Using AI to Solve the Energy and Industrial Sectors | Srikanth Muralidhara from Flutura

Srikanth Muralidhara knew a lot about data analytics and machine learning, but nothing about the oil industry—which is why he was a bit apprehensive to take a meeting with a Shell executive who’d expressed interest in a blog post he had written.

But it was, in fact, Srikanth’s position as an outsider that most attracted the executive. “We need to learn from other industries,” he said. “And there’s always concepts which can be borrowed.”

This was how Srikanth and his partners quite unexpectedly found themselves solving problems in the energy and industrial sectors.

The result was Flutura, a startup that uses data analysis, AI, and machine learning to help these sectors optimize operations and prevent failures.

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Srikanth has been in the area of data science for close to 2 decades. He co-founded Flutura, an Industrial AI company focusing on transformational solutions for the industrial sector. Flutura’s award winning flagship product Cerebra is deployed across several industrial facilities worldwide in the Energy, Natural Resources, Manufacturing, Defense and Transportation companies to power transformational business outcomes around Industrial AI. Flutura’s vision is to deliver 1 billion USD of outcomes to its customers by 2024.

He started his career in embedded systems where he had a brief stint developing communication protocols for payment systems and networks. He has incubated business analytics initiatives for large Fortune companies and has been part of building more than 400 transformational analytics solutions in his career.

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

Srikanth Muralidhara 0:00
You know, we need to learn from other industries. And there’s always concepts which can be borrowed. And there are a lot of innovation which happens at that intersect. And that opened our, you know, our journey which

Alexander Ferguson 0:20
Welcome to UpTech Report. This is our applied tech series UpTech Report is sponsored by terylene. Learn how to leverage the power of video at Today, I’m joined by my guest Srikanth, who’s based in Houston, Texas. He’s the Co founder and Chief Customer Officer at Flutura. Welcome, Srikanth, good to have you on. Thank you, Alex. Nice to meet you. Now, at understanding Futura industrial AI company, you’re focused on yield uptime and netzero outcomes for the industrial industry. This started though back in 2012, the three of you, you and Krishna, Derek co founders, you actually had a start with a coffee, if I understand correctly, was Robert Patterson, the CTO for Shell? Can you describe like, take me back to that moment for the concept of this where it began? Sure,

Srikanth Muralidhara 1:07
you know, Alex have been in the industry for almost 25 years and have been in the data analytics, space and machine learning. And 2012, when we started, you know, we decided we need to take an adventure on our own. And these are a lot of possibilities when it comes to innovation and adoption. in machine learning, right. And, you know, we used to write a lot of blogs and ideas for the industry. And so one of the gentlemen from Houston, you know, picked up one of the blogs and said, Hey, you know, what shell is interested in hearing about your idea? Would you like to, you know, come and share your idea and thoughts with them? And how did he come across your

Alexander Ferguson 1:59
blog? That was he? You just were already well known in the space? And oh,

Srikanth Muralidhara 2:04
no, so we are we are not Steve Jobs are already know, your background, right. So it was just someone on LinkedIn, they picked up the, the thought paper which we had written. And then you know, so then we decided, you know, we were deciding, you know, that it should be me or Christian or direct to to take this meeting. We didn’t have any oil and gas experience when we started the company. So I was a little nervous, you know, meeting and with, on shell, you know, what do I talk? And how do I share this idea? So then, you know, so we decided it’s going to be me, who is going to take the, the beating from shell. And I met Robert in Houston. And it was a fantastic conversation I had and people who know, Robert would obviously not is one of the best visionaries in the industry. And through that conversation, you know, the first disclaimer I I told, Robert is, you know, I have no idea about oil and gas, but he said, you know, we need to learn from other industries. And there’s always concepts which can be borrowed. And there are a lot of innovation which happens at that intersect. And that opened our, you know, our journey, which and he shared a lot of problems which which the energy industry faces, and especially not they work and not very mission critical facilities, right. And he said Mother Earth, I know that it actually extracts the oil and gas, which is extracted from Mother Earth comes at 15,000 psi of pressure, which is mind boggling. can’t even imagine the pressure at which, and look at how the you know, we have designed systems to manage that pressure. And that’s how it started, then we know we went on to on the journey, we understood the market well. And sometimes people actually think that I’m actually from an oil man.

Alexander Ferguson 4:19
But it was all happenstance because because of a cup of coffee. Now I’m wondering, what was the paper on what was what was the blog post that captured his interest?

Srikanth Muralidhara 4:28
Now the blog post was around. You know, how do you manage large volumes of data and scientifically pick exceptions from the data to influence business outcomes? So we had written a thought record in terms of how do you even think about managing and in a structured way, use data for influencing better outcomes. That’s how it was,

Alexander Ferguson 4:55
and it was came from from your previous experience. Have in this right space. And then that’s just happenstance or or just a meeting of minds that he comes across and says, while we’re in that same, or we need to be thinking about this, and it was probably.

Srikanth Muralidhara 5:13
That’s a nice question, Alex. In fact, Krishna and Derek and me were part of a company called mindtree. And our last stint at mindtree was we were part of the team which built our platform, which is basically the the citizen repository for the Government of India. So you can imagine a billion population, and it’s like the SSN in us, but imagine an SSN platform in India for a billion citizens. So it’s a massive ecosystem where, so that’s where we got exposed to very complex systems, very large volumes of data. And you can imagine the possibilities around around this. That’s a bit of exposure.

Alexander Ferguson 5:57
And then that’s where you’re at didn’t come from a background of oil and gas and energy. But the the underlying challenge of what do you do with lots of data and using machine learning? That’s where you’re translating across now going forward from here? Your first year? So I would say if I understood correctly, was still a year of discovery and a lot of discussions with engineers in in the oil and and industrial space, right already from that point?

Srikanth Muralidhara 6:22
Yes. So one of the good things we did Alex is, we were open to learning. We didn’t. One good thing about the three of us is we don’t have a strong bias towards certain solutions or certain aspects. Because in this field, a lot of things are evolving. And you have to learn every day. And that’s exactly what we did. So you know, we met people, Robert had introduced us to some us colleagues, and we actually, you know, started to grow our network within Houston. And it’s mind bug, I don’t know whether you have visited Houston, you know, a city, which I think it’s, it has the densest population of Fortune 500 companies in the world. So it’s much easier, right? As a startup, we don’t have too much money. So there’s no travel expense. So you just be in Houston. And, you know, just talk about visiting every road, one step at a time and visiting all the companies, you see that that’s literally we did that, you know, we throw around Houston and pick, which are the companies we need to be speaking to,

Alexander Ferguson 7:29
seriously, you drive around and you say, oh, there’s a company, this company will just start knocking on doors. That’s how it happened. Yeah,

Srikanth Muralidhara 7:35
that’s when we learned the ecosystem. And yeah, the first six months was a lot of intense learning. And I think that that’s the one which we learned a lot in terms of the various industry problems, and their own it was it boiled down to which one do we pick and solve?

Alexander Ferguson 7:52
How did you get those conversations, when you just walk in, say, I’d love to talk to your engineers here and see what kind of challenges you have. And you started that way.

Srikanth Muralidhara 8:00
I think mostly a reference really help you make friends. And you build a trust, and you don’t try to sell anything, or so the first thing was, you know, we used to go and say, You’re not here to sell, we’re here to learn. And the other aspect is, you know, many of the companies will also want to learn from other companies. Right? I think that’s the biggest human motivation. What’s my competition doing? You know, that that question? So you use that as one of the core aspects, saying that, you know, I’ve learned from some of your competitors in the industry, love to share thoughts, exchange ideas, and that’s how it worked. Yes. And, you know, you will be surprised, Alex, if, very often, we do have our own biases, where we think that people don’t help. But if you’re genuine, and you say that, that’s your intent, and you seek help, you know, you’ll get it in many ways.

Alexander Ferguson 9:02
If you think over the last 910 years, since since since you started, one of the highlights, and that was maybe one of the best moments of your time so far that it really shone out for you What comes to your mind.

Srikanth Muralidhara 9:18
Highlights, I think, when we started the company, in fact, we didn’t have a name for the company. So we actually so as soon as we decided to start a company, it you know, it was natural to a saying, you know, it has to be something to do with the transformation of which we see nature right from a butterfly from a caterpillar to a butterfly. So we said, you know, it has to be around trust and transformation. And it took us really a long time six months to give a name to the company. And by the time you know, we didn’t stop focusing on meeting customers and so on. And, you know, one of those days we landed in a in a situation We had a contract on hand, but we didn’t have a name of the name for the company to sign the contract against. And, you know, we were forced to choose a name in two days, you know, we put it on Facebook and, you know, requested our friends to share ideas. And you know, Fletcher name was actually came from one of those Facebook service we did. And that was a very memorable event for me. Second thing is obviously meeting Robert Patterson was definitely the the inflection point, I would say, if we had not met a visionary like him, it would have been difficult for us would have taken longer time to, to go in the right direction, right.

Alexander Ferguson 10:42
Not a lot of startups can can can have that same type of initial experience to kickoff their, their, their company.

Srikanth Muralidhara 10:50
That’s right. And, you know, the same journey. We used to write a lot on LinkedIn and, and some of the blogs and through that, again, our first investor also reached out to us, he was also our ex customer, former customer of ours. He said, You know, you are doing something interesting, can I invest? And then our first investor hive were based in Palo Alto, and they also have a franchise in in Mumbai. They came and said, you know, your company looks interesting, can we invest? So the I think the first struggle in terms of raising money was, was not that difficult for us. Thankfully, if I reflect back, I think we are blessed to have some of this. But I think there are ways in which you can know if the, the ideas are worth it, I’m sure there are people who would definitely want to invest.

Alexander Ferguson 11:44
I feel like the mix of success here comes from you had some good ideas. Obviously, if we didn’t write good content, people wouldn’t be wouldn’t care. But it’s a mix of writing good ideas, getting it out there. And then having the network that at least someone who knows someone is sharing the content and being able to get that initial buzz. That’s right.

Srikanth Muralidhara 12:04
And obviously winning shell as a customer, we won, I think the second, the third important milestone was the first investor, it’s always a sweet one. And the next inflection point is is was basically winning shell as an account. We won against 14 companies, very large ones. And you know, the customer, in fact, our customers, Scott blanket, he was also part of, he was the person who gave us the opportunity to work with him. And in fact, last week, we also signed him as our strategic advisor. He retired from shell and now he is part of the advisory board. And I asked him, you know, why did you give us the opportunity to work with you? So one of the things he said is, you know, he has met many companies in our space in the industrial AI and industrial IoT space. He, obviously, you can imagine the amount, the number of companies would want to who aspire to have shell as a customer. He said, you know, we were the only company which did not try to sell licenses to him, but to actually really cared about the business problem we wanted to solve for him. That’s the feedback. And that’s what I learned from him. Interesting. So always good to ask your customers who have given you an opportunity to ask why they chose you, and your biggest differentiators would actually come from there.

Alexander Ferguson 13:28
That is a powerful insight, right from there that how you talk to them how your conversation was led, it was not focused on now. Would you like to get a license here for this? It was just let’s focus on the problem and, and help find a solution.

Srikanth Muralidhara 13:43
That’s right. That’s right. We empathize with his pain and problem. And we continue to do that.

Alexander Ferguson 13:49
Now speaking of the pain itself, you mentioned the beginning. you’re focused on industrial, industrial by using AI there, but you helping with yield uptime and net zero outcomes? I imagine. Yes, uptime is key. And that’s what you’ve been focusing on. But kind of landing on the last one net zero comes in green is obviously always been a topic but it’s not decreasing. It’s only increasing. How is it over the top? Has it always been all three of those or has has a kind of change and more of the importance and priority that’s put on one of those pieces? Yeah,

Srikanth Muralidhara 14:28
the first one was the net zero obese, we basically added it in the last six months. It was it is but we are and the primary reason is going back tying back to the customer pains and the problems which which is relevant to be solved. And I don’t know like the last four weeks, in fact, has been even more dramatic when it when you see the focus in terms of net zero, right from the investor ecosystem. In terms of how the overall allocation of funds globally is going to happen, you know, big investors like BlackRock, for example, clearly have stated their intent. Saying that, you know, they would their primary, so they would be investing in companies who are really focusing on sustainability as one of the core drivers Otherwise, they know companies don’t get investments,

Alexander Ferguson 15:24
like from investors First, the art of saying we’re not gonna invest

Srikanth Muralidhara 15:28
unless this is a priority. Absolutely, I think that the next thing which would happen is I can see governments becoming more, I think the citizen moment is going to become more, or I think many companies really have to take that seriously. And the third is, obviously, I think there are a couple of things. I think mobile, if you look at all the industries globally, I think the transportation and mobility space is the one which is dramatically going to change. And if you look at the overall emissions, transportation contributes to almost 20% of the emissions. And if you look at electric vehicles, look at the trends, I look at how it’s going to change our lives, you know, you and me, would be traveling in different ways in the next 10 years, guaranteed. And that is going to drive, how the other industries, it’s going to have a cascading effect in terms of how other industries would be aligned, and especially the energy industry that sort of the demand comes down energy companies will have to start looking at different so clearly. Transportation. Yeah, I think it’s going to affect every single industry. And it gives us a good adjacency to scale for future in the future.

Alexander Ferguson 16:45
As has been a member in several conversations with the focus of electric vehicles or other types of movements that Okay, now not as much gas and other types of abuse. So that is gonna, as you mentioned, a ripple effect. How have you seen then, energy companies? Are they already planning for that? Are they already like, looking at things differently?

Srikanth Muralidhara 17:07
It does, you know, you’d be surprised. No, every conversation of mine, customers are asking, Hey, have you guys done anything unnatural? And I’m not saying it. I’m experiencing it day in day out last four weeks, especially have been dramatic in terms of these conversations. Yes. There they are. They are in Westfield, for example, they are looking at solutions where I think that not the first step is, and there is an urgency to it, Alex, that’s what I’m seeing. There’s a lot of urgency. Today, if you look at it, most of the companies have to file their admission reports and ESG compliance reports once a year. And that is not going to help. Because at the end of the day, when when it’s it’s not, it’s not mere reporting or complying to regulatory requirements, they have to take some serious actions where the emissions are really brought down, that means that infrastructure changes has to happen, solutions where they get visibility into where emissions are happening. And also in terms of predicting, you know, if there’s any issue in the plants, which is leading to emissions. So I think they are looking at solutions overall, across the ecosystem,

Alexander Ferguson 18:27
recent shell court ruling that the focus on as you mentioned, government is coming from one side investors from another, your angle looking at this is okay, how can we use technology to help facilitate that outcome? Where does technology really come into play then? versus simply just changing the processes and etc? Is it just managing and knowing what is going on? Is it just more visibility? The technology allows

Srikanth Muralidhara 19:03
multiple things. One is you need to understand the source of permissions right the first that’s, you know, before you even try to fix things, you want to know where things are have gone, or I wouldn’t say going wrong, but it’s more of which sources of emissions do they have to plug within their facilities? And the second thing is, so I don’t know whether you’ve read about it. So broadly, there are scope one, scope two and scope three emissions, which every company has to think about scope, one is their own contribution to the emission, which they need to cut down. The second one is more to do with their dependency on other companies like now they might be purchasing electricity or they may be purchasing water. So that means that how do you reduce the consumption of some of those aspects into their business and hence, reducing the scope to scope three is overall as a company, how does the product which Did which they, which they produce, which gets consumed by you and me? How is that affecting dimension. So broadly, if you look at it, companies have to address all these three dimensions. Or you can imagine, for example, let’s say you produce tomato sauce, for example, for lack of a better example. So that means that you would you would buy tomatoes, and to grow a tomato farmer uses chemicals. So, the chemical gets manufactured somewhere. So you can imagine the supply chain of, of the cause and effect relationship, which a single company has on the environment. So, you can imagine a lot of calculations, so, it’s it becomes a data driven business, so, you need to measure and then it becomes a data business, it becomes a data centric problem. And once you measure, then you will actually look at what are the strategies to cut down those emissions? You know, you could you need to make investments in your sensor technologies, you need to install carbon capture facility, there are companies which are actually coming up with new machinery to capture the coverage or emissions in their facility to dispose it in the in the right way. And then, finally, also looking at the overall, how are they doing on the overall 2050 netzero outcomes?

Alexander Ferguson 21:29

Srikanth Muralidhara 21:30
it becomes a data driven business, Alex,

Alexander Ferguson 21:33
it’s like knowing the data, and in each of those three beats those those three phases, you say, there’s no way one could wrap your head around the world that call comes in. And that’s where you do need to use algorithms and machine learning to be able to find that

Srikanth Muralidhara 21:47
out. Yeah, it’s gonna, it’s gonna be it’s not going to be easy, I can say that it is going to be a complex implementation. And there’s also an urgency.

Alexander Ferguson 21:55
The thing about using machine learning, though, is it’s like it’s, it’s hard to talk about, but not as easy to implement, like, like, for, for general engineers, for instance, like for them to be able to suddenly become data science experts. I mean, is that the expectation?

Srikanth Muralidhara 22:12
of that’s a super question, Alex. And if you look at it, in fact, yesterday, I was having a conversation with the computational fluid dynamics expert in Houston. So imagine a very large network of, let’s say, a water network, right. So they have so you know, for example, from a water treatment facility to for that water to reach your home. There are a lot of things which happens in between, isn’t it? So there are a lot of networks, which does pass, there are treatment facilities, there are, there are pumps, which actually help in delivering the requisite pressure at your home, you have valves. So you can imagine the complex, you might have certain heaters, where you want to control the temperature of water, especially in colder regions. And on the network, you can imagine 1000s of assets or equipments, it can fail anytime. And let’s say, you know, data scientists wants to solve a problem in terms of, so let’s say, you know, this section of the network is going to come down, how is it going to affect the overall pressure on the neural network? You know, do we need to make any changes on the compressor, so it’s going to be humanly impossible for data scientists to solve this problem. And that’s why I’m giving a very simple problem, which you and I can relate to. But these problems have to be solved by experts. And it’s very critical that these experts have to learn data science, there’s no way some of these facilities are going to be managed without the core engineers behind these operations. Also leveraging data science for their operations. Otherwise, you know, in fact, I can’t name the customer but I was having a conversation where this customer was managing a gas network. He was he was doing capacity planning for the network in terms of Hey, you know what, there’s a new city, which needs to be serviced by through our network, so there’s a new city where all the homes in that city have to be delivered the you know, the gas has to be delivered. So what are the investments I need to be making in order to do that, you know, he was saying, it’s so complex, she can’t you know, it’s humanly impossible. for someone to say this molecule is going to travel across this network and reach Alex’s home, it cannot, you know, it’s it’s gas, it’s liquid, you know, the mall. I don’t think there’s any technology in the world, which actually can track every molecule of things which which goes around on the network. I know he was saying I need tools to help me the cause and effect relationship on such a complex problem. Physics cannot solve it. Unfortunately, there are tools which are there. And he was showing me examples of the inaccuracy which exists because of the sheer complexity. Now, you need to consider the ambient conditions, you need to consider the complexity the the interrelationship between these equipments. So there are so what I’m experiencing Alex is there are certain problems where data science is the only way to solve it. And, you know, people who are experts have to be important, there’s no way out.

Alexander Ferguson 26:00
So your your focus in for flutter is, is to basically empower the these engineers to become data scientists to be able to to make it easier for them. Yes,

Srikanth Muralidhara 26:12
yes, you know, some of these higher order problems, there’s no way these experts cannot be kept out of the loop. So on fact, we were we are working with a refinery where, you know, coming back to the netzero problem, right. So they are seeing that the refinery is consuming more fuel in the last three years, you know, suddenly they’ve seen the fuel, which they use for their furnace, in the refineries is eating away a lot of gas, that means their expenses are going up, that means that your scope to exposure on emissions is going up, that means you’re buying more gas. So, that means that again, it has an impact on your carbon footprint. And because of this, we we are now helping them to figure out where exactly the problem is. So they have a heat exchanger train. So, you know, many of the energy which is used is also recycled within the plant, so that you use, you know, you can buy less amount of fuel to support those operations. If so, that means that those recycling systems, which harness the energy which you already generate, and use it back into your operations, so those there are certain inefficiencies which have happened. But just imagine, you know, someone, at least I, I, I don’t have I’m not an expert on on heat exchanges, it has to be an expert on heat exchanges. But if you want to answer the questions, which I’m asking, you need to know, data science, and hence, you know, we are we are our entire product suite Cerebro, which we call it our we are everyday we spend a lot of effort in terms of how to simplify an engineer’s life. So that, you know, these complex operations can be managed.

Alexander Ferguson 28:01
This, you’ve painted a picture of the need for both the concept of Yes, yield and uptime, but this net zero comes from from investors through governance, to focus on it, but it’s such a large problem, and only really, data science and machine learning will can play a role here. And now it’s really a future where engineers are going to have I think, where do you use data, citizen data scientists and then just more and more are going to be able to say Are we need to understand how to use this and have the right tools to make it make it happen? And I feel like I remember you mentioning your, your goal, what’s your What’s your goal for 2024? Is that what it is?

Srikanth Muralidhara 28:45
So we want to deliver a billion dollars of business outcomes to our customers. Secondly, also empower 100,000 data scientists industrial data scientists by 2024

Alexander Ferguson 28:58
Wow, that’s that’s only three years away.

Srikanth Muralidhara 29:02
So we are working a lot in terms of a partner ecosystem channels to scale effectively.

Alexander Ferguson 29:09
I love the the goal though, it sets the vision and where are you guys are headed Thank you so much. Sorry for for sharing the journey that you have been on from from that first initial coffee and and the the mission that you’re on and the goal that you’re headed towards. For those that want to learn more, you can head over to their website. That is Futura. Correct? That’s But to be able to explore it, thanks so much for your time. Thank you, Alex. Pleasure. We’ll see you all on the next episode of UpTech Report. Have you seen a company using AI machine learning or other technology to transform the way we live, work and do business? Go to UpTech And let us know


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