 Thank you for having me. We're in balance, and we're the software trying to drive the energy transition by doing responsive asset management. So we do price forecasting for utilities, like now. And currently, our customers are using our software to improve their revenue by up to 25% per year. So we all know that the grid's facing a lot of issues at the moment. First and foremost, there's extreme weather. So in Texas, we've seen that oftentimes your natural gas pipelines aren't reliable when you need it the most, and it's created massive grid outages as a result of the fact that the traditional fuel sources can't always be trusted. We've seen in Europe so far this year that natural gas is at an all-time shortage point and the grid is less reliable than we've previously experienced. The new energy sources, wind energy, solar energy, we can't control whether or not they're producing at any given point in time. So you need storage to effectively make up for the shortfalls in the new energy store, the new energy in order to be able to effectively integrate it into the grid. And finally, the increasing complexity and diversity of assets means that you can now consider fleets of electric vehicles as a power plant in a certain sense, right? And each of these new types of power plants and these new types of energy assets that we're bringing online has its own constraints with respect to how it can operate. And you need an effective, intelligent engine behind these power plants in order to be able to make sure that for each of these new power plants, they're being leveraged to support the grid as effectively as possible. So what do we do? If you think about wind, it's a price taker. You can't control whether or not the wind is blowing. But on the other side, you can think of water behind a dam as a market maker. So you only have so much water behind the dam for day and you need to make a decision as to the hours in which you've released that water from behind the dam. So we do electricity price forecasting in order to make sure that if the electricity price is higher at 6 p.m. than 7 p.m., you've saved the water behind the dam so that you can release the water from the dam at the point at which the grid needs the power the most. We work at a number of different levels with the grid. We work with the actual trading desks that are trying to make these complex decisions with respect to how to manage fleets of assets. So a company might have hydropower. They might have solar farms. They might have wind farms. They might have grid-scale storage. And they want to manage those assets as a holistic portfolio. We can deliver our price forecasts to them in order to make sure that they have a picture as to what's happening over the course of the next 72 hours in the grid where they operate. We can also work directly with individual grid-scale storage assets so that as that one battery is making a decision as to when to charge from the grid, when to discharge back into the grid. They have the pricing signal information that they need in order to make the right decision. And then as I mentioned, we're not doing this yet, but we really want to start working with the next generation of assets, like large electricity or large fleets of electric vehicles, to make sure that they can be effectively used as a support mechanism for the grid. How do we do it? We're machine learning driven. We've gone out to make sure that we have the right signals to understand how to price the grid based on my background and wind generation forecasting, as well as our team's background in quantitative forecasting for other markets around the globe. A lot of times, these markets are underfunded and underdeveloped. So for instance, in New England, we've had examples of the price file for the electricity price for the past 12 hours doesn't show up or it's broken or it's not properly formatted. And you still need to make operational decisions for these assets, even if the data is a complete mess. Then on top of that, we build the electricity price forecasting system using machine learning. And then finally, we have an optimization engine that ingests those forecasts, looks at the ways in which the asset is able to operate and then makes operational decisions for how the battery or the hydro plant or the electric vehicle fleet is going to interact with the grid based on the price signals. This is definitely a historic opportunity over the course of the next seven plus years. The amount of storage installed just in Europe is about to increase by 20 fold. So these battery assets are more responsive than any assets we've ever seen before. They're lithium ion batteries. They can go from full charge to full discharge over the course of approximately 30 seconds whereas even a hydroelectric plant takes on the order of 30 minutes from going from no powers being released to 100% of your powers being released. So the decisions that you can make are more complex than anything we've ever faced in the grid and the rate at which they're being installed is growing rapidly. Asset management itself when it comes to dealing with responsive assets attached to deregulated electricity grids is massive. So already in Europe, assuming that we could expose ourselves to approximately 10% of the revenue coming from these facilities, it's an $8 billion market in North America. It's already a $5.5 billion market and as I mentioned, it's growing rapidly. Currently, we're interacting with the grid in two ways right now in North America but hoping to expand globally. First of all, we're working with Inel and we're delivering the price forecast to Inel for them to be able to make daily decisions on how to manage their responsive assets in Texas. Second of all, we also have market registrations multiple markets and we're building out the software interface necessary for us to directly make purchase decisions and sell decisions of electricity into markets in North America and hopefully soon in Europe as well. So why does our team care? We care for three main reasons. First of all, when you're able to effectively transport clean energy from the points in time when it's produced to the points in time when it's needed the most, you make sure that those peak hours in the day between six and eight p.m. when everyone's plugging in their electric vehicle, the cost of that electricity is as low as possible long-term and that the grid is adequately planned for that charging decision while also making sure that the renewable energy itself coming from the wind farm sees more value. Second of all, this is an essential part of decarbonizing the grid over the course of the next seven plus years. Storage is an important part of the solution in order for us to meet our targets, but you need the right brains behind that storage in order for you to know how to operate it and which hours that storage can be best utilized in. And finally, when the grid's unreliable, there are safety issues involved. So with imbalance, we make sure that when the worst happens, when you have electricity shortages, when you can't even trust gas pipelines to deliver the natural gas to the power plants that you generally rely on in order to support the grid, you have your battery reserves ready to operate to make sure you can get as much power to the homes that need it. We're in balance. Thank you very much.