Zulema Quintans, COO of Noda, on Why Your OKRs Don't Get Done

Speaker A: M In the US alone, commercial buildings spend about 200 billion every year on energy. And somewhere between 30 and 50% of that is just wasted. Buildings are very complex ecosystems. They have aging equipment, messy data, and many m hands that touch that data and that equipment over time. So inefficiencies are inevitable. So what used to take, you know, weeks can now be done in hours. And that dramatically reduces the engineering sharing time and Lyft.

Speaker B: Before we get into it, follow Between Two coos in your podcast app right now so every episode lands in your feed. When they drop, the next few are worth catching. We've got the former chief operating officer of Grindr, who took the company public at a 6 billion dollar valuation. Then we have the chief operating officer of Cowboy Space, who's building data centers in space and the rockets to get them there. And we also have the COO of Katana, the platform behind more than $3 billion in transactions for SMBs selling physical goods. And finally, the CEO of Zapier, a company on the forefront of and automating your operations. These are real operators building incredible companies, talking about how they actually run things. Hit follow so you don't miss them. Um, hello and welcome to Between Two coos. I'm your host, Michael Koenig, and today I'm joined by Thulema Quintans, COO of noda, a company that's pushing the frontier of how buildings operate by using AI to make them smarter, more efficient and more sustainable. Thulema has a fascinating career arc. She started out as a Juilliard trained professional dancer, went on to Bain and American Express, and now she's helping scale a climate tech company that's rethinking how we run the built environment. Along the way, she's led strategy, operations and transformations that boast massive enterprises and fast moving startups. In our conversation, we're going to dive into all of that and see how NOTA is building its own operating system to scale. Thulema M, thanks so much for coming on. Let's jump in.

Speaker A: Thanks for having me. It's great to be here.

Speaker B: You started your career as this Juilliard, uh, trained professional dancer. It obviously not the typical path. So what. Walk me through this. And um, also like, what lessons did you take from performance to and choreography that have carried through to how you lead?

Speaker A: Yeah, I'd say my path to NODA was both unexpected and very natural at the same time. Unexpected in the sense that, you know, I spent the first half of my life thinking I wanted to be a professional ballet dancer. I Did not envision being the COO of a tech startup. Um, but ballet is a, is a short career, so I always knew I'd need to plan for a second career. I ended up in business where I was always kind of naturally drawn to strategy and then later entrepreneurship, the parts of business that always felt the most creative to me. Um, I think as a dancer, I really enjoyed, especially towards the end, working with new choreographers and helping envision and bring new ideas to life. And strategy was always very much like that. Um, so, you know, and then as a dancer, you're always being asked to learn new things and step into new roles. You really have to excel at learning, uh, and be adaptive. And being in a technology environment, you're constantly learning and having to adapt.

Speaker B: And it's also highly structured and predictable.

Speaker A: Yes. So that's, I think, the other really interesting, I think the lessons that really shape how I think about leadership now in this capacity. You know, it's this very creative profession in one sense, but it also requires extreme focus and discipline. So it's about holding both focus and flexibility at the same time. And that's a balance I think a lot about. As a coo, you know, you're often cast as the focus person. You're the one driving okrs. Discipline, structure, repeatable operating processes. But just like in dance, where the moments of, I think the greatest artistry really come when you're least expecting it. When there's an element of improvisation, you want to be flexible so that you're also anticipating change and you're open to those moments of change. Magic, huh?

Speaker B: How do you balance that? Ah, at, uh, what point do you look at it and go, you know, what, how I was planning to do this and the things I, I, I imagine you take a lot of comfort in planning, um, and in having that predictability. How do you, one, push yourself to be outside of that in the flexibility range? And then two, how do you decide when? Okay, we need to be a little flexible here.

Speaker A: I think it depends on the context you're in and what your, you know, what your objectives are for that particular quarter. I really like okrs as a framework, um, both because they give that clarity to the teams, but also visibility at the leadership level. On, are we going on or off track? They're also kind of a good check and balance on, like, did we come up with the right goals? Are we being ambitious enough? Are we, you know, are we getting things right? Um, I think it depends ultimately on how much time you have. Right. With the benefit of time you can give teams more space to come to the conclusion on your own. And so I think that's where I try to balance. Sometimes when you're moving fast, you have to be super directive and, um, push people to get to that outcome. Um, but then when you have periods of, um, exploration and you have that little bit of time, it's always good to give teams more space to come to those conclusions on their own, even if it takes like an extra day or an extra week.

Speaker B: Well, let's talk about OKRs, probably the most predominant strategic framework that's out there, at least within the tech sector. But so many times they fail and fall right on their face. What is it? How do you approach them that's working so well for you and your team at noda?

Speaker A: I think there's a lesson in failure. Right? We are all learning from failure all the time. And okrs, if you, you know, you're not trying to get 100% accuracy, success is like you're getting to that 70% mark so that they're, you know, lofty and stretchy enough that they're pushing everybody just past that, that, that comfort zone. Um, but I think there's lessons, you know, in, in the ones that don't work. And why didn't they work? Because, you know, sometimes, you know, we. If you have to change your product okrs in the middle of the quarter, that tells you that you probably didn't have a good, firm enough understanding of your product roadmap going in. And that's something you can, um, get better at. So I think there's always lessons, um, in the failure, and they're a really good way for leaders to get, um, a quick pulse on the business. And they also just keep everybody engaged and feeling like they know what they're doing and how that contributes.

Speaker B: Interesting. Now let's talk about the 70 completion rate, because a lot of the time I hear this and folks are, are maybe a little demoralized about this, where it's, uh, I'm setting these lofty ambitions knowing that I'm only going to get 70% of the way there. Like, do you ever find complacency or just. Or just people being down about it?

Speaker A: I mean, it's natural to, you know, not, uh, to want to shy away from things that aren't going well. Um, I'm sure you've seen this. There's this great, uh, video of Roger Federer, the Dartmouth address, where he talks about, um, failure in tennis and how, you know, even at the top of his game, he only WINS Just over 50% of the points. And so he's got to get really good at putting those failures in the rear view mirror so he can focus on the next point. And I think that's a really good um, analogy for, for these examples as well.

Speaker B: Let's get back to the flexibility aspect to it. With okrs is so rigorous and structured that during that quarter or during however long your cycles are, as you mentioned, new things pop up. How have you figured out that balance of evaluating whether or not those new strategic aspects need to unseat something that you've already planned and agreed to?

Speaker A: I think that the OKR framework is a prioritization tool for leadership. Like you can make those informed trade off decisions of hey, this new thing has come up of uh, this level of urgency. Okay, what's coming off the list? So that's one, I think the other thing that's really important in OKR design is they have to be both a combination of top down, right aligned with the strategy where you want to place the emphasis, but also bottoms up. They often fail when teams, you know, you put out these goals but they're not actually linked to projects or initiatives um, that are, that people are actually working on. So making that, you know, closing that feedback loop and then also increasingly integrating them into the performance management system. We actually just put our okrs into the performance management system and you know, people are paying a lot more attention to it.

Speaker B: That's really interesting. You, you mentioned closing the loop between bottom up and top down. Uh, with top down being, you know, where that strategy is going to come from. I think when you're saying bottom up as well is that you're taking the feedback from the team of here's what we're seeing. So it's really creating uh, connectivity but the, where the team is informing you of what they're seeing on the front lines and then you're taking that and forming the overall strategy.

Speaker A: It's definitely that. I think in fluid tech startup environments you constantly are toggling between the people that are closest to the customer and the ground with what you're seeing externally in the market and where you're trying to go, um, six, 12, 18 months from now. So there's that balance. But I think more importantly to the success of delivery of an OKR in the quarter and is that work, that OKR has to be linked to somebody's job and to a specific product or specific roadmap or project and that work needs to be identified, it needs to be prioritized within within the teams and it needs to be happening. And that's often where I see things not getting done or things not getting completed on time because we said we wanted to do this thing but we didn't actually know how to do it and we didn't make it someone's priority.

Speaker B: Well, let's talk about the market driven aspect to it. There's a balance of making decisions that are influenced by what you're seeing in the market versus making decisions based on what your North Star as a company is. How do you take those imbalance those inputs and signals from the market with what your plans are?

Speaker A: Yeah, I mean I think it's a great question and it's, it's certainly evolving. Um, you know, I think you have your roadmap, you are constantly getting feedback from customers and so it's finding that balance on, you know, you can't just listen to the loudest voice in the room. You're trying to balance things. You're getting feedback from all directions. There are internal stakeholders who want internal tools that are competing with customers who want new features. And then you kind of add investors. And so there's a lot of surround sound. And so I think this, you know, the role of the CEO and certainly like the strategic planning process is to try to bring all these things together in a thoughtful way so you can kind of assess the trade offs based on where you see the biggest opportunities.

Speaker B: That's interesting. Now when we first started talking about OKRs and the flexibility, you mentioned, uh, that sometimes there's, it's all about the time that's available and that allows the flexibility versus if something there isn't a lot of time you have to be more directive. Huh. How do you walk that fine line between being directive and micromanagement?

Speaker A: I think, you know, it um, comes down to the delivery. As a leader you can impose, you can, you can pose questions to your team to solicit the answers. Those questions can be broad or narrow. Um, but that technique, um, puts the onus on the team to actually arrive at the answer. And so in those moments where you're really trying to get to a solution, um, you know, I think having very, very, very, very tight questions come can sometimes get you there faster.

Speaker B: Last question on okrs. Well, I promise we're going to talk about other things but uh, you all have, have really, it sounds like cracked the code. So um, I always love learning about how other leaders and other companies are doing this in terms of the cadence and implementing OKRs as an actual operating system by which Your company is running. How do you, aside from just setting the okrs? Okay, here's what we have team. This is what the direction is. Um, how are you going to contribute to these? Um, but past that like drafting portion, how do you systematize okrs throughout the quarter so that the focus is always on them?

Speaker A: Yes, that's a great question. So you know, we um, it's a collaborative process so we typically think about our objectives, you know, the overarching objectives for the okrs on a six month time horizon. So those are kind of set twice a year. And then you develop your key results really what the performance you're trying to drive in quarter, every quarter. And that becomes a collaborative process with the team. You know, senior leadership will suggest some and then we have them work with the teams to identify them. Every OKR gets an owner. Then we publish these, we talk about them in our um, in our all hands, our monthly all hands. I also spend a lot of time thinking about how do we like reinforce them internally and kind of and embed them in all of our communications. How do we celebrate wins and highlight projects and success that are contributing towards these results? Um, I mentioned this before, but we've also um, we put them in our um, performance management system. So there's actually a record of your goals. So when you have to go into your performance review, you can see the progress and your manager can see the progress. And you know, in that context people take them more seriously. So I think there's you know, collaboration, reinforcement and visibility and then also tying that into um, your performance conversations are kind of three ways that you can really reinforce and get people to focus on them.

Speaker B: Uh, quick note for listeners, this episode gets into something I care a lot about. How operators turn messy, complex work into systems that actually scale. That's also why I'm starting the between two coos newsletter. The podcast is mostly me interviewing other people. The newsletter is where I'll pull out all of the practical takeaways from guests, the patterns I'm seeing across operators, and some of m my own notes from having been in the COO seat. So if you want the frameworks, lessons and useful operating notes around these conversations, go to between2coos.com or b2coo.com and subscribe. So let's talk about Noda. This is a climate tech company and just for listeners to understand, there is a difference between climate tech and clean tech. Clean tech being some of the things that are actually going to power the buildings and society versus climate tech that's focused on optimizing those. How does NODA play into this? What do you guys do exactly?

Speaker A: Yeah. So NODA is an energy management software platform for the commercial built environment. In the US alone, commercial, uh, buildings spend about 200 billion every year on energy. And somewhere between 30 and 50% of that is just wasted energy. This is where NODA comes in. NODA helps commercial building operators stop that waste before it happens and reinvest it back into the things that matter.

Speaker B: And how do you do that? Because now we're talking about physical infrastructure. Buildings that are hundreds of years old in some case, um, or not so much, but buildings that were built during a time when technology wasn't as efficient and certainly not connected in a way that we think about with IoT. So how do you actually do that with this legacy infrastructure?

Speaker A: It's a great question. You are right. Uh, buildings are very complex ecosystems. They have aging equipment, messy data, and many hands that touch that data and that equipment over time. So inefficiencies are inevitable. Um, I think there's also this interesting challenge and perfect storm that is, you know, building operators are facing right now. You know, if you read the newspaper, it's hard to avoid articles on the rising electricity prices. In addition to that, you've got, um, you know, increased compliance burdens for reporting, um, as well as a labor shortage in this industry. So one in three building engineers, the teams that really take care of these buildings, are retiring and those positions aren't being backfilled. So this is where technology can come in to help automate, um, some of those workflows for the building operators to help them meet their goals, both in terms of reducing their costs and also reducing their energy use. Yeah.

Speaker B: Ah. And we have a significant lack of electrons that are, uh, within the U.S. i mean, the energy dependencies that we have, plus the energy needs of all of the data centers that power AI are quite significant. So reducing the amount of electrons that a building is wasting is highly significant. About this, in terms of the software aspect, I mean, I get, we get an idea of how software can integrate with this. Very, very difficult, of course, but the actual connecting the building to the Internet to get those signals, how does that work? Like, say I've got, um, a building that's 50 years old and it's got a, uh, really old boiler. And maybe the only signal that I have about my electricity use is from the meter that I check or the bill that this building gets from the electric. Like, how does that work?

Speaker A: Yeah, it's a great question. So building data is very messy. But we have something called an independent data layer and a ontology of equipment in the building. So we can really connect into a building through a variety of different ways. We can connect into the building management system, we can connect it into smart meters. We can also collect data through things like utility bills. We bring it into our platform, we standardize and we clean that data so that you have consistent ways of describing points throughout the building. That's really the foundation, um, for any of the AI applications that you might want to run. Right. AI is only as good as the data that you feed it. So having really clean, structured data is a really important foundation for everything that we're trying to do.

Speaker B: So once the data is there, they're like, what do you do with it then? Because that's always been a really big problem with companies that are like, oh, we're connecting this data. And I've mentored so many startups through some of the accelerators and they're like, yeah, we're going to have this data and we'll sell that data, but what do you actually do with it and how does that translate to like, the physical changes that are going to reduce the carbon footprint of these buildings?

Speaker A: Yeah, so I'm glad you mentioned the carbon footprint because, uh, globally commercial buildings are responsible for 40% of global greenhouse gas emissions, a pretty staggering figure. So thinking about how we operate them, um, is going to be a meaningful way to reduce the impact on climate. We've taken a modular approach to how we think about product design for this technology, really, because we need to meet customers where they're at in their automation journey. So we start with a reporting layer which puts more of the onus on the operator to understand what's going on in their building, identify and take action. Then we move into, um, a layer where we actually, our analytics as well as our service team really comb through that data in the building and understand where they can drive projects, ah, to reduce costs and reduce energy use. And then thirdly, we have our automated layer, um, which is where the technology gets really cool and exciting. So I'll give you an example. Um, imagine you had a knob on your desk that controlled all the thermostats in the building and it's making small adjustments throughout the day. So, um, reducing, um, cooling parts of the building early in the day when there's more clean energy, it's cheaper, and then easing off on that later in the day. And these adjustments are so small that the people in the building don't know that they're happening. So there's really no disruption to the 10 experience. Um, but you are driving meaningful change and savings and energy usage for the building.

Speaker B: So. Interesting. And you talked about all the data and going through the data later, later. Data, excuse me, data layer first. Uh, certainly this is something that AI can excel in. How are you all embracing that and, and working with that to, to really bring that to market?

Speaker A: Yeah, so this is one of the top priorities for me as CEO and thinking about how do we use AI to automate our own operations, support and deployment. So as I mentioned, data that comes from a building is very messy. And typically engineers on my team would spend hundreds of hours mapping all the different systems, systems and points in the building. We've started to automate that process. Now we can use AI to read the names, the descriptions and help assign those points automatically. Um, and so what used to take, you know, weeks can now be done in hours. And that dramatically reduces the engineering time and lift.

Speaker B: So if I'm a building operator and just to pull back so listeners can understand the scale of noda, some of your biggest clients are Marriott and Hilton. I mean these are major, major real estate holdings and property owners and they contribute, they have huge carbon footprints. So in terms of actually adopting this automation, like, what are some of the big challenges that you're seeing m with building operators trying to actually do this?

Speaker A: I think it's a great question. I think, you know, technology like this, and certainly that example I described, which is automated demand that can't work in isolation, certainly not in the context of buildings that are so unique and complex and full of nuance. And so this is where I think having a really strong service bubble around the customer with technical expertise to drive things like product adoption and integration, change management and technical support are increasingly important as we think about how the market is going to adopt and bring these tools into their buildings.

Speaker B: And you recently had acquisition, uh, fairly sizable with building IoT. Can you give us first quick, uh, rundown of, uh, building IOT and what that brought to noda and then how that's helping you all meet these challenges?

Speaker A: Yeah, so buildings IoT had this incredible technology stack. We were in the process of thinking about how we relaunch our energy products. So they had this technology that allowed us this independent data layer, this project ontology and automated controls, as well as really deep expertise working with building management systems that complemented very well the operational savings, ROI and service components of our energy product. And so it really helped us expand our product suite so that we had offerings at every stage of a customer's particular automation journey.

Speaker B: Let's talk about AIOps, which you use, or rather AI in Ops, which you started to talk about. Um, we've gone through your operating system. How now are you thinking about, uh, AI within your operations? What have you done so far? What do you see in the future? Where are the biggest impacts that you can have with this?

Speaker A: Yeah, I mean, I think as you know as a CEO, we're always often the ones championing internal tools. Right. How do you use automation to drive more productivity? One of the things that I'm excited about that we're prototyping is an agent that can help identify some of these cost savings. Working with a smaller data set. This is allowing us to really speed up our customer onboarding and cuts time to value for the customer pretty dramatically. It's also allowing us to completely revisit the whole choreography of everything that happens post sale for the customer. So I think broadly any workflow that really touches the customer is going to go through an exciting period of transformation. It, you know, I think in the past you've had one of two options. Doing something that's bespoke and expensive or one size fits most. And I think what AI is going to allow us to do, it's going to create this new middle segment where tooling can drive more customization and personalization, um, in a more cost effective way, um, that allows you to bridge between those two, two extremes.

Speaker B: Well, it's an interesting example that you gave this AI agent. Is that something that you all are building? Is it something that you bought? How, how are you approaching just this actual adoption?

Speaker A: Yeah, I think we are definitely, um, you know, we're an AI focused company. We are definitely prototyping and experimenting with AI agents both to solve our internal workflows and eventually, um, you know, you can see those in, in customer use cases. So I think these are all things we're developing in house right now.

Speaker B: Well, let's talk about how AI is impacting your role specifically as CEO. What's changed and uh, what's in, like how do you even compare this to last year, let alone what's going to be in a couple of months?

Speaker A: Yeah, it's an interesting question. So what a year to be in operations, Right? Because AI is suddenly everywhere in the conversation. I, you know, the first six months of this job versus the second six months of this job are so fundamentally different. Um, but it also feels kind of like a golden age for operators, don't you think? Because now we suddenly have all these like magic AI wands and you can just, you know, create a custom GPT to, you know, help with strategic planning. That's something we're doing. You know, we talked about the agents, but there's just so many applications, um, for all these things that historically have been hard to build. So I think it's going to free up so much time and brain capacity to solve problems that we haven't even thought of. What do you think?

Speaker B: It's a great question. I mean certainly there's a lot that comes into giving power to people that haven't necessarily had the power to build and actually make their workflows more efficient. Right. Most of the time operators tend to not be technical and so they're reliant on engineers, uh, and data scientists. And we've kind of seen this adoption. I mean if we go back 2010, when you get tools like Optimizely and you get tools like Mixpanel, these are surfacing data and it's surfacing like abilities to do AB tests for instance, of which you would need to know JavaScript and PHP to be able to do. Right. And as these, as these softwares uh, mature more and more capabilities, uh, get put into the hands of the operators which just if you can have more self sufficiency, my goodness, it's going to open up the door for what you can do versus what you were doing that you no longer need to do. So I think it's really exciting in terms of just advancing the capabilities of everyone. In terms of self sufficiency.

Speaker A: Yeah, it's certainly a lot easier. You can, you can build these tools um, with fewer resources and you can often do it in yourself so they're more accessible. So I fully agree.

Speaker B: There's a question though, that is how much do we trust the AI tools to do a good job? That's accurate, that's, that's reasonable. That's not just making things up. It still, I think very much requires a hoot, a human in the loop versus just handing off complete automation. Like how, how do you think about sort of that quality control?

Speaker A: Almost 100%. I mean most of my team are engineers, really technical people that have spent, you know, years studying and understanding building equipment. And so I don't think AI is a replacement for that expertise, which is why that service layer around the customer is so important in terms of embedding them into their operational workflows and being able to service and support them. Um, and so that becomes increasingly important as well in your product design is that, that Expertise.

Speaker B: And as you think about the future of skills that you need to develop and maybe, uh, let's pull this back. What skills do you think future COOs are going to need in order to succeed in this AI driven operational environment?

Speaker A: I mean, it's interesting because I think we're all kind of learning at the same time, right? Because some of these things are relatively recent and that has been, uh, a great kind of normalizing function. But I think if you see the future as we've started to discuss as being able to bring tooling to, uh, kind of an underserved operations, uh, function, then increasingly you're going to need people who understand what those tools are, um, on the team and how to drive that rapid adoption. So I think that's an area certainly of growth for the coo.

Speaker B: So it's almost a growth mindset. You have to have the growth mindset. You have to have a willingness to go out and experiment. Uh, you also have to manage your time well. So you have the time to go out and experiment like that. Let's, let's look around the corner. You have a crystal ball, um, looking out five years. What excites you the most about the future of AI and autonomous building operations? And the reason I ask this, if I were to ask you purely like, hey, what do you think AI is going to be like? Software, software wise, five years from now, we'd be, I don't know, I don't know what it's going to be a week from now. But buildings and infrastructure, they certainly change much more slowly on a much longer time frame. Where do you think we are in five years?

Speaker A: Yeah, I think they're, you know, getting back to this point that buildings account for 40% of global greenhouse gas emissions. I think the next frontier for buildings are buildings that don't just look inward in terms of optimizing their own, uh, operations, but also outward and adjusting how they use their energy in sync with the grid so they can be not just part of the problem, but part of the solution. I think just touching on where are we going with the future of AI and autonomous operation? I think everybody is understandably very focused on the technology parts and I think you'll continue to see investment and excitement in that space. But I don't think the service side is going away and I think it becomes only increasingly important. So I think there will be some correction at some point. And, and companies that have gotten the tech part right, um, with a compelling service value proposition, um, to support customers through that journey are the ones that will kind of emerge as winners.

Speaker B: The inward versus the outward thinking is actually quite interesting. Even, ah, as you said that I'm thinking, well, even if they're thinking outwardly, it probably isn't for the greater good of the planet. It's probably more like, oh, let's optimize when we're turning lights on because you know, the grid is going to be more, you know, it comes through the bank account and the wallet.

Speaker A: Well, this is the beauty of it. And this is, I think you know why as an operator, this is such a fulfilling problem to work on because it ticks all our boxes, right? It's efficiency, it's cost savings, it's repeatable, you know, improving our processes. And it also is good for the planet.

Speaker B: Yeah, I love tech that actually touches physical infrastructure where it's not just ones and zeros, but it's ones and zeros affecting the actual world and how it's running. It's time for my favorite question. We've all had those moments where we've seen something just totally off the wall while we're in the COOC and we just think to ourselves, like, wow, I never thought I'd see that. Do you have one you can share with us?

Speaker A: Yeah, I think, you know, you know, when you start a new job, you, you know, you make the rounds, you turn over every stone, you're getting up to speed. I think when I took this job, I was surprised to learn that we had a distribution warehouse for sensors in the uk. It kind of always puzzled me how as a SAS company, like, why were we managing like a logistics supply chain? Right. And so it, it actually brought me back to my first day at Bain. So as a brand new consultant, you do this like, strategy workshop. And I went into that thinking, I remember, like, oh, there's going to be complicated frameworks and graphs. And it starts with a whole discussion around this really simple but powerful question around business definition, which is, what business are we in? And they, you know, they explain how that's, you know, where companies make, you know, hundreds of millions of dollars mistakes when they don't understand what business they're in. And so that kind of, it brought me back all the way to, all the way to the beginning. Um, because that's, we're not a logistics company, we're a SaaS. SaaS company.

Speaker B: Well, so what's the story there? Why did you have a warehouse full of sensors?

Speaker A: So, well, let's see. Um, so in the process of, you know, this job, we have really, um, focused our product portfolio. So we've talked a lot about energy and climate change. Um, we, we had a much broader product portfolio as well of other IoT sensors and things in, in the European markets which we've, we've exited.

Speaker B: So that's so interesting. So this is a sort uh, of a vestige of a past business strategy. So what, what'd you do with all of the sensors? What did you like, did you close the warehouse? Do you still have the sensors? What do you do with all that which I imagine cost millions of dollars?

Speaker A: Uh, we're in the process of finding a new home for them, so a happy home for them.

Speaker B: Got it. So if anyone has interest in, uh, interesting opportunities or ideas on how to use a warehouse full of sensors, what kind of sensors they are, well, that's a mystery. But, uh, I guess Noda is certainly open to ideas here. Listen, it's been so great, Thulema. Thanks for joining me. Where can people go to keep up with you and Noda?

Speaker A: You can find me on LinkedIn. Okay, thank you.

Speaker B: All right, we'll drop a link there. Well, and thank, uh, you to you all for listening to between two coos. I'm your host, Michael Koenig. And a very special thank you to Thulema Quintans for joining us. Tune in next time for our next COO chat and be sure to subscribe on Apple Podcast, Spotify, or wherever you listen to podcasts, so you never miss an episode. Just visit between2coos.com and if you get it a minute, leave us a review so others can get great advice from phenomenal CEOs. Thanks for listening. Tune in next time. And until then, so long.