Pod Insights Eye On A.I. What Industrial AI Actually Looks Like | Kriti Sharma, Nexus Black About this episode Most AI is built for people sitting at desks. Kriti Sharma builds it for the people who work in refineries, aircraft hangars, and utility networks responding to wildfires at 4 a.m. and she spends weekends on-site with them to make sure what she builds actually holds up. In this episode, Kriti joins Craig Smith to discuss what industrial AI really looks like when failure genuinely isn't an option, and why the gap between an impressive AI pilot and a production-grade AI system is so much wider in the physical world than most technology companies appreciate.
The conversation is grounded in three specific products from Nexus Black, the elite AI unit Kriti leads inside IFS. The first is Resolve, a predictive maintenance platform built in close collaboration with William Grant's - the distillery behind Glenfiddich and Hendricks Gin - that is projected to save £8.4 million per year at a single factory by reading complex engineering schematics, identifying failure patterns before they occur, and giving frontline technicians step-by-step guidance on their phones without requiring them to remove a safety glove to type. The second is an airworthiness compliance tool for commercial airlines that automates a process currently consuming weeks of human engineering time, where a single mistake carries regulatory fines of up to $20 million and grounding a fleet costs $140 million per day. The third is a disaster response coordination system for utilities, built in partnership with Anthropic, designed to help field crews coordinate during wildfires, hurricanes, and grid outages in ways that, as a California disaster responder told Kriti directly after the most recent wildfire season, will get communities back online and hospitals lit up faster than ever before.
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Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Craig S. Smith? I'm Craig Smith with Eye on AI on the sidelines of IFS's Industrial X Conference in New York City. IFS is the leading provider of industrial AI software. It provides AI solutions for asset-centric industries, think energy, utilities, manufacturing, and aerospace. I'm interviewing Kriti Sharma, CEO of IFS's new unit, Nexus Black, which builds solutions for hard problems. Okay, Kriti, great to meet you.
0:39 Speaker 01 So happy to be here.
0:40 Craig S. Smith? Lovely to see you in New York. Can you introduce yourself to listeners to begin? Tell us your background, how you got to IFS, and how you got to Nexus Black?
0:51 Speaker 01 Yeah, sure. I'm Kriti. I grew up in India. As a kid, I used to build robots. They didn't solve very important things. The first robot I built as a kid, it helped fat Snickers, the candy from the snack bar, at 3:00 PM every day.
1:05 Craig S. Smith? Oh, really?
1:06 Speaker 01 Much to my parents' delight, I do better things with my life now. I'm a computer scientist by background, and then started building AI businesses and products in the last 10 to 15 years. And for the most part in my career, I've built AI businesses and products for people who work at their desks, accountants and lawyers and so on. But I was fascinated by building businesses, products, experiences for people who are not at their desk. And that's what brought me to IFS. We started Nexus Black earlier this year. It is a business inside of IFS that works with the most strategic clients, boots on ground. If you think about AI with a hard hat on, that's us.
1:48 Speaker 01 We go on the ground, we spend weekends and hangars, we spend our days in refineries and factories, and we travel along with the field technicians so that we build things where failure is not an option.
1:58 Craig S. Smith? Yeah, and given your your background in in robotics, I'm just curious, where where did you study? Where did you uh study computer science?
2:08 Speaker 01 Um at Saint Andrews in the UK on the east coast of Scotland, um lovely tiny, small town.
2:14 Craig S. Smith? That's a wonderful school, yeah.
2:16 Speaker 01 Yeah, thank you. Um I I've always loved building things and I'm so glad I get to do that now in the physical world. It's every day is a joy. Um and and yeah, you start to learn how different it is to build things for people at their death's horses. In factories and hangars and plants and sites and in the field, it's um it's much more challenging and more fun.
2:36 Craig S. Smith? Yeah, and and I was talking to Mark Moffett earlier about uh some of the the uh work that you guys are doing with uh Boston Dynamics and OneX uh on, I I guess it's still called Spot on the robot dog and uh and with Neo, the humanoid robot of uh how and and is that your focus at Nexus Black? Or I I know you're also working with Resolve. Was that a Nexus Black uh product?
3:08 Speaker 01 Yeah, so Nexus Black products are um regardless of the form factor. So we're very focused on building the right tool for the right problem that needs to be solved. And we have a team of elite engineers. So these are um maybe perhaps the the most um rare people on the planet right now who built production-grade AI products already before. And we combine that with deep subject matter expertise and I'm having to talk about what that really means because everyone comes and says, you know, they're building deep subject matter expertise in our products. Um so I'm having to talk about that, but we're um agnostic of the form factor. Whether you're deploying it in a humanoid or you're deploying it in a robotic environment or a much more digital form factors.
3:51 Craig S. Smith? Yeah, and on the digital form factor, uh which isn't really a form, I guess, it's uh is uh uh you're doing a lot with agents, I imagine.
4:02 Speaker 01 Yeah.
4:02 Craig S. Smith? And and I I'm curious about. Uh how you you you see the agent workflows developing. Uh because from what I understand, there's a lot of excitement, a lot of promise, but. Most of the pilots out there are not going into production. Uh for various reasons and and how do you, how do you see agents developing?
4:27 Speaker 01 So we focus purely on the job to be done in Nexus back and by that I mean. We go on the ground and we, yeah, we'll make a bold claim and I'll back it up in fact. Um we start to see value for our clients in about three weeks.
4:44 Craig S. Smith? Yeah.
4:45 Speaker 01 And the way we do that is, I I've worked in AI my whole career since I was a kid, as I mentioned, and I. I I absolutely would be the person who detects AI washing and and statements like this. The way we do it is. I don't think there's any other way of solving these problems and making it successful in the physical environment, except for being on the ground.
5:05 Craig S. Smith? Yeah.
5:05 Speaker 01 So we get out of our comfort zone, of our of our world, we're on those sites. And we look at the situation that we're dealing with, the kind of data, the real problems, and and I'll give you an example. When we work with field technicians, they tell us, if I have to take my safety glove off. To type into a thingy to use your thingy.
5:24 Craig S. Smith? Yeah.
5:24 Speaker 01 I'm just not going to do it.
5:26 Craig S. Smith? Yeah.
5:26 Speaker 01 That teaches us what we need to really use. Um here is voice transcriptions and other sort of form factors and capabilities. Um oftentimes you might think, oh, this is very cool. I can use an image model to scan a thing and solve a problem. And then you realize, when you work with the engineers, that the often the first. Signal of something's about to go wrong is a vibration or pressure. So all of these little nuances, you learn those things and you refine. You're building something for. A site or a plant in in the offshore environment, there's no wifi in the Pacific. You have to learn to build in this hard world. And when you solve these real problems on the edge, on the ground.
6:08 Speaker 01 With the people who are gonna use it, then your chances of failure dramatically reduce.
6:12 Craig S. Smith? Yeah, uh, and that's interesting, uh, working on the edge in environments with lower, no connectivity. How, how do you do that?
6:24 Speaker 01 Um, we compress a lot of that knowledge and we deploy it um on devices to be able to do that work. And all of these problems are solvable. Um, the value really comes in from knowing which problems need to be solved. And it's really also no longer about the best technology or the best model. It's about using it in the best possible way to solve a hard problem. And that is where failure occurs if you don't identify the right high value solution. You don't really identify these constraints that our solutions need to operate in. You don't understand deeply what's going through the emotional hopes and dreams and fears and needs of these um workers. Um, then it's very hard to build a product.
7:06 Speaker 01 When we get these things right, when we know the constraints they're operating in. When we know their, their real emotional and practical fears and challenges, we work with them. That's when magic happens.
7:18 Craig S. Smith? How big is your team and, and when you go out onsite? Uh, there was the example of the distiller. Uh, and I think that was resolved that you were applying there. Uh, you're going alongside with, with the worker, kind of watching what they do. Uh, how, how many people do you have doing that with one client, for example? Is it a team that goes in? Is it one person that's responsible for absorbing all of that? And then when you go back and, and, and build the product or, or tweak the product for that use case. Are you, uh?
7:59 Craig S. Smith? Fine tuning of uh uh an anthropic model or you and is it always with men? I'm just curious how that works.
8:08 Speaker 01 Let's walk through that with an example, because I did make a bold outrageous claim earlier. I told you I was gonna back it up, so let's go through it. Um let's see the example of what the work we're doing at the distillery you mentioned. The one in in in um in the conversation here is William Grant.
8:24 Craig S. Smith? William Grant.
8:25 Speaker 01 They are the brand behind um Glenfiddich Whisky and Hendricks Gin. Um they are on the west coast of Scotland, which brings me back to Scotland a lot. I love that. Um so we went on the ground and we understood just very rapidly, within a few days, what their challenges were. And in their case, the issue was a production loss. When a machine goes out of whack and something breaks, they were spending about, you know, thirty eight percent of their time in emergency and corrective fixes. And that takes so much more time. You plant is down the site, you lose batches, right? Now, that's point one. Problem two, the number of technicians who have the deep domain knowledge um that that is quite constrained and they have to, they're expanding, so they have to do a lot more work with a what fewer resources.
9:10 Speaker 01 Some of these are, you know, challenges that are not just unique to them.
9:13 Craig S. Smith? Yeah.
9:14 Speaker 01 On the ground, we observe what's going on with the technicians, how the plant is functioning, and then started to build a knowledge about a representation of the factory, of the plant, and what's going on. And some of these challenges and the data, the knowledge that is represented here is not in formats you and I would usually hang out in and read. For example, how a plant, a little pump or a seal connected to the pump, is connected to the rest of the environment, is represented in a complex engineering schematic diagram called piping and instrumentation. Now, these things, if you look at it, it's like, oh, how do I even make sense of it unless I'm an engineer on the job with twenty years of experience? So we take capabilities like Claude um from Anthropic,
9:58 Speaker 01 and then we domain train it and we push the boundaries. And this is what Garvin from Anthropic yesterday was saying with our lead team of engineers, we just work so closely together. We push the boundaries to make it understand what is a piping and instrumentation diagram. So when something goes off or before it goes wrong, we can see not only that part that needs to be fixed, how is it impacting upstream and downstream? And these are the kind of moments where you go from. I did an A I pilot, it was like, yeah, I kinda did the job, but not really ended up with a chatbot. Or some sort of an agent.
10:31 Craig S. Smith? Right.
10:32 Speaker 01 To, I now have a solution that understands my site, my plant, what's happening here, what's the connection, how does it fit? And then it's helping my technicians, working alongside them, get an answer and solving the problem. The result in the case of William Grants is by their own uh assessment, they expect to save about eight million, eight point four million pounds. Per year at just this one factory, now you scale that.
10:57 Craig S. Smith? Sure.
10:58 Speaker 01 So, anyway, long story short, it works when you are on the ground and you're not scared of piping and instrumentation diagram.
11:05 Craig S. Smith? That's right, or or you have a a a model that can read the piping and, yeah. Uh, the uh, uh, the uh, being on the ground, I mean, I talked to a lot of companies. That are offering uh off the shelf solutions. Uh, and and part of the problem, it seems, in penetration or uptake. Uh, at the enterprise, particularly physical operations, is is a one size. That's all doesn't really work. You, as you say, you have to go in and see that that the guy's not gonna take his glove off, for example, yeah.
11:47 Craig S. Smith? How important is that bespoke uh element? To the adoption of A I in in the industry?
11:57 Speaker 01 I I think is more important than bespoke is deep domain understanding. So, if you are building, so we spend a lot of our time in these industrial situations. We only operate in these specific industries and manufacturing and utilities and aerospace and defense and service and talco construction engineering. We don't venture outside of our lane, because that means when I go into these universe, in these industries. The next and the next and the next company I speak with, I I understand their.
12:27 Craig S. Smith? Right.
12:27 Speaker 01 Needs better. I've solved the the ability for A I to read these complex engineering diagrams once. The next time, we can do it like that, right?
12:36 Craig S. Smith? Right.
12:36 Speaker 01 So over time, you start to build specialization and you start to scale said specialization. The magic now is previously it would have taken.
12:47 Speaker 01 A lot of people, maybe possibly even hundreds of engineers, years worth of effort to make some sort of technology or software work for manufacturing supply chain.
12:57 Craig S. Smith? Yeah.
12:59 Speaker 01 That's not how it works anymore. The timeline has shrunk. So the people um who we work in my team, they are of course some of the best, mostly AI engineers, but we pair that with this expertise of what happens in the manufacturing world. Experts in utilities, just across the room from here, we have a number of our key utilities, customers, the most advanced and subject matter experts right now building the next product. Can't tell you what it is, but I will.
13:25 Craig S. Smith? Yeah, Resolve is a standalone product.
13:28 Speaker 01 Yes.
13:28 Craig S. Smith? Was that developed for William Grant or?
13:31 Speaker 01 It was developed with the first set of um clients that are in asset-heavy environments.
13:37 Craig S. Smith? I see.
13:38 Speaker 01 And plants and out to the field. And we did this with a bunch of them, so we end up not making something that only works in that environment. And that's a good example of how we can deploy something and create something very quickly, very closely with our clients, and then scale it to a much broader base. Now, for for your listeners and viewers here, um they're probably already um quite aware of this new generation of AI companies where a small number of employee base is creating tremendous amount of value, exponential value in revenue and valuation terms. And that has been now possible because of the ability for the creation of technology itself to have um gone so fast and so much more advanced. And that is that is what we do now, too. Say, a small number of people who are highly capable
14:23 Speaker 01 and highly specialized, um who know how to use AI tools themselves to build sophisticated solutions that are production-grade in a matter of weeks.
14:32 Craig S. Smith? Yeah, and that's important, but production-grade, because, as I said, so much of what's going on right now is is piloting and and not making it, yeah.
14:42 Speaker 01 Yeah, I can't make that work in my industry, right? In my world, it has to be production-grade. You know, we work with Airlines in um in compliance and airworthiness, air safety. It every time a flight takes off, about four hundred lives are at stake.
14:59 Craig S. Smith? Yeah.
15:00 Speaker 01 We can't mess that up. It's it would be reckless to do anything that's not production grade, enterprise grade.
15:05 Craig S. Smith? Yeah.
15:06 Speaker 01 That's what we do.
15:07 Craig S. Smith? And Resolve, can you just describe what's inside that word?
15:12 Speaker 01 So, um Resolve is a product we've designed for the frontline workers in the factories and out in the field. The way it works is it understands the issues before they occur. So it's constantly monitoring systems at a plant, in a site, and what's going on through sensors. So it's temperature, pressure, noise, sound, images, video, um to detect what might be going on and past failure patterns as well. And then using that, it figures out what what might be the issue. It reads those schematics I talked about, upstream pressure buildup, downstream production loss. And then helps create a series of detailed instruction sets like an engineer would to give you the tools as a frontline worker to be able to do the job right the first time.
15:54 Craig S. Smith? But uh in that you're a frontline worker, this isn't someone sitting at a at a computer.
15:59 Speaker 01 They're out.
16:01 Craig S. Smith? So how are they receiving that?
16:02 Speaker 01 Um on their device. So they're just getting it on their device. They don't have to type because of the situations we talked about. They can use their voice and get the response um in the right way depending on the form factor.
16:13 Craig S. Smith? And and you guys manufacture the device or?
16:16 Speaker 01 No, it's just their phones.
16:17 Craig S. Smith? Could be anything, yeah.
16:18 Speaker 01 Yeah, they can use it on their mobile phones. Um and usually we deploy it on um on the kind of phones that are um that are cleared for usage in the factory environment because of fire and safety risks.
16:30 Craig S. Smith? Yeah, uh what are some of the other products in the pipeline on?
16:34 Speaker 01 So another one we talked about yesterday at the event here in New York um is something we're really proud of. Um this is our solution that we've built for airlines, commercial airlines and airworthiness. Um what that means is every time a aircraft manufacturer or the um FAA, the Aviation Authority, they release a guidance for some sort of service bulletin or airworthiness directive, which means things that manufacturers or operators of aircrafts need to do to keep the fleet safe.
17:06 Speaker 01 Now, these things, these directives, they come in the form of hundreds of pages long documents. And this is not just words in the documents. There are pictures, there are detailed specifications of parts. And getting it wrong, the consequences are huge.
17:21 Craig S. Smith? Yeah.
17:22 Speaker 01 Each time you get it wrong, you could be faced with a one to twenty million regulatory fine dollars. If you have to ground the fleet, because you couldn't do this in time, you're talking about a hundred forty million dollars in lost revenue per day. And then the worst case scenario, it's a risk to human lives. So this is failure is not really an option. Getting it almost right, not an option. So we read through these directives. We compare it against the fleet of information that we already have, so the tail numbers. Um, all the aircrafts that may be in operation. We look at all the parts and then we do the planning and the maintenance work of. Um, what you need to do, which part exactly needs to be swapped out. Now, in in normal situations or in non non-technology situations or non-Nexus Black environments.
18:07 Speaker 01 Um, human engineers are spending weeks reading through this information and trying to make sense of what is the action they need to take. What's that part that needs to be swapped out? What kind of information they need? And when do they schedule it? Because the planes are coming in and out. So. These are, these are the kind of problems we love solving because they are complex and the impact and. Um, and the value we drive is huge. Um, and I would love to tell you about one more, and this is really the closest to my heart. Um, this is the work we're doing in the utilities world when disasters hit.
18:40 Craig S. Smith? Yeah.
18:40 Speaker 01 Um, disasters and major events are sadly becoming more common. Um, these could be weather-related events like snowstorms, as we see here in New York, um hurricanes, wildfires in California. Or outages like what we saw at um Heathrow Airport, I live in London. Um, or what we saw in Iberia with the uh cutoff and blackout.
19:00 Craig S. Smith? No.
19:01 Speaker 01 And when these kind of moments occur, um, there is a lot of challenges the frontline workers have to deal with. They have to go in and solve a problem when a lot of the infrastructure might be submerged in water. You know, um, and they're having to coordinate, they send a bat signal out to all their other crew friends in the nearby utility areas. They all pool together, they meet in the parking lot of a Walmart to figure out what instructions there are. To go tackle the situation, whether it's to bring a power line back up. Or it's evacuating people from their homes until the situation is resolved. Um, now in partnership with the work we are doing with Anthropic on the upcoming capabilities of the models.
19:41 Speaker 01 Not only to answer text or reason through it, but take actions on long range planning. We can now solve these problems in a way that has never before been possible.
19:54 Craig S. Smith? Hmm.
19:54 Speaker 01 That means coordinating this complex amount of action. This is the hardest time for frontline worker.
20:00 Craig S. Smith? Yeah.
20:01 Speaker 01 They get a call, four A M, the storm was gonna go this way, it's coming here. The fire is escalating. And for me, it gives me goosebumps.
20:09 Craig S. Smith? Yeah.
20:10 Speaker 01 When I hear firsthand from um, people who are. Who have been responsible for leading disaster response, uh, for example, what we saw in California, beginning of this year. Um, the same person called us yesterday, they sent us a a note saying. Um, this is, this is so amazing, cause it's gonna help us get communities back online and hospitals lit up faster.
20:32 Craig S. Smith? Yeah.
20:33 Speaker 01 Um, it it really, right now, I'm I'm feeling the goosebumps, cause to me, that's the power of technology. This is why I I I love doing what I do. Uh, you wanna go back to your thing?
20:43 Craig S. Smith? Well, I was gonna ask you, when you're on an airline and and they, there's a problem and. Some, and they need to fix something, I'm always, how do the guys know? You know, some light is not. Lit up or something, is that a a use case for you guys? Or you're really in the uh in the the maintenance center?
21:06 Speaker 01 So we do a lot more on the of the maintenance planning work and maintenance execution work. And and things where we know that there may be a fault with a part that needs to be swapped out and something else needs to get done. Um and and these systems are getting more and more sophisticated. When a plane goes into the maintenance hangar, a lot happens. Um my team and I have been inside and we spent the weekend at a hangar. Uh we went inside a stripped down plane. Um and we as you walk through the plane is do you do see that you're trying not to triple where the legs of the engineers are lying on the floor just doing things. Um and oftentimes it's managed on um pen and paper cardboard situation. It's kind of wow, that has to change. Um and now we're giving them these tools that give them much more confidence.
21:50 Speaker 01 Um and also there's a lot of tension on the ground, you know, the engineers want to do things a certain way and the planners sitting in their offices, they want to do things a different way. Um and now we can bring the two together, um process huge amounts of data, but also take the right actions at the right time.
22:04 Craig S. Smith? Yeah, or is your team growing? I mean, where is this going? Because it seems like the possibilities are endless.
22:11 Speaker 01 Yeah, we are growing. If you know people who are very excited about solving hard problems out in the real world and they love the physical environment and they love um the the world of making technology solve problems. Um then we are the people to to come and talk to, uh we're at nexusblack at athos dot com. Um that was a little plug there. I I do think the the importance, the the magical moment now is you don't have to be a geek like me, who is a computer scientist and roboticist type person. Um to come and solve problems in this world. Um you can be a subject matter expert in your domain. You can be someone who is just exceptionally curious person with very low ego. You're all welcome.
22:51 Craig S. Smith? Yeah, okay, great.
22:53 Speaker 01 Very good to meet you.
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