Githesh Ramamurthy: Well, again, our focus is not to increase the total value of the order. Our focus is to get it as accurate as possible. So, to that degree, what we do is we take pictures of the vehicle. And because we have millions and — literally hundreds and hundreds of millions of photos, prior accidents and estimates and the like, we can actually look at this front right hit of this particular vehicle and say, with this level of depth of damage, we also have 3D mapping and the like, so we can tell how deep the damage is and actually predict a lot of the parts pretty accurately. And oftentimes, there are minor parts, you might be missing a $0.50 clip. You might be missing six clips, which might be a total of $3 out of a $5,000 repair, and that could delay the repair by days.
So, getting some of — more of that completeness can actually have a big difference. So, we are able to do that. But right now, that capability is not deployed. That is what I talked about, Estimate-STP being deployed down the road or that’s what we’re testing to deploy with our repair facility customers.
Gary Prestopino: Okay. Thank you.
Githesh Ramamurthy: Thanks Gary.
Operator: Thank you. Our next question comes from Arvind Ramnani of Piper Sandler. Your line is now open.
Arvind Ramnani: Hey thanks. Most of my questions have been asked, but I want to follow-up on some of the color you gave on AI and some of the kind of areas you’re using it. From a financial model perspective, where are you starting to see some of those benefits? Is it like already kind of improving your revenue growth or your margins? Or is that still something that’s probably like a year or two out before we start to see material improvement?
Githesh Ramamurthy: Brian, do you want to take that one?
Brian Herb: Yes, absolutely. Hi Arvind. Where we’re seeing it, Estimate-STP is an example where AI is in production being used in generating revenue today, so that’s an example of AI being used and getting rolled out. And we’ve talked about Estimate-STP and how it’s contributing to growth. It’s one of our emerging solutions. We highlighted emerging solutions contributed one point of growth in the quarter. So, that’s one example where we’re driving — that is being rolled out, used by clients in generating revenue. When we think about the other AI examples, Githesh referenced the casualty example in his prepared remarks. Again, that will be a revenue-generating solution and other products like subrogation will be using AI or have AI embedded as well in another area of revenue generation. So, revenue will be the solution rollout and revenue generation will really be the driver for AI going forward.
Arvind Ramnani: Great. And just if I can follow up on that. These are sort of direct kind of contributors. Is there sort of an indirect contributor that sort of informs your win rates or conversion rates or ability to kind of keep your rates — your bill rates at a particular level? I mean I know that may be hard to sort of fully quantify, but are you starting to see some of those benefits even qualitatively that these kind of innovative solutions are driving conversions?
Githesh Ramamurthy: Yes, I’ll just make one macro point, which is we work with our customers for a very long period of time, right? If I look back at just the 10 quarters that we’ve been public, 10 quarters, we’ve added solutions to our existing customer base on a pretty wide variety of fronts. Revenue in the last 10 quarters has gone from a run rate of about $632 million to a run rate of about $848 million, which is an increase of about $200-plus million. EBITDA has increased by — from 220 run rate to about 320 plus in run rate. And this has come about not from any one particular solution or any particular modeling, but the fact that we’ve delivered new products, solutions, innovations with very specific ROI to different components of the process for insurance and for different components of the process for repair facility, different components of the process for parts providers.
