4.5.23-AI-Leela AI

CYRUS SHAOUL: Hello, everybody. My name is Cyrus and I'm an MIT alum. My co-founders are also all MIT alums. So we have a very deep field of MIT people at Leela.ai. And very excited to talk to you today about what we're doing.

We're building AI assistants for industrial engineers. And it goes beyond that, as I'll explain. But I think that sort of hopefully will grab your attention. I do want to say off the bat that we do not have any large language models yet.

But not everything has to start with large language models. I think there's a lot of value to be gained from using other kinds of AI. And who knows, maybe we'll work with them later. We're open to that.

But let's take a look at the visual side today and see what's coming out of this world for manufacturing and also for supply chain uses. One thing that's really important to understand is that there's a lot of waste in many processes. I think we all have seen it. We've all been frustrated by it.

And it's easy to say we can get rid of waste. But before you get rid of it, you have to identify it. And identifying waste is not easy. Once you've identified it, it can be removed. And who are the people who are best at this? These are the industrial engineers.

Industrial engineers don't just work in factories. They work in lots of service operation areas. These are the people who can understand processes and help optimize them. And they need help. And they also need-- we don't have enough industrial engineers. There's a giant shortage of them right now.

I don't have to, I think, emphasize enough these numbers. If you've seen these numbers, they're very scary. This whole trend is reshoring. How do we reshore? We need to bring manufacturing and other things back to the US. But if we don't have the talent and the skills to do that, we won't be able to finish it.

So we need other sources of smarts to really bring manufacturing and other types of activity back onshore. And so what we've built is an AI assistant for industrial engineers. And here's some video that sort of gives you an idea of what we're building.

It can see what's happening, see the relationships between operators and their tools, between the environment and people, and measure lots of things that would take many, many, many people standing around with clipboards and stopwatches hundreds of hours a day to really measure. But with that rich data, we can bring the level of understanding of manual work and what people do closer to the data richness of what machines do.

You can monitor machines easily. They spit out numbers all the time. But people also need to be in that mix. And so here's some examples of a lab where we've just been experimenting.

And you can see in this lab scene our system is tracking everything that the person is doing. This is Meg. She's also using a tablet to look at her own data here, because our system is pumping her own data back to her, so she can figure out stuff. She becomes her own industrial engineer, really, looking at her own productivity and her own quality of her work, and can help her do better.

And industrial engineers in the back office can use this, too. And you can see, you could have somebody realize that they can produce way more per day just by looking at their data. So that's the product. And what happens then is that AI's been used for a long time in manufacturing to inspect those products.

But our solution helps them inspect the process in the same way, with high-powered visual AI systems. And this gives industrial engineers and operators superpowers that they never had before, which is what we need right now. It's not-- oh, yeah, there we go.

So just to reiterate, we can help industrial engineers become twice as productive, because they get data that they never had and they can collect more data than they've ever been able to collect. And then what that leads to, as a follow-on, is that the whole site that they're working at becomes more productive, because if industrial engineers and operators who are also using our tools can really find waste, it really boosts productivity.

Now how does this all work? It's really simple. Cameras are very easy to set up now. The cameras send the video to the cloud. Our platform analyzes it in the cloud and generates a dashboard. And that dashboard will include suggestions, ways that our AI assistant is suggesting could improve productivity.

And that means that they can increase capacity. They can bring new products to market faster, because they can do all sorts of experiments with process and find out how to fine tune it. They can reduce waste, and they can improve safety and ergonomics.

So that is a very, very powerful suite of activities. Our customers, and I should say in the room, there are some of our customers we've been very successful with ILP members. We've worked with many. We have five ongoing right now and some have subscribed. So we really love working with the ILP community.

Giving early access to this powerful tool has been great. And they've seen 10% capacity increases, and 10 times what they put in to us getting back out in terms of benefit to their companies. So final slide here, love to work with more ILP members. If you are a digital-first organization, you have visionary team members, yourself are visionaries because you're here today, you want to try new things and give your industrial engineers and your operators superpowers, come talk to us.

If you already have IoT solutions, but you want to boost them with the visual side, you know, it gives a boost to any other kind of data input that's coming from your operational situation. Love to talk to you. Anything to do with machine operations, handoff between automation, handoff between robots and people, those are the areas which are very valuable, give that sort of autonomy and human connection point that's really valuable, and also logistics as well.

My email is there and I'd love to talk to all of you at our booth. So come on over. We're in the corner and I can give you more demos. Thank you very much for your time today.