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The $45M Industrial AI Revolution | Daniel Raj David, CEO of Detect Technologies on BP2B S2 Ep.2

Welcome to the second episode of the second season of 'The Best Place to Build' podcast, recorded at the Center for Innovation in IIT Madras. In this episode, host Amrut sits down with Daniel, CEO and Founder of Detect Technologies, one of India’s fastest-growing industrial AI startups. The conversation explores how Detect leverages machine learning, computer vision, IoT sensors, and edge computing to transform industrial safety, workplace safety, and preventative maintenance on a global scale. Daniel explains how Detect Technologies connects to diverse data sources—ranging from visual feeds to IoT-enabled sensors—to deliver actionable AI insights that enhance operational efficiency and reduce risks. Their solutions directly address challenges in Industry 4.0, aligning with global OSHA safety standards and helping industries prevent costly failures, workplace injuries, and fatalities. Listeners will also hear Daniel’s inspiring journey—from being a student innovator at IIT Madras to leading a global company. He shares pivotal moments in Detect’s growth story, including their hardware-to-AI SaaS evolution, strategic partnerships, and funding milestones. The discussion highlights the importance of grit, co-founder support, and mentorship in scaling a startup. This episode is packed with insights for entrepreneurs and professionals curious about the future of AI in industry, maintenance optimization, and solving real-world challenges with deep tech. 👉 Tune in to discover how Detect Technologies is shaping the future of industrial AI, safety, and efficiency—and learn how passion, obsession, and the right ecosystem can turn breakthrough ideas into global impact. 00:00 Introduction 01:04 Welcome to the Best Place to Build Podcast 01:11 Introducing Daniel from Detect Technologies 01:32 Understanding Detect Technologies' Mission and Operations 03:17 Industrial Safety and AI Interventions 07:07 Real-World Applications and Impact 09:02 Origins and Development of Detect Technologies 17:18 Student Life and Early Exposure to Industrial Challenges 26:25 Incorporating the Company and Future Prospects 28:33 The Fundraising Journey Begins 28:42 Early Days and Initial Pivots 31:45 The Impact of the Pandemic 34:31 Transition to a Global Player 37:01 Industry 4.0 and OSHA 40:51 Expanding Internationally 46:26 Funding Journey and Validation 48:38 Advice for Aspiring Entrepreneurs 55:03 The Role of Ecosystem and Mentorship 57:08 Closing Thoughts and Reflections

Daniel Raj DavidguestAmruthost
Aug 1, 20251h 0mWatch on YouTube ↗

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  1. 0:001:04

    Introduction

    1. DD

      Our mission as an organization is to make the industrial world a better place and a safer place through actionable artificial intelligence. In this ecosystem, we have technology, we have professors, we have industrial specialists, but our X factor is students who don't know that something can't be done. There are many levers in the ecosystem that can fuel that initiative. [upbeat music]

    2. AM

      Hi, this is Amrut. We are at IIT Madras, my alma mater, and India's top university for people who like to build. We are here to meet some builders, ask them: What are you building? What does it take to build? And what makes IIT Madras the best place to build? [upbeat music]

  2. 1:041:11

    Welcome to the Best Place to Build Podcast

    1. AM

      Hello, and welcome to the Best Place to Build podcast. This is season two. We are recording at the Center for Innovation in

  3. 1:111:32

    Introducing Daniel from Detect Technologies

    1. AM

      IIT Madras. Today with me is Daniel from Detect Technologies. Daniel, welcome.

    2. DD

      Oh, thank you. Thank you for having me, Amrut.

    3. AM

      Detect is one of the hottest, uh, IIT Madras startups of the last decade. Um, you've raised more than $40 million, and you have operations across the world. Um, and you're a, a, like, quiet startup, not too much noise. We don't see a lot of press releases.

  4. 1:323:17

    Understanding Detect Technologies' Mission and Operations

    1. AM

      Um, so maybe you can start by telling us, what does Detect do, and, uh, what is your presence worldwide?

    2. DD

      Firstly, thank you for having me. Uh, second, it's always good to, to give an introduction of, uh, Detect, right? So, um, I would say today, we- the company has gone through several evolutions in the time that we've existed, but today, our, our mission as an organization is to make sure or make the industrial world a better place and a safer place through actionable artificial intelligence, right? Actionable being the key word. We do this through a various set of technologies and means, right? But the overall summary of what we do is we built a central AI layer that can take in information, which can be visual from your cameras, time series data from sensors, as well as simple data that's, that's scribbled on notes and put into ERPs, right? And be able to process that for large industrial plants, as well as smaller fabrication yards as well, and put actionable information right in front of the frontline so that they can act on, let's say, something that is a life-saving risk that's happening at the facility. Um, and even post the required statistics to management so that you understand what's the culture of your company, how do I improve the efficiency of my company, the safety of my company, at scale, um, at an enterprise level. So we touch people both at the frontline all the way to the CXOs and the executives, um, of these organizations through very verticalized AI interventions, uh, from our end.

    3. AM

      Okay, hold on, hold on. That was a lot. Let me take it in.

    4. DD

      Sure.

  5. 3:177:07

    Industrial Safety and AI Interventions

    1. DD

      Sure.

    2. AM

      Um, so you said industrial AI, uh, i- in, uh, in for safety and efficiency?

    3. DD

      Yes.

    4. AM

      So let me, let me try and understand it in my way as best I can. I started my, uh, career in a factory.

    5. DD

      Okay.

    6. AM

      We used to produce detergent liquid. So there are multiple plants inside a factory. There are machines there, there are workers going from here to there. We had about 500 workers.

    7. DD

      Nice.

    8. AM

      So in a plant like that, um, the kind of safety issues I would be thinking about is maybe, and, uh, somebody falls down and then equipment falls on them, or a worker accidentally touches a machine at where it's hot or where it's moving, and they sort of hurt them- hurt themselves at a minor level-

    9. DD

      Yeah

    10. AM

      ... but at a major level, maybe lose a finger or fall down, have a fracture. Uh, there were no life-threatening kind of situations in our factory. Uh, I'm sure in bigger factories, somebody could die for sure.

    11. DD

      Yeah.

    12. AM

      So in a factory like this, what would Detect end up doing?

    13. DD

      Thank you. You, you asked, uh, a, a great question. Um, so in any factory, even as small as you s- said 500 people in your factory, right? There are factories with even just 30, 40 people-

    14. AM

      Mm

    15. DD

      ... all the way to factories that have thousands of people at their site. There are actually a set of, over time, iteratively, we as human civilization, have actually identified what are the risks and the scenarios that would lead to someone losing their finger or, worst case, fatality. Just a, a sad but interesting statistic is, in the frontline workforce today, there are close to about 6,000 people or 6,000 fatalities that happen on a daily basis.

    16. AM

      Who publishes that statistic?

    17. DD

      Uh, generally, uh, Gartner, McKinsey, consultants.

    18. AM

      Okay.

    19. DD

      Um, this is referenced, um-

    20. AM

      6,000 fatalities in factories daily across the world?

    21. DD

      Across the world.

    22. AM

      Or for-

    23. DD

      Across the world

    24. AM

      ... maybe 10 million workforce or may- maybe 100 million workforce. I don't know, what is the workforce?

    25. DD

      The workforce would be larger than, uh, 100 mil, actually.

    26. AM

      Okay.

    27. DD

      Um, but effectively, there's close to about 2 million deaths a year across all industries, right? Every single industrial segment from warehousing, ports, uh, fabrication, all the way to the biggest oil gas-

    28. AM

      Construction

    29. DD

      ... construction as well.

    30. AM

      Mm.

  6. 7:079:02

    Real-World Applications and Impact

    1. DD

      Detectt plugs into, let's say, in a facility like this, I think two or three of these cameras can cover the whole facility.

    2. AM

      It's a great point. We are sitting in a lab-

    3. DD

      Yes

    4. AM

      ... and, uh, there's welding happens sometimes here, and then, uh, they do gas cutting.

    5. DD

      Mm.

    6. AM

      Um, they do vacuum pump operation.

    7. DD

      Yeah.

    8. AM

      All of these operations have some risk associated with it, and, uh, maybe, uh, for the general public, we have all seen construction sites.

    9. DD

      Yes.

    10. AM

      Right? And we can see that there's a beam, and there is a girder being moved, or there's a hole that's been dug, which is six feet long, and some- something can happen.

    11. DD

      Exactly.

    12. AM

      Um, and obviously, industries are not so open and so risky, but still there is always a slight risk of something. So your technology is plugging into the cameras that's looking in-

    13. DD

      Yes

    14. AM

      ... and a- as per OSHA rules, recognizing, uh, those scenarios where something is risky.

    15. DD

      Yeah.

    16. AM

      Is that right?

    17. DD

      Exactly. I'll give you an example, right? Something that is also relatable. Ports. There's crane operations, cranes that take containers from ships into land, into lorries, right? The biggest risk over there in any of these ports is if the structure is not held properly-

    18. AM

      Mm

    19. DD

      ... sometimes these containers, when there are people walking under those containers, the containers actually fall on those individuals. Big risk-

    20. AM

      Mm

    21. DD

      ... right? Um, and the biggest contributor of deaths in that sector. So in those cases, not only does Detectt build the AI to detect whether people are under those loads-

    22. AM

      Mm

    23. DD

      ... but also integrate back to the machinery to tell the machinery it's detecting a person under that load, the machine needs to stop, right, for safe operation, and alarm needs to go so that the person goes back into safety. So it's not just detection, but also intervention into the real world, um, right, that, that Detectt does. And, uh, we've saved... or at least the technology with the information put at the right time, has, uh, saved multiple, or prevented multiple incidents as well as fatalities across the world. Uh-

    24. AM

      That's

  7. 9:0217:18

    Origins and Development of Detect Technologies

    1. AM

      amazing. Um, I'm so curious about how you got into this business. But before that, just to complete this, uh, your technology plugs into AI. You said camera feeds, but you also said some other feeds.

    2. DD

      Sensors.

    3. AM

      Uh, so these are sensors like?

    4. DD

      I'll give you a few examples. So, uh, large complex industries like oil and gas have a multitube- multitude of data sources giving lots of information. Um, the last couple of decades, the industry has spent on investing in infrastructure, right? Um, so it can be things like gas sensors, it can be pressure sensors to detect pressure changes within the process fluids that are being sent across the facility. Um, and also, there are also ultrasonic sensors. So there are multiple use cases in each of them. I'll give you one example of a technology that was invented here in IIT Madras, right? So it's a technology called GUMPS, where effectively we have the patent for the first sensor in the world that could generate [ sonoquisition sounds] signals across a pipe at high temperatures. Why is that important, right? Just to give you an example, have you done an ultrasound before? I'm sure you have. We needed to do it before-

    5. AM

      Oh, I...

    6. DD

      -getting into everything.

    7. AM

      I'm a, a... Do you know what's stone maker?

    8. DD

      No.

    9. AM

      Uh, a, a stone maker is a word used by urologists to people who make a lot of kidney stones.

    10. DD

      Kidney stones. Oh, okay.

    11. AM

      Yeah.

    12. DD

      Wow.

    13. AM

      I'm a stone maker. [chuckles]

    14. DD

      [chuckles] Wow.

    15. AM

      Multiple ultrasounds for kidney stones.

    16. DD

      You should hydrate then, in that case. [chuckles]

    17. AM

      [chuckles]

    18. DD

      Um, no, but, uh, so basically, ultrasound, he would apply the gel on you to do that-

    19. AM

      Yeah

    20. DD

      ... ultrasound. That's because for the signal to flow, um, there should be no mismatch of what's called impedance, right? For the signal to flow from the sensor into your body. That's called gel coupled. The problem is, the moment you have a system that's at a higher temperature, let's say above hundred degrees Celsius, those gels are not stable anymore, right? So effectively, we have a, a patent for a technology that is air-coupled, um, and uses a different piece of technology called magnetostriction. Without getting too much into the technicalities, you can just put the sensor around the pipe, and through the air, just generate signals. Why is this advantageous? It works at high temperatures. Today, most of your hazardous gases, liquids, like crude oil, naphtha, and the like, in very complex industries, work at temperatures above two hundred degrees Celsius, right? So we actually have a sensor that can generate these signals, and you need a sophisticated AI system to be able to tell you what those signals mean, because all you see are sinusoidal waves, right?

    21. AM

      Mm.

    22. DD

      So it'll analyze and tell you, "Okay, from these signals, there are defects occurring in certain pieces of the pipe. In about six months of time, pipe failure is going to happen, so you need to plan a maintenance activity," right? And this can be done across not just pipes. Using thermal data, you can also track if there are gas leakages, if there is corrosion happening in high-temperature systems. So there's a multitude of data sources where you can pull useful information from to predict both efficiency issues, as well as safety and-... shutdown of s- facility-related issues.

    23. AM

      Sure. So it's, uh, preventive maintenance-

    24. DD

      Yes.

    25. AM

      - preventive care.

    26. DD

      Yes, precisely.

    27. AM

      I have a question here. So I can understand this part, but just dialing back to the vision part, um, it's an AI system, so it requires a lot of training data.

    28. DD

      Yes.

    29. AM

      How do you get training data for hazards? Because obviously, this is something you want to avoid, right? So.

    30. DD

      That's actually a great question, which, which bleeds into what is really the moat of Detect? Like, why Detect, and, uh, why, why hasn't someone else done this? The most important thing is the amount of data that you train the system on. Synthetic data didn't exist as a concept back then when we started, I would say, 2015, 2016, but even now, it's always better to train a system on real data as opposed to synthetic data-

  8. 17:1826:25

    Student Life and Early Exposure to Industrial Challenges

    1. AM

      You started your journey as a student here.

    2. DD

      Yes.

    3. AM

      Uh, which years were you here? What, what course were you doing?

    4. DD

      So I'm a mechanical engineer. I did the dual degree mechanical engineering program with the master's in intelligent manufacturing.

    5. AM

      Oh, that was my course, too.

    6. DD

      Really?

    7. AM

      Yeah.

    8. DD

      Oh, wow! Okay. I, I actually didn't know that. Uh, no, fantastic. Uh, so IM was, was our specialization. I came in 2012.

    9. AM

      Okay.

    10. DD

      So I was here from 2012 to 2017. Um, that was my, my overall course.

    11. AM

      You know who else was in IM? Divanshu from InvVolve, uh, and, uh-

    12. DD

      Really?

    13. AM

      ... so he's nice. Yeah.

    14. DD

      Oh, okay. Divanshu was in IM?

    15. AM

      Yeah.

    16. DD

      I, I didn't know that as well, and I mentored the guy through the, through the [chuckles] startup journey at least.

    17. AM

      So you were a student, and the work that you do is very industrial. How is it that you came to this? I'm also curious, what did you do as a student to be exposed to something like... Did you work anywhere else in the middle?

    18. DD

      Uh, no, I didn't.

    19. AM

      So this is-

    20. DD

      This is my first gig.

    21. AM

      ... Okay. [laughing] So you were a student, I think, 2011, right?

    22. DD

      Yeah.

    23. AM

      And, uh, so how-

    24. DD

      '12.

    25. AM

      2012. So how did you enter this field? What did you do as a student to be exposed to this?

    26. DD

      Yeah, this is... I'll take you through. I don't know how much time you've got, but- [laughing]

    27. AM

      Lots.

    28. DD

      Lots? Okay. So I'll take you through the, the story, right? I, uh, since my first time there, uh, my dream... So my father's from IIT Madras.

    29. AM

      Okay.

    30. DD

      Uh, so he went to Mandakini hostel. Very smart man. Uh, brought me to campus, and, uh, he knew I loved animals, and he's like, "Dari, there, there's monkeys here, there's deer," and whatever that other thing is-

  9. 26:2528:33

    Incorporating the Company and Future Prospects

    1. AM

      incorporated?

    2. DD

      2016, my pre-final year.

    3. AM

      Okay.

    4. DD

      Uh, we incorporated also because of a need. There were multiple, um, inflection points. A lot of folks that were working, we had, at one point, like 45, 50 people within campus working on this piece of technology. We were not funded. Um, we had-- we got some, uh, funding into IIT Madras, uh, from, let's say, Reliance, to work on pieces of technology. But by then, 2016, we were working on sensor-based technology. We were like a innovation lab, drone-based technology, drone as an acquisition device, and then analytics on visual and thermal data on that piece of technology. So there were, like, at least 15 different types of projects we were working on.

    5. AM

      Interesting.

    6. DD

      Um, and we had great talent, like people that formed the computer vision group over here, formed the AI group over here in, in IIT and CIFI, and, and multiple groups. So we didn't want the talent to, to, to leave. So 2016, we were like: "Okay, now it's make or break. People are graduating," um, right? And we all have, uh... Tarun was four, five years experienced at that time in the industry. He had to make calls as well, and we also had to make calls. Are we gonna sit for placements or not? 2016 was that fulcrum point where we said, "No, there's something here." What we didn't know is it's a $90 billion target market for us.

    7. AM

      Mm.

    8. DD

      Uh, we did the math three years later, but in 2016, we knew it was a big market, right? Um, and we had advisors from the IITM ecosystem at that time telling us: "How do you build a company?" Right? The, the basics, the 101s.

    9. AM

      Mm.

    10. DD

      Um, and we had inspiring leaders, uh, prior to us, Tarun Mehta, who recently did the IPO.

    11. AM

      Mm.

    12. DD

      Uh, we had Kedar from HyperVerge. There were a few stories, um, and we wanted to-

    13. AM

      Kedar just raised a million.

    14. DD

      He had just raised a mil at that time. Yeah, you're right. Absolutely.

    15. AM

      So we said- [chuckles]

    16. DD

      I also want to po- point out that in IIT Madras ecosystem, if you have a name like Tarun, [chuckles] then you are statistically higher likely to start up. [laughing] Start up.

    17. AM

      I agree.

    18. DD

      And very all the Taruns. [laughing]

    19. AM

      Yeah, I agree.

    20. DD

      If you are called Tarun, you should consider joining here. [laughing]

    21. AM

      Yeah.

    22. DD

      So, um, 2016, we realized, so 2017, we needed to raise

  10. 28:3328:42

    The Fundraising Journey Begins

    1. DD

      funds. Um, and that's when the fundraising journey started-

    2. AM

      Okay

    3. DD

      ... um, at which point I would say Hari and me were still students at that time, but, uh, Karthik and Tarun had both graduated, uh,

  11. 28:4231:45

    Early Days and Initial Pivots

    1. DD

      from the company.

    2. AM

      From what you do now to when you started in 2016, '17, has there been a lot of change or, or can you take us through what you were doing in 2016 and '17, and the pivots that have happened to where you are now? Of course, there was a pandemic in the middle also, so I'm sure there are some interesting stories there.

    3. DD

      There were multiple pivots, right? We were a hardware-cum-software technology player, right? We built patented hardwares, and we built software layers to analyze the data from those patented hardwares, right? But what we understood was the industry started trusting us a lot. We were looked at as the innovation lab for the industrial ecosystem in India. There were BPCL, Adani, Vedanta, Aditya Birla, um, HPCL, and Reliance, multiple of the large players working with us across the country, right? So what we understood was, it became very important for us to under- understand where is the value, what is the value, and what can we outsource? This became a big question to us because I think as inventors in IIT, you get very biased towards your technology that you built now, um, and, and less think about product market fit and other things, right? So I would say a lot of the mentors, as well as the investors, helped us in that journey.

    4. AM

      That's not specific to IIT. If you're sitting in a lab, you're solving for problems that the lab gets.

    5. DD

      I agree.

    6. AM

      And then you get attached to that solution that you have built.

    7. DD

      Yeah.

    8. AM

      And then you take the solution to the market and hope that the market has a problem.

    9. DD

      Yes. [laughing]

    10. AM

      [laughing] But if you're building a company, you should have started the other way.

    11. DD

      Yeah. Um, that's actually what we did. We spent so much time in the market throughout our college years, that we understood all the needs. This solution can be optimized further.... right? So number one, we did the hardware plus software play. I would say we did that up until 2018. Um, then 2019, we had collected so much data. We are the leading player in the world to collect visual data with regards to safety globally. Right now, we've collected more than 10,000 terabytes of information and trained it, right?

    12. AM

      Mm, nice.

    13. DD

      We didn't realize that that's a moat at, at that time.

    14. AM

      Right.

    15. DD

      Even then. We realized, okay, this is automation, and we can do this on our set of cameras and your set of cameras. Um, then-

    16. AM

      Well, actually, I want to just point out here, uh, I was working with Hyperverge for a while.

    17. DD

      Oh, okay.

    18. AM

      Um, and, uh, in Hyperverge, they were also building vision systems in the 2014, '15-

    19. DD

      Yeah

    20. AM

      ... 14, '17 period. Uh, and they also ended up collecting a lot of data through their own work-

    21. DD

      Yeah

    22. AM

      ... or through their partners, and labeling it-

    23. DD

      Yes

    24. AM

      ... and, and creating those data sets. And if you own the data sets, then obviously your-

    25. DD

      Yep

    26. AM

      ... training is much better and-

    27. DD

      Exactly.

    28. AM

      And it's weird how your ability to sort of do a manually intensive work, um, without feeling like: Oh, this is too much work, I won't do it. Uh, but actually going and doing it can end up being a very strong moat.

    29. DD

      Yeah. Um, uh, as I said, we, we knew it could be a moat among the 100 things we were doing. It didn't hit us as to how much of a moat-

    30. AM

      Okay

  12. 31:4534:31

    The Impact of the Pandemic

    1. DD

      Then 2020, the pandemic hit-

    2. AM

      Mm

    3. DD

      ... and nobody was allowed in facilities, so drone services out, sensor installation out, right? It was just bare bones, um, in the ecosystem. Um, revenue took a hit, and we, of course, um, had to understand: Okay, who are we as a company? People were going through layoffs. As an organization, are we going to go through the same process? We actually took a call at that time that changed our trajectory of the company, right? When revenue was going down, we sat down and said, "Okay, what's our strategy? Our revenue is down. We don't know how long this is going to last." And then we said, "We're not gonna let go of a single person. All these people have, uh, trusted and counted on the company." So we actually modified a lot of the roles within the company that were operational-focused, trained them to be data scientists, and we said, "We are going to be a hardware-agnostic company, and we'll just run these programs." We've already built a strong analytics layer. Let's just pivot completely to a AI SaaS company that works on existing infrastructure and provide insights to customers that are of multimillion-dollar value to each of them, right? And that pivot changed the trajectory of the organization, uh, in 2020.

    4. AM

      Can I ask you a technical question here?

    5. DD

      Please.

    6. AM

      So if you're, if you are using the camera feed-

    7. DD

      Yeah

    8. AM

      ... to do analytics, that camera feed is coming to a central location, or it's going to a cloud? Is it hosted on the-

    9. DD

      It is a cloud-based... We- There is both a cloud-based as well as an on-premise version.

    10. AM

      Can it be deployed on the camera itself?

    11. DD

      Yes. We have edge-based deployments as well, but the level of complexity of the compute is much lower-

    12. AM

      Right

    13. DD

      ... right, for, because of hardware limitations. Hopefully, that gets better, uh-

    14. AM

      As cameras can take more compute.

    15. DD

      Yes. Yeah. Um, uh, given how much, uh, the market cap of NVIDIA and other folks is, we will eventually get there-

    16. AM

      Yeah

    17. DD

      ... is, is our belief. But as of now, you can run a few of the rules on the cameras itself, locally.

    18. AM

      But that would mean that more remote, uh, uh, it-

    19. DD

      Locations

    20. AM

      ... can run in more remote locations.

    21. DD

      Offshore locations. We've have multiple offshore deployments-

    22. AM

      Yeah

    23. DD

      ... across multiple oceans, um, in the world, as well as even onshore, where bandwidth and connectivity is a big problem. You can run it.

    24. AM

      Okay.

    25. DD

      But if you want to run something, OSHA, it's more than 1,000 rules. Like, if I had to count, program all the rules, it, it can fill a full book, book, right? To run something that complex, you will have to utilize the cloud at this point of time.

    26. AM

      Okay. Understood.

    27. DD

      So we use a cloud-based system across, uh-

    28. AM

      Understood. Understood. So you, you were a hardware plus software company when you started?

    29. DD

      Yes.

    30. AM

      And then, as the pandemic hit, you realized that it makes more sense to move to a software-only company-

  13. 34:3137:01

    Transition to a Global Player

    1. AM

      Uh, at what point did you go from companies like Reliance, HPCL, Indian-based companies, to a global, uh, portfolio?

    2. DD

      2019, 2020. Same, same period of time.

    3. AM

      Okay.

    4. DD

      Um, our first international contract at that time was with Shell. Um, and then we started scaling across, uh, right? So I would say 2020, uh, was our first international deployment in the United States. Um, and then from there, again, the rest is history, right? We've scaled across-

    5. AM

      Mm

    6. DD

      ... multiple players across the Shell, [beep] uh, and not just oil and gas, construction, international construction players, uh, international paper, paper manufacturing facilities, where it is surprising the amount of risk that exists, um, in paper manufacturing facilities as well. Um, and cross-industry mining, steel, the, the like. Um, 2020 was a pivotal moment, again, at that point of time, because now that you're hardware-agnostic, the value of your solution can be seen not just by players in India, but players in Canada. There were some companies where we didn't even have a face-to-face meeting where we had deployed the technology. They had seen the value of the technology over a one-month time period, and then scaled the technology up as well. So suddenly, from 2020 to now, we've gone from being, let's say, a two-country deployment company to now we're deployed in more than 20 countries at this point, um, globally.

    7. AM

      That's cool. Um, two questions, uh, one remark about the way you mentioned paper.

    8. DD

      Yeah.

    9. AM

      Uh, maybe consumers may or may not realize it, that something as delicate as paper [chuckles] in a factory process, it goes through absolute torture. Those molecules have gone through hell- [chuckles]

    10. DD

      Yeah

    11. AM

      ... to turn from pulp-

    12. DD

      Pulp, exactly

    13. AM

      ... or a, or a tree's cellulose to paper.

    14. DD

      Yep, yep. And there's dangerous equipment, right? There are these equipments called winders-

    15. AM

      Mm

    16. DD

      ... that actually wind after you smash, like, winds the paper over there.... now, there are people that have got their arms stuck, chopped off, dislocated in these locations because they think the machine is stuck, and they think the machine is off.

    17. AM

      Mm.

    18. DD

      And then they go and try to fix it. Suddenly, it switches on, and, uh, there are multiple incidents like that that happen.

    19. AM

      I'm, I'm so happy that your technology is trying to prevent that.

    20. DD

      It is actively preventing it. It's, uh, uh, we have technology that can create red zones, that can again integrate back to the machine and tell the machine, uh, "There's a person in the vicinity in danger. You need to stop work."

  14. 37:0140:51

    Industry 4.0 and OSHA

    1. AM

      Can you tell me a little bit about- uh, you used the word OSHA. Um, can you tell me how OSHA has evolved over time? And also, I think I've seen on your website, the word Industry 4.0.

    2. DD

      Oh, yeah.

    3. AM

      Yeah, I'm curious to know. I mean, I ha- I guess I have a broad idea what it means, but curious to know what is Industry 4.0 and-

    4. DD

      Sure

    5. AM

      ... and the question comes to mind: what is 3.0 and [chuckles] there's 2.0? Is there a 5.0? [chuckles]

    6. DD

      Yeah, there seems to be one coming up, at least-

    7. AM

      Sure

    8. DD

      ... according to the some of the books I've read recently. Um, I'll answer the second question first, then, on the Industry 4.0, and then come to OSHA. Some people say we're in a transition from 4.0 to 5.0, but for the most part, we are right now still in 4.0. Um, Industry 1.0 was the first invention of the steam engine. [train whistle] Um, right at that time, which was, okay, you can have machinery perform activities, um, faster, um, than a human, right? Then Industry 2.0 was the Henry Ford era, which is what? The 1800s.

    9. AM

      So Industry 1.0, we are building things like rail lines and-

    10. DD

      Correct

    11. AM

      ... machine- uh, large, uh, maybe machines for, uh, construction, things like that.

    12. DD

      Yes. 2.0 is when you look at assembly line manufacturing, large-scale manufacturing, and how do you impact consumers at scale, right? Which is when Henry Ford came into the picture. Um, so that was, I would say, the 1800s. Then the next big leap of innovation took a while after that, which what people consider 3.0, is over the 1970s, where computers and digital started coming in, and automation came in, where you understood machines could perform repetitive tasks that you were having people do in these supply lines. And finally, most recently, over the last decade, or a little over the last decade, last 10 to 20 years, is where Industry 4.0 has come in, where you understood that you can create central layers. Um, devices can be IoT devices with the advent of the internet, machine learning, AI, and the like. And you can understand data, have machines talk to each other, have information from multiple machines come to a point for a central decision-making, um, right? And that is a continuously evolving process right now. So today, we are in the Industry 4.0 era, which is a massive, massive market. There are multiple companies-

    13. AM

      So in e- each of these layers, uh, industries have become more ambitious, larger, uh, they're producing more goods at a faster speed-

    14. DD

      Yes

    15. AM

      ... more efficient, more power efficient, more, uh, safer.

    16. DD

      Yes.

    17. AM

      Right? And, um, that's interesting. So then, um, how has OSHA evolved?

    18. DD

      Oh, yeah. So OSHA would have started, I would say, with the first set of incidents that, that ever happened across these, these supply chain lines, with a few rules saying, "Okay, you shouldn't put your hand in specific areas of machinery that you're operating." Today, uh, OSHA is still evolving, right? It's a- your- the equipment you're building is safer and more efficient, but it comes out with new risks, right? And this continues to change with newer machinery that's brought into the, the market. So OSHA continues to evolve and upgrade. There's another body that works like OSHA, but in construction, called IOGP. Um, right? So these bodies, uh, today, they have massive books of, of this size, with thousands of rules, um, right across... I would say, I would categorize it into eight key categories, uh, which includes PPE, work at height, line of fire, uh, vehicle safety. There are multiple major categories, and under them, there are hundreds of sub-categories, uh, that come in. Um, right, so today, these are mature organizations. They do incident investigations across the world-

    19. AM

      Okay

    20. DD

      ... um, and, uh, uh, make the world a safer place, actually, through policy as well.

    21. AM

      Nice. And so, um, yeah, I think I've understood it. This is good. Like, let's, let's, let's, let's focus on something else that you spoke about-

  15. 40:5146:26

    Expanding Internationally

    1. DD

      Sure

    2. AM

      ... uh, which is that you went from being a one, one-country, two-country operations to a 20-country operations.

    3. DD

      Yes.

    4. AM

      It's a global space. Um, you're catering to American companies. Some of them, of course, may be 100 years old.

    5. DD

      Yes.

    6. AM

      Um, uh, how does it- how do you make the leap from, uh, an Indian company working with other Indian companies? Maybe, uh, Professor Krishnan may have known them for a while-

    7. DD

      Mm

    8. AM

      ... um, or your co-founder would have worked in one of those companies. So you, you had, uh, your friends, or friends of friends, or friends of friends of friends-

    9. DD

      Mm

    10. AM

      ... to working for, uh, global companies, you know, thousands of kilometers away. They don't know you. Uh, they're from a different culture. They work differently. How does that, how does that leap happen?

    11. DD

      I think every entrepreneur reaches multiple, uh, inflection points in their journey, right? Where one decision is going to change things. For us, um, this was one of those decisions that the first question was, "Is... Are all of these problems just an Indian problem because of the number of unregulated activities that happen, or is this a global problem?"

    12. AM

      Mm.

    13. DD

      Right? I think our first case study of that came from one of our first partners. Similar to how I said we had initial partners in the industrial ecosystem in India, our first international partner was Shell, uh, for that matter, through their office in Bangalore, right? And even them, they came and said, "Look, this is of value-- we believe is of value globally, but there's only one way to find out. Test it," right? "See the value on the ground. Have people on the ground say, 'I cannot live without this piece of technology,' right? Or, 'I, I cannot operate efficiently or safely. I feel better with this technology in place.'" Once that's there, then this is a very reputation-based market. Um, everyone speaks to everybody. People share learnings in keynotes and conferences. So-... the rest, organically, business will, will, will fuel itself, right?

    14. AM

      Yeah. I can imagine that. I mean, uh, some Halliburton safety manager will always be curious, what is Shell safety manager doing?

    15. DD

      Exactly.

    16. AM

      Or, uh, what is the- how, how are the safety incidents elsewhere, and-

    17. DD

      Precisely.

    18. AM

      Mm.

    19. DD

      So there's this- actually, there's this one person, his name is, uh, Shekar Karani, he's from Accel.

    20. AM

      Mm.

    21. DD

      Along with the-

    22. AM

      Hey, one person? [chuckles] He's a very famous person.

    23. DD

      Very famous person, very famous guy. Him, and the second guy who told this to us was, uh, Ashok Junjunwalla. So two very famous people actually came and told us, "What are you doing here? Just book a ticket and fly out. Figure the rest out later," [plane engine] right? Uh, to which we took a little bit of time, but then finally, in 2019, booked a ticket. Went- ended up in New York, which is the wrong place to be-

    24. AM

      Mm

    25. DD

      ... um, for the industrial ecosystem.

    26. AM

      Yeah, because the industries are in Texas and-

    27. DD

      Texas, Louisiana, Pittsburgh, probably. Once we flew there and identified that, okay, the problem is a global problem, but the way you cater to that problem has to meet certain international standards.

    28. AM

      Mm.

    29. DD

      The level of delivery, implementation, support, perfection, and customer success as a concept has to be far more stringent in that region. And then, as I said, success fuels itself, so that became very important for us. So we actually have fully fledged entities in a lot of these regions between North America, the Middle East, um, and we operate through partners in, in Europe, and we have an entity in Singapore that takes care of the Southeast Asian region. Each of these entities ensure excellence in the region-

    30. AM

      Okay

  16. 46:2648:38

    Funding Journey and Validation

    1. AM

      Um, can you give us a idea of your funding journey?

    2. DD

      Oh, yeah, absolutely. I would say we went through multiple rounds. Uh, 2017, we did a 800K, uh, round at that time. Uh, majorly angels, and I would say Accelor Ventures, which is Chris, uh, Chris and Shibu's, uh, fund, uh, seed stage fund, as well as-

    3. AM

      That's a deep tech focus fund.

    4. DD

      That's a deep tech focus fund. Deep tech focus funds were coming up at that time, actually. It was good timing. Uh, to- we also had Bharat Innovation Fund, which was another deep tech focus fund. But then we had, uh, one of the larger players, uh, Elevation, come in in 2018. Uh, that was a $3.3 million round. Um, then post that, our next round was in 2021, which was Accel, which is close to about, uh, Accel, Elevation, and I think, uh, the first infusion of funds from Shell Ventures, um, as well, close to about $9 million. And-

    5. AM

      It must feel quite good to have a c- a customer-

    6. DD

      Absolutely

    7. AM

      ... come in and say, "We'll invest in you," right?

    8. DD

      Yes. Yeah.

    9. AM

      That's a huge validation.

    10. DD

      Massive. Massive. One of the best gold standard players in the world coming and saying, "Not only do we want you to add value to our sites, but we also want- we believe in your journey, and we want a piece of the cake," is absolute validation, uh, for the technology, the vision, and the mission. Uh, very recently in Shell specifically, we won what's called the CEO Award, which is an award, uh, CEO Award for Gold Zero, which is one of the awards given, uh, for technologies that have scaled the most on their Gold Zero journey, which is zero incidents-

    11. AM

      Okay

    12. DD

      ... in their facilities. So, uh, that, that was another big win. Uh, but to continue on your point of funding round, then, uh, in 2022, we did another $25, uh, million between, uh, Process, Accel, Elevation, and of course, Shell Ventures, and Bharat Fund, and Accel- uh, Accel continued doing this. Uh, also, another player that's invested from day one, 2017, all the way till now, is also a item at, uh, Blue Hill, uh, Ventures.

    13. AM

      Manu Iyer.

    14. DD

      Manu Iyer, yeah.

    15. AM

      This is damn good. Um, I wanna, I wanna s- sort of talk about a few things here and there, which I think we've missed out.

    16. DD

      Sure.

    17. AM

      Um, we spoke about your

  17. 48:3855:03

    Advice for Aspiring Entrepreneurs

    1. AM

      origin story. We spoke about how you entered this field, and, uh, there was a point where we were talking about-... on how students looking to start up or young, let's just say young people looking to start up, um, who don't have a large, um, experience. They have to pick a problem they want to spend five, 10 years with, maybe 20 years with. Um, how do they seek problems that is sort of outside their limited zone?

    2. DD

      Yeah, that's, that's a great question. Um, it has to come from within, firstly, because that was the same- Like, I'll tell you what, Detect wouldn't have existed if we didn't go out looking for problem statements, right? So I think initiative, uh, number one. Number two, there are many levers in the ecosystem that can fuel that initiative, right? I think we didn't have this. This was, again, started by Professor Krishnan, the Gopalakrishna Deshpande-

    3. AM

      GDC, yeah.

    4. DD

      GDC, the initiative over there, it didn't exist in our time. In fact, one of the reasons that that came up was because we believed that, okay, we got exposure. Everyone needs to get exposure, so something like this needs to be formed, was how Krishnan sir also was, was, was thinking about, uh, doing this. But, um, I think there is also a great mentorship ecosystem within IIT. Um, there are folks in IITM Incubation Cell and mentor programs that, uh, we used to, uh, spend a lot of time just going to the IITM IC, uh, just to take advice from folks like John, um, and then other mentors from the ecosystem. And there was another person, very impactful in our journey, which is, uh, a person named Gopal. Um, he was the previous MD at the Sun Mart Kemplus IITM grad, and, uh, he started with, "Okay, you need to stop being a student. You need to understand finances, use cases. How do you bring ROI," right? That was the first evolution to a third-year student on how do we do this. Um, so I think leveraging your, your seniors, your IITM network is extremely important. Um, spending time- The moment you think of a use case, let's say it's a B2B. B2C, okay, you can meet customers. B2B, the only way is actually to go observe how their facilities operate, observe how their sites operate, and I think the alumni ecosystem is the best way-

    5. AM

      Sure

    6. DD

      ... to be level.

    7. AM

      So basically, you are saying put yourself out there.

    8. DD

      Absolutely.

    9. AM

      Leverage your ecosystem, and use your, whoever you have access to, to be exposed to a larger problem set. But I want to ask you a question about that. You sent one mail, and the prof replied. [laughing] Which is, I feel, amazing. Okay. Now, a student today, maybe he sends 15 emails, contacts 15 people, gets 15 problems-

    10. DD

      Wow

    11. AM

      ... 'cause the idea with us is, like, getting a lot of problems for everyone, right?

    12. DD

      Yeah.

    13. AM

      So there's so many things that people are working on and so many... It- As a student, how do you parse, like, "Should I spend time on this? Should I spend time on that? Um, will, will this be interesting to me for a long time? Will I create- be able to create value?" Um, you get what I'm saying? Like, it may be the, the... Is it a pr- I feel sometimes that it's a problem of plenty.

    14. DD

      Yeah. I, I would say two, two things to that. One is, of course, I sent one mail to Krishnan. Before that, before I had the chat with Harry, I had sent a mail to, like, some 15 different prof [chuckles] who, who ignored me.

    15. AM

      [chuckles]

    16. DD

      But, um, I, I would say it's also another thing. One common trait you will see in all of the founders that we met, even at the recent event at Sangam, is there is a problem of plenty.

    17. AM

      Mm.

    18. DD

      But the ones who survive are not the ones who are actually successful, uh, initially. It was all the folks that were obsessed. The, the one common thing about founders is, of course, obsession with some sense of se- uh, with some sense of, "Okay, there is value." But, uh, I can tell you there were multiple times in our own journey where folks came and told us, "This is too complex an environment for you guys to innovate in."

    19. AM

      Mm.

    20. DD

      Um, "You guys are not going to make it. You're... Yeah, you're, you're, you're too young."

    21. AM

      Young 20-year-olds-

    22. DD

      Yeah

    23. AM

      ... in a large-

    24. DD

      In a where decision makers are 50 and above.

    25. AM

      Yeah.

    26. DD

      Right? So it w- To many folks, it was illogical. What Taran Mehta was doing at that point of time was j- too ambitious.

    27. AM

      Yeah.

    28. DD

      Um, right? So the common trait across most founders that make it eventually is, is just sheer obsession, uh, with the problem statement, and clear visibility that there's value to be unlocked.

    29. AM

      Mm.

    30. DD

      And they figure out a way. So you are going to get knocked down tons of times. We got knocked down tons of times. [chuckles] Um, it was not easy, but eventually that grit, um, in the individual is... There's no hack. There's no easy-- If it was easy, everyone would have multi-billion dollar startups at this time.

  18. 55:0357:08

    The Role of Ecosystem and Mentorship

    1. AM

      it together.

    2. DD

      Take it.

    3. AM

      How has the IIT Madras ecosystem helped you?

    4. DD

      Tremendously. Um, I think starting from day one, access to the lab, right? Lab infrastructure, that is... Of course, we work in Professor Krishnan's lab. At the time, it was one of the most cutting-edge research facilities in the world, uh, in fact, uh, competes globally. So that number-

    5. AM

      This is the Center for Non-Destructive Evaluation-

    6. DD

      Non-Destructive Evaluation.

    7. AM

      CNDE.

    8. DD

      CNDE. Fantastic, uh, center. Um, we had access to a lot of, uh, industrial problem statements as a result. Um, and then in the next phase became the mentorship from seniors. I would say, just the fact that you have talent over here that you-

    9. AM

      Yeah

    10. DD

      ... would be- you would struggle to find-

    11. AM

      Yeah

    12. DD

      ... um, in the open market, right? And, uh, those- the best minds across departments exist over here. Um, then the fourth is going to be the incubation ecosystem. I think from an incubation perspective, probably one of the best in, in the country. Um, right, so the access to mentors, I think we had the vice chair- previous vice chairman of the Murugappa Group, who would come in, spend half an hour, actually, just giving us yarn exposure and say, "If you're worth it, go visit our facility in Coromandel," right? And he would set up meetings as a result. So, um, the incubation, uh, facilities also are great, and the fact that you have talent of CFI, where people are itching, uh-

    13. AM

      To do work.

    14. DD

      To do work, right? Who are itching, uh, and want to create value desperately, and these are smart minds-

    15. AM

      Yeah

    16. DD

      ... uh, trying to do that. I think all of this put together is a recipe. In fact, June puts it extremely well, uh, Ashok, sir, he puts it well. He says, "Here in, uh, in, in this ecosystem, we have technology, we have professors, we have industrial specialists, but our X factor is students who don't know that something can't be done," right? It, it requires that foolhardiness, um, and that's what, uh, we all represent.

    17. AM

      Nice. I'm going to quote that.

    18. DD

      Yeah.

    19. AM

      "We don't know that something can't be done." [clears throat] Very cool, Daniel. This is

  19. 57:081:00:08

    Closing Thoughts and Reflections

    1. AM

      amazing. Um, just, uh, we are at the hour, so we've spent a lot talking about your company. Congratulations on the growth you've had.

    2. DD

      Thank you.

    3. AM

      You're an inspiration to a lot of people, and, and I hope that... Uh, you know, you've been a little stealth throughout, so I think a lot more people will be aware of the scale and the impact that you've had-

    4. DD

      Absolutely

    5. AM

      ... up to now, so I'm really looking forward to that. Um, you're also married to an IIT batchmate.

    6. DD

      Yes.

    7. AM

      So, you know, I think [chuckles] congratulations.

    8. DD

      [chuckles] Thank you.

    9. AM

      That's something more that IIT has given you. [chuckles]

    10. DD

      [chuckles] Yeah, yeah. IIT is, uh, ingrained. So my father is from here, my wife is from here, I'm from here, so, so yeah, it's, uh, very close to my heart.

    11. AM

      Nice. Let's close on that note. Thank you so much for coming in. Um, yeah, any ending thoughts?

    12. DD

      Oh, no. It, it was definitely a pleasure to finally meet, uh, you, Amrut, because when I met you in Sangam, uh, to be introduced to someone who was the, the first mind for CFI, uh, it's because of, uh, folks like you that we are able to do what we do.

    13. AM

      I wouldn't say that. I... CFI, as a setup, started in 2000- the work started in 2007. I was a student then. Uh, I definitely was part of the team that was leading the effort to put up CFI.

    14. DD

      Yeah.

    15. AM

      I graduated one month before CFI was actually founded.

    16. DD

      Oh, wow! Okay.

    17. AM

      So, uh, I graduated in June-

    18. DD

      Yeah

    19. AM

      ... and July was the, uh, setup. And of course, right from the word go, CFI has been, uh, blessed. Uh, the principal scientific advisor to the Prime Minister inaugurated it.

    20. DD

      Yeah.

    21. AM

      I did come down for the inauguration, and after that, we've had a series of successive, uh, heads who have done really good work, uh, some of whom... Uh, my- the first head was, I think, Ravikant.

    22. DD

      Yeah.

    23. AM

      And then, uh, there was Ravi Teja, and, uh, lots of people who have gone on to do really good things. Of course, Swapnil and Tarun were very active in CFI at one point.

    24. DD

      Yeah, yeah.

    25. AM

      Uh, so I mean, I don't, I don't know what stars aligned for us.

    26. DD

      Yeah, you're being super humble, but it really set up the ecosystem for us, people like us to exist. Um, right, or otherwise it would have been... I tell you what, at one point, we were a student-run, like 40, 50 interns, right?

    27. AM

      Mm.

    28. DD

      Half of- like, almost all of them were, were folks just sitting here working on... Maybe not here, but in the, in the old CFI.

    29. AM

      Old CFI. This is the new building of CFI.

    30. DD

      CFI, yeah, yeah. Who were just working on different robotics equipment over there. Though, that, that was the talent that built, built us up, and, uh, the stories goes the same for many of us.

Episode duration: 1:00:08

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