Nils Schanz: No exactly, as you said, we will not share let’s say pricing details here, but basically we see a lot of demand from all our OEMs from all our customers supporting them with exactly the products I presented. So it’s really about not just plugging in ChatGPT. We clearly hear here from customers that this is not sufficient, instead they need our infrastructure and our layer in between to support them managing the cost, ensuring best performance, and really provide safe and reliable information which they provide to the end user.
Quinn Bolton: Got it. Thank you.
Operator: Thank you. Our next question comes from Mark Delaney with Goldman Sachs. Your line is open.
Mark Delaney: Yes. Good morning and thanks for taking my questions. First, I was hoping to better understand the dynamics of Toyota. Is Cerence still selling to Toyota just under a different program going forward, or will they not be a meaningful customer after fiscal 1Q with the acceleration of the deferred revenue?
Stefan Ortmanns: Yeah. So we have the preferred partner of Toyota, Toyota North America, Toyota Europe and Toyota Japan, the mother-co (ph). As a reminder the program what Tom referred to, goes back to 2011. I mean, compared now to the new technologies or applications in the car, it was clearly not competitive and there was also low traffic after almost 12, 13 years and they came to this decision, right? So but still we are working with Toyota on model year ‘24 and other opportunities.
Mark Delaney: Okay. And so the new multi-year outlook would assume perhaps maybe sales under these newer contract types and perhaps maybe even some higher, I mean, would you ship in more under the newer programs, if they’re shifting there from this?
Stefan Ortmanns: It’s always the newer programs, right, based on Cerence Assistant with all the latest and greatest technology.
Mark Delaney: Okay. My other question was just on the consumption of fixed contracts. Tom, you mentioned it’s slowing down and I think guidance for this coming fiscal year applies maybe $60 million of consumption. So it’s maybe it’s been understand that the reason that the consumption of the licenses is starting to decline? Thanks.
Tom Beaudoin: Yeah. Well it’s a combination of the decision, we made starting in Q4 of ’22, we didn’t do any fixed contracts and then this year we limited it to $40 million. And then on a go-forward basis starting this year to $20 million. So, we’re adding a lot less to be consumed in the future and then we’re burning off the higher levels back in, kind of ‘21 and ‘22 from both fixed contracts and the minimum commitments deals. And just to remind you, we’re not doing any further minimum commitment deals.
Mark Delaney: I understood. I’ll turn it over.
Operator: Thank you. Our next question comes from Jeff Van Rhee with Craig Hallam. Your line is open.
Jeff Van Rhee: Great. Thanks for taking the questions. Maybe a couple for Nils, if I could start with you. On the LLMs, it sounds like you’re obviously going with a hybrid architecture. You can expand a little bit further on what are the some examples of cost-effective versus non-cost effective, where you might use external versus not?
Nils Schanz: Yeah. Sure. So basically, what we are trying to do is really keeping the model parameters optimal. So we really fine-tune the applications to the needs of the OEMs. And then you should know that, let’s say, the context window in automotive is a bit much smaller which allows us to really manage the cost and have only a viable cost structure. On your point of cloud and in-car systems embedded, so we clearly see that still the solutions the OEM ask us for should also cover embedded so that’s clearly the need and there we can we can support with our new solution where we fine-tune based on our automotive specific data and customize it really to the auto use cases for the OEM. So basically what it means is that we can decide together with the OEM which queries are forwarded to a third-party LLM and which can basically be covered by our conventional AI stack or our proprietary LLM we have built.
Jeff Van Rhee: Okay. And then Tom on the Toyota adjustments the, forgive me if I missed it, Q1, did you break out the impact — EBITDA impact of Toyota?
Tom Beaudoin: No, but it’s a very small deferred revenue component to it. So most of that whole amount of revenue is profit.
Jeff Van Rhee: Yeah. Got it. Fair enough. And then maybe lastly on connected, just curious what process improvements you’ve made with respect to gathering, analyzing, incorporating your learnings from usage on connected. Obviously, you get some very valuable data. I think you had some efforts to try to improve the ability to capture that and gain insights. Just what might have changed there?
Stefan Ortmanns: May I just for clarity, Jeff, on the technology side?
Jeff Van Rhee: No, from process-wise. So you get the insights on usage. In the past, I’ve asked a lot of questions about what features, functions, capabilities are really getting consumed. It sounds like there were a lot of steps and initiatives internally to get more precise and understand exactly what people want and were using. Curious, if those initiatives have been implemented and what kind of insights you have on connected usage.
Stefan Ortmanns: So what we see here, Jeff, is the following. So when comparing year-over-year, we see a year-over-year growth of about 16%. When looking at monthly active user, it’s up by 30% or even more year-over-year, right? This shows actually that our solution is actually adopted by OEMs customers and finally also our customers, right? Nils and team, we are looking together with the OEMs how to improve further adoption here, that’s key to success. We have also this as platform where we can also adapt and adjust the specifics and also creating more personalized information for the drivers and passengers. So overall we are making great progress and we are also related as we are the innovation partner of most of the OEMs, right in close collaboration with the OEMs.
