5.5.22-Efficient-AI-Arundo

MARK TIBBETTS: Hi. There I'm Mark Tibbetts and I am product and data science lead at Arundo. And I'm here today on-- get this thing to load. There we go. On behalf of our founder, Tor Jakob. And we founded Arundo to really bring digital technologies to heavy asset industries. Now, we're doing that through providing enterprise software. And that enterprise software is primarily targeted at the maintenance and operations teams of these companies.

But before I get into our technologies, I just want to take a little overview of what we see in the market today. So these companies, they do see the importance of these digital technologies. But we are seeing some challenges. There are many pilots of AI initiatives, but they're not having the success that we would like to see. In fact, a lot of these initiatives are not moving beyond the proof of concept phase.

And interestingly, a lot of the effort is being focused on digital infrastructure but not on the digital literacy of the organization. So at Arundo, we help our customers overcome these issues by developing software tools which are focused at the end users, the actual operators and maintenance, the decision makers. We're giving them the workflows they need to make sure that they can take the right decisions at the right time in the right place.

Now, the products that we use to do this. We have several core products. I'm going to introduce those to you. So first is Dataseer. This is our tool for allowing our customers to build a digital representation of their physical assets. And typically that information is contained in engineering diagrams, such as process and instrumentation drawings. And those are PDFs or typically offline files. So what Dataseer allows you to do is upload those files. It uses computer vision technology to extract the symbols, the text, and the tabular information, sorts and stores that in a way that it can then be used downstream in digital projects.

Our next product is Marathon. Marathon is what we're using to connect to the existing physical systems. We have an edge agent, which is extracting the telemetry data from those systems, sending it to cloud, and then we're running data analytics pipelines, including machine learning, on top of those data as they come in. In particular, we're focused on finding the data signatures which correlate to clear action needing to be taken from operators not running equipment efficiently or just the equipment not being an efficient operation state itself. And we then provide a list of prescriptive actions which allows the operator to systematically move that equipment back to its desired operational state, thereby preventing potential failures.

But we don't just work in our own products. We also partner with experts in the industry in both gray label and white label digital products. And for example, we're building the next generation of process optimization for ethylene production. And then as we have the view on the assets, as we have the view on the process and as we've got this granular bottom up view, we're now in a position to look at sustainability and energy efficiency, which is increasingly important in today's world of energy security and the climate challenge. And so the next product we're bringing to market is exactly that.

So I want to briefly walk you through a customer journey. And this is Evolution Well Services. And to introduce you to Evolution, they're a pioneer in the oil gas industry. They provide pressure pumping services to oil and gas providers. And they've revolutionized this industry by turning diesel fleets electric. But they also realized they needed to revolutionize their digital strategy, because they were only using data to operate, not to improve efficiency.

By partnering with Arundo, they've adopted our Marathon software. They now have that deployed across all of their operational fleets. That's allowing them to save money in repair capital. But also because they have this deployed across all fleets, we're now in a position that if we learn something from one fleet, we can apply those learnings to all the fleets right away. And so that's really the advantage of this scalable approach to constructing these data pipelines.

In terms of partnership asks, well, we've had success with our first customers and now we're looking to scale up the adoption. But we're also interested in development partnerships and allowing our partners to service their customers with technologies that we build together. And both myself and Joanne, our executive lead for Dataseer, will be at the lunch booth. So please do come and talk to us. Thank you.