? Sometimes they have schedules
00:37:49 --> 00:37:51 in the session. They don't know how to, which
00:37:51 --> 00:37:54 button to click to join a session. But those
00:37:54 --> 00:37:56 are some things we are working to. So we are
00:37:56 --> 00:38:00 improving our ability to give them training and
00:38:00 --> 00:38:06 those kinds of information. So every day we work
00:38:06 --> 00:38:09 with the motto, ask at the end of the day, what
00:38:09 --> 00:38:11 value we deliver to our users. And that's been
00:38:11 --> 00:38:14 our goal. So we ask that on a daily basis. So
00:38:14 --> 00:38:18 we move our goalposts based on that. That's wonderful.
00:38:18 --> 00:38:22 And thank you so much for explaining, Shaji,
00:38:22 --> 00:38:25 this wonderful innovation, Freescale AI. And
00:38:25 --> 00:38:27 I feel like this should be easily available.
00:38:28 --> 00:38:30 This should be within the reach of physicians
00:38:30 --> 00:38:33 and as much as patients as possible, because
00:38:33 --> 00:38:35 it's truly helping with their daily lifestyle.
00:38:35 --> 00:38:39 And with that companion part kicking in in the
00:38:39 --> 00:38:42 future days, it's going to be a... Jim, for sure.
00:38:42 --> 00:38:45 I'm sure my audiences would love to know more
00:38:45 --> 00:38:49 about Friska AI and about you as well. So I just
00:38:49 --> 00:38:51 want to let you know, dear audiences, if you
00:38:51 --> 00:38:55 do want to learn about Shaji Nair, you need to
00:38:55 --> 00:38:58 know more about Friska, how it can help you.
00:38:59 --> 00:39:02 You can definitely get in touch with Shaji if
00:39:02 --> 00:39:06 you go to our website, www .activeaction .fm
00:39:06 --> 00:39:09 and just search Shaji Nair's name. you will get
00:39:09 --> 00:39:13 access to his webpage. So there is their links
00:39:13 --> 00:39:17 for his socials and Friska, and you will find
00:39:17 --> 00:39:20 a way to contact him over there. And also, if
00:39:20 --> 00:39:23 you feel free to ask Shahji any questions, feel
00:39:23 --> 00:39:25 free to shoot me an email. I'll send them to
00:39:25 --> 00:39:30 his team as well. Thank you, Dr. Nassif. I appreciate
00:39:30 --> 00:39:34 your wonderful conversation we had. I want to
00:39:34 --> 00:39:36 leave with one thought. As I mentioned at the
00:39:36 --> 00:39:39 beginning, Friska is to solve some of the challenges
00:39:39 --> 00:39:43 in the health space. So your listeners, if they
00:39:43 --> 00:39:45 have a challenge or they think there is a problem
00:39:45 --> 00:39:48 they would like to address, I'm not promising
00:39:48 --> 00:39:51 a solution, but definitely we would like to take
00:39:51 --> 00:39:55 a stab at it. Please reach out to us. Thank you
00:39:55 --> 00:39:58 so much, Shaji. I really appreciate your openness
00:39:58 --> 00:40:02 to taking on how to improve Friska by taking
00:40:02 --> 00:40:05 those suggestions and feedback. Like I mentioned,
00:40:05 --> 00:40:07 dear listeners, if you have any, please feel
00:40:07 --> 00:40:10 free to reach out to Shaji and his team. Thank
00:40:10 --> 00:40:13 you again so much, Shaji, for joining this podcast.
00:40:13 --> 00:40:16 And I'll be in touch with you to learn more about
00:40:16 --> 00:40:19 this wonderful platform and looking forward to
00:40:19 --> 00:40:22 taking a better look at it. Dear listeners, we
00:40:22 --> 00:40:26 were talking to Shaji Nair and we got to learn
00:40:26 --> 00:40:29 a lot about Frisca AI today. If you have liked
00:40:29 --> 00:40:33 our episode. Please feel free to share this with
00:40:33 --> 00:40:37 your friends, families, or any other who might
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00:40:44 --> 00:40:48 Those are always very helpful. Last but not the
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