Maggie Nolan : Thank you, guys.
Bin Chiang: Thanks, Maggie. Next question comes from Bryan Bergin from TD Cowen. Please go ahead.
Bryan Bergin : Hey, guys. Good afternoon. Good to see you. Wanted to start on kind of existing base and industry expectations as you go into the next quarter. So, can you talk about what you’re seeing in the existing client base, whether you are seeing signs of stabilization that are becoming a little bit more broad-based? And specifically, if you can kind of key in on TMT and CPG as you plan for the third quarter?
Leonard Livschitz: So, Bryan, I would not select any specific vertical. I think it’s pretty much goes across all of them at this point. We do see stabilization. On the technology space specifically, it’s a little bit more client to client, very, but mostly because they’re giants, right? So one department goes a little bit more active in spending, the other one takes a little bit backseat. But from the CPG, it’s no different than manufacturing or life science. We see that dynamics of conversations start becoming more deterministic by dollars for investment. And recently, we won a couple of very notable RFPs, which is kind of great for us anyways, because that’s something we are doing a little bit more aggressive. And we see that the companies start preparing, there’s anticipation, then there will be an inflection point in Q4, and maybe even late Q3.
But those are a little bit more speculative. We reflect our guidance based on the facts. But I would say that, from the dynamics of the engagement with existing customers, because new customers you already know we’re doing fine, but it doesn’t create that, inflection from the short-term revenue perspective. So, we do believe there is going to be some positive momentum coming in.
Bryan Bergin : Okay. That’s good to hear. And then a generative AI question for you, but more so internally. So, can you just talk a bit more about how you’re applying generative AI, obviously early proof of concepts internally, any early measures of success you can share around developer productivity? And I also wanted your viewpoint on really a high-level question, whether you think that this technology can potentially reduce the competitive benefits of scale? Meaning, do you see this as an opportunity for some of the smaller, more specialized vendors to have a leg up in competitive positioning versus some of the large-scale global players?
Leonard Livschitz: Well, let me start with the last one. Of course, I would love to tell you there is nobody in the world better than us, right? That saying, the big guys can invest big dollars. Big dollars can lead to big failures, because this is not the time to compete on the size of investment per se. It’s how smart you invest. The models must be proven. You can’t prove the models till there is a, sizable result in the industry, and you need to remove the bias, the noise. I hope anybody who’s done their modeling, they realize that how sensitive the environment of the forecasting to the boundary condition, to the variance, and all this stuff. And you need to really look at the consistent correlation. So that’s on a more technical side.
See, I do believe on a laser-focused engagement rather than a broad-based announcement that we’re going to put X billion of dollars. Now, seeing what we do internally, there are a lot of things happening. So first of all, on the code itself, it’s not a secret. People say, you know, the code can be developed — with natural commands, with, augmentation of the code, the quality controls, the automation to the next level where there’s, I would say, artificially driven factors. Again, internally, it always works great because, you know, we’re paying for ourselves. But the importance to test those samples of the codes with the clients what did say, and I mentioned just before you asked this question, in terms of the productivity, in terms of selection of skill, I mean, the skill set map, which we’ve been using for a long time, there’s a lot of guesswork there.
