Tianyi Jiang: Yes, that’s a great question. As we stated in the prepared remarks, the foundations to effective AI deployment it’s actually based on the premise that you have really good information management strategy. That means making sure that you have data collated from all your various sources across your enterprise. And also making sure that the data are actually properly classified, intact and have the right life cycle. So you don’t get in to trash in, trash out issues when you build that proprietary layer of modeling on top of generative foundation models. So every enterprise is doing this today to ensure that they can leverage their own decades worth of domain data, proprietary enterprise-owned data to actually form better capabilities and innovation on top of AI to then drive purposeful outcome.
So that’s a space we actually play very well in, and we’ve been doing this for enterprises for the past decades. In fact, we’re not a stranger to machine learning, AI. As many of you know, my background is machine learning, AI. We have been a long-time consumer of Azure Cognitive Services, and other open source AI capabilities for — especially for governments, on-prem and hybrid deployment scenarios. So all these things actually points back to the importance of information management capabilities. So it’s not a separate use case from AI. It’s really very much of the same motion to make AI effective and workable for enterprise.
Chirag Ved: And then maybe one for Jim. It looks like net retention rate increased sequentially, excluding FX to 107%. Can you speak to what contributed to the step function increase? And what needs to happen from your perspective to hit the net retention rate targets that you outlined?
James Caci: Yes, great question. So this quarter was a strong quarter for us for existing customer growth in terms of additional ARR. For the first time, we’re over our incremental ARR for the quarter, over 50% of that came from our existing customer base. So we had a strong quarter as it related to that existing customer base. And even more so, so that definitely contributed to the NRR growth, and then in addition to that, we saw a really strong quarter for existing customers who were with us less than a year, kind of adding to their original purchase, which is a strong indicator for us. Number one, that our customers are enjoying with the services we’re providing, the software we’re providing, but also consuming more. And it’s interesting that when the uptick in the first year, it’s almost a leading indicator because that sale does not impact our NRR.
We won’t see that impact for a full year. So when we think of NRR, it’s really a trailing 12-month or a lagging indicator. And when we sell to a customer within that first year, it’s really a leading indicator because it’s going to bode well for future NRR. So we feel good about that. I do think we still have a lot of work to do to get to those medium-term targets that we put out a couple of months back in March to get to that 110% to 115%. So we definitely want to continue to see continued execution on what we’ve been doing so far.
Operator: And the next question comes from Nehal Chokshi with Maxim Group.
Nehal Chokshi: That was Northland Capital Markets. We had a really strong incremental ARR. Jim you already sort of alluded to one of the drivers of that was NRR being signature positive. But just to be clear, is that NRR moving up a result of increasing the fee for existing customers or increasing functionality?
