Everyone, good morning. My name is Tejas Savant. I know a lot of you in the room. I cover the life science tools and diagnostics sector here at Morgan Stanley. Before we get started, just want to read the disclosure statement. Please see the Morgan Stanley Research Disclosure website at morganstanley.com/researchdisclosures. If you have any questions, do reach out to your sales rep. It's my pleasure this morning to host Nautilus, and speaking on behalf of the company, we have Sujal Patel, CEO. Thanks, Sujal, for joining us today. Maybe just to set the stage, you know, for folks here and on the webcast not as familiar with Nautilus, could you just give a brief history of, you know, how the company came into being?
Where do you see Nautilus fitting in the evolving proteomics landscape?
Great. Well, Tejas, let me start by thanking you and Morgan Stanley for the invite to the conference. Happy to be here again. Let's maybe start that question just, you know, just at the very beginning of what.
Proteomics is study of proteins. Proteins make up all of the functional pieces of your cell. They do all the work, and unlike the genome, your DNA doesn't really change from the day you're born to the day you die. Proteins are dynamic. They change every day in response to disease, in response to what your body needs. And so measuring proteins is really critical to understanding what's going on in biology. Most of our FDA-approved drugs target proteins, most molecular diagnostics target proteins.
Mm-hmm.
And the problem that we have in the world today is that the technologies to measure proteins in sample are incomplete, they're hard to use, and they really don't give you a precise signal. And that's in stark contrast to genomics, right? Over the last couple of decades, we, as a scientific community, have conquered genomics. Genomics, it can take a sample from any organism, and you drop it on a genomics sequencer.
Mm-hmm.
Every time it works, in a day, I've got the full genome, and that's just far from what we have in the proteomics world. So many different companies have tried to attack this problem in different ways, trying to make incremental improvements. Nautilus, which is, founded in 2016, really is the only company that's taking a completely new approach to trying to measure proteins and sample, with the goal of building an instrument that's able to measure 95% of the proteins in a sample from any organism, and do it in a push-button, simple, easy method from sample to answer, to bring proteomics in parity to where genomics is.
Mm-hmm. Got it. And, and, you know, talk to us about, you know, how it's differentiated from some of the, the peptide sequencing efforts out there, you know, Quantum-Si and Encodia, et cetera. And, and then you've got, you know, the targeted platforms like Olink and SomaLogic as well. And, and, and of course, there's, there's Seer as well, which is sort of, you know, positioning themselves as complementary to mass spec. So in, in that ecosystem, where does your approach kind of fall?
Yeah. Well, so, you kind of have outlined three different camps of approaches-
Mm-hmm.
to tackle the proteome, with us being the unique one, peptide sequencing being a new one-
Mm-hmm.
traditional assays being the standard way.
Mm-hmm.
Right? And each of them has different pros and cons, right? Your traditional assay, Olink, SomaLogic, as well as a number of, products that are inside of larger-
Mm-hmm.
Organizations, are really focused on building antibodies or affinity reagents that are specific to different types of proteins, and then putting them in an assay so you can measure those proteins, for example. Those approaches are good, but they're not super sensitive, and you have to build an antibody for every single different target.
Mm-hmm.
It's really infeasible to go and build one for all of the 20,000 gene-encoded proteins
Right
... and do that in an effective way. Even if you could do that, you would then not be able to see all of the different forms of these proteins, the different modifications. Those modifications and isoforms have a profound impact on the distribution of these molecules in a cell, their function, their degradation. So if you want to understand biology, you have to understand-
Mm-hmm.
You have to understand that complete picture. So that's one camp. Another camp you mentioned were these companies that are focused on peptide sequencing. There are a number of new initiatives out in the world that are trying to... like we sequence DNA, trying to sequence protein. And for sequencing protein, it's impossible, so what they do is they break these proteins into little pieces and try to sequence very short fragments. And it is a very complex, hard thing to do, and it's a hard thing to do with scale. And scale matters in proteins because, you know, just in a single cell, there's 1,000,000 protein molecules on average.
Mm-hmm.
If you want to understand what's going on in a traditional pharma sample, for example, you would need to see billions of molecules and analyze them in a precise way. And so those types of approaches are good for answering, niche types of questions
Mm-hmm
... but really don't scale to being able to look at a whole proteome in a sample.
Right.
And so the unique thing with our approach is that we're really focused on bringing the scale and the sensitivity to the problem that matches the customer samples and the needs inside of biopharma, right? The goal for us is to be able to analyze sensitively any of these samples that have 100-1,000 cells or blood serum, so that we can help our customers improve the efficacy and speed of drug development.
Mm-hmm.
We can help them build more precise diagnostics. We can help them with basic science research and so forth.
Got it. So on the last earnings call, Sujal, you talked about, you know, balancing time to market versus, you know, minimum product specs. What specs do you see potentially sort of ceding some ground on, on version one of the product? And you talked about even on those sort of winnowed down specs, it would still be, you know, a game-changing sort of introduction to the field. So talk a little bit about, you know, what are those game-changing features of the platform that you think cannot be met with other sort of efforts out there?
Yeah, maybe it's a great question. Let me back up just for context, and then we'll
Mm-hmm.
kind of get to that, right? So the product that we're building, much like the first time this next generation genomic sequencer was built, it is a hard, complex task. It involves lots of components. It involves different pieces of science that have evolved over the course of the last six, seven years-
Mm-hmm.
that we've been building these. And there's a, there's a huge amount of effort bringing those things together. Just like Illumina did, or Solexa did, which was the company Illumina
Mm-hmm
bought to get into NGS, there are some compromises that you would make at V1 to go and get to market sooner.
Mm-hmm.
From our standpoint, we were gonna make some of those same sort of very pragmatic compromises.
Mm-hmm.
For me, the important thing is to be able to talk to a customer about the fact that you can buy the instrument today, and just consumable updates are going to get you to the full specifications that we had originally-
Mm
... disclosed. Because that means the investment they make in that instrument, that roughly $1 million instrument, that instrument isn't something you have to rebuy afterwards. And so you asked, like, what are the sort of compromises that you would make? I think that the first thing I'd like to just kind of hit on is, the compromises that we would make are really informed by conversations with customers-
Mm.
and looking at the competitive landscape. So to give you an example, we need roughly 300 different affinity reagents in our system to be able to achieve 95% of coverage of the proteome. If the coverage was 50%-60%, that's still well beyond any other method that exists.
Right.
Of course, we would still launch. Dynamic range is another, right? We have a chip in our system that has 10 billion spots for proteins.
Mm-hmm.
That's five orders of magnitude in excess of where the competition is. If we need to make a compromise there, a half an order of magnitude or even an order of magnitude
Mm-hmm.
That's not a problem. We're already so far above where the competition is.
Mm.
We can resolve those types of things with consumable updates in the future. So those are the types of compromises that we'll make initially, and we've pressure tested that with a lot of our early customers. You know, I think that pragmatic is really how I would describe those.
Got it. On the point on the multi-affinity binder, Sujal, how confident are you that you can get to 50% or 60% by the time of the V1 launch?
So, I think that, you know, I will, I will hedge my answer in that regard. Look, we, we said that, on the last earnings call, we, we said we continue to target roughly the middle of next year
Mm-hmm
... to have our commercial launch for the platform.
Mm-hmm.
A big piece of that depends on us having enough multi-affinity probes that operate within specification on our platform to be able to get to the 50%-60%.
Mm-hmm.
I think that we are extremely confident in the development path.
Mm-hmm.
We're confident in that rough timeline, but there's still a lot of work to be done.
Got it. Got it. And then in terms of, you know, the sensitivity and ease of use, I think you'd call them sort of the non-negotiables on even the V1. What is the minimum level of sensitivity you think will be required to drive meaningful adoption?
Yeah, so that is a simple one. We are a single molecule counter. That is... There is nothing else our play. We can't have any sensitivity other than single molecule. And in terms of single protein molecules, we are the only platform that is-
Mm
- even conceived of that can analyze a single protein molecule.
Mm-hmm.
Everyone else who uses those words breaks their thing into breaks their proteins into peptides and then does an analysis.
Right.
For discovery platform, single-molecule sensitivity is what we do. That is the only mode that we operate in.
Mm.
It is one of the key things that customers look at for our platform. You know, to give you an example of what's the practical use of that, right? If you are developing a drug for, for a particular disease, the first thing you would do is look at healthy cells, look at cells that are affected by disease, look at the cell surface to look for differences. The differences between healthy and sick cells are very, very minute, sometimes even single molecules.
Mm
that are changed. And so for a pharma company, being able to push that threshold down and see things that are more and more rare, those could be the biomarkers that are the best potential drug targets for the next generation of compounds that they're gonna go and put through the FDA and get out into, into the clinic. That is, that is an incredible-
Mm
Value proposition for customers, so that absolutely is non-negotiable in our platform.
Got it.
Ease of use is the other. We set out, when we founded this company in 2016, we set out to go and build a platform that could be used by any biologist. Any biologist who wants a proteome can get a proteome from any sample, from any organism, and that is, that is a non-negotiable goal for us. You know, mass spectrometry-based proteomics workflows are extremely complicated.
Mm.
You know, even companies like Seer, which you mentioned earlier, which make it incrementally easier to prepare samples for mass spec, they still don't fundamentally change that. Mass spec-based proteomics is extremely complicated. We are bringing an easy-to-use platform to market so that any biologist can get the types of answers that are gonna translate into massive discoveries.
Got it. You talked about, you know, not requiring an instrument upgrade as you go from V1- V2. Sounds like you're further along in terms of instrument development at the moment. Could you just give us a sense of what remains to be done at this point, versus, you know, middle of next year when you launch?
Yeah. I think that when you kind of look at all the platform pieces, right? We have to have an instrument.
Mm-hmm.
Yes, the instrument is, you know, it's on a timeline that will certainly
Mm-hmm
meet that middle of next year.
Mm-hmm.
Because it is just engineering.
Mm-hmm.
And we are operating on the final generation of instrument now
Mm-hmm
in our facilities. The other thing, the next thing that you have to have is, you have to have the single molecule array, right?
Right.
Something you and I have talked about quite a bit. Our platform depends on taking molecules from a sample and spatially separating them on a chip, and that chip is a combination of a chip with built with semiconductor fabrication processes, chemical function, functionalization, and then an assembly that's built into a flow cell. That system and our single molecule deposition are also in good shape, good enough along that we're willing to put the pencil down
Mm.
-and say: Yes, that's good enough.
Mm.
Getting close to good enough for launch. Let's evolve those processes for scale. Let's, let's get the production, manufacturing scale.
Mm-hmm.
Let's move that on, right? The next two sections are the actual multi-affinity probes, cycling them through our sample, one after the other, and being able to detect-
Got it
-those binding events. And that's the area where we continue to spend a lot of our time and energy in building those multi-affinity probes and figuring out what the right mix of them will be. And then the last piece, which is also kind of tied into that, is really the bioinformatics and the data science associated with turning that data into identification subroutines.
Mm.
And so that's where the predominant effort is today, is on those pieces as we go and try to get to that set of initial multi-affinity probe-
Right
reagents that we'll use to launch the platform.
Got it. And, you know, on that point, Sujal, the repeat sort of, you know, bind, wash, bind, wash cycles that you need to do.
Yeah.
First of all, how many of those cycles do you need to do? And then, is there any concern that that might sort of, you know, change the signal or, or sort of, you know, add noise?
Yeah. So, you know, the question you bring up is a question that, many life sciences instruments have to deal with, right?
Mm-hmm.
Particularly in NGS, you have to deal with this. So in our assay, we flow one affinity reagent in, we go and we do our analysis, we wipe that plate clean
Mm-hmm
... we send the next one in over and over again. We need 300 affinity reagents-
Mm-hmm
to get to about
Mm-hmm
95% coverage of the protein, which is our target. We do two at once-
Mm-hmm
-two colors, so that's 150 cycles.
Mm-hmm.
Across any number of cycles, there's always a degradation in signal.
Right.
So you start a little bit high, and you have a little bit of degradation. But we've done enough experimentation through. We've talked about in our earnings calls that every quarter, we're pushing that higher and higher. We're up at the 100 mark now and are comfortable with the degradation of signal and where we're at. So I think that-
I see.
-that's not gonna be a concern that's gonna stop us.
I see. Got it. Makes sense. And then one of the features of the platform you've highlighted in the past is just the ability to characterize, you know, proteoforms and PTMs. Is this important in your mind to have on V1?
So, I think that you should think about proteoforms and different isoforms as kind of a short to medium term type of focus for us, right? I think that at V1, we may have very simple proteoform detection.
Mm-hmm.
For customers that want to do more, there will be improvements and consumable kits in the future. But as well, we have a number of customers who have very specific proteoform questions, who come to us and say, "Hey, help us to go do that.
Right.
Genentech did that, for example, around the different forms of the phosphorylation patterns and the Tau protein. We're happy to work with customers, in custom engagements on those types of things.
Mm-hmm.
This question of proteoforms and isoforms is really a question of different biological insight, right?
Right.
We think that, you know, over the course of the, you know, five-10 years after we launch this platform, we will move down a continuum of enhancements that will give us more biological insight. First, understanding in deeper depth what the protein modifications and splice forms are. The next thing after that is understanding what cell did that-
Sure
-molecule come from. After that, spatial is definitely something our customers are interested in. And so for me, I think that there's a roadmap for us that gives us the ability to make enhancements over time, that give more and more biological information to the customer.
Got it. Do you think it's fair to expect some sense of lockdown specs and form launch timelines by year-end?
What I will tell you is I'm not gonna commit to a timeline on it, but I think that if you back up from launch-
Mm-hmm
... right? And you said, "Okay, wherever our launch is," you back up three-four months, we'll probably have some physical betas out with customers so that we make sure that we're testing the physical hardware with our customers. You back up another three months from that, and we'll start some early access samples with customers. So that's a model where the customer ships a sample to us, we run them on our equipment in our facility, we give them the data. We use that not just for papers and publications-
Mm-hmm
... marketing value, but as well to drive the pipeline and the pre-order pipeline for the instrument. And then if you back up from that, there'll be a point where we talk about real data coming off our platform. We will likely have a paper poster that shows some significant progress, and that'll be about the point that we will then lock down final pricing and, and talk about it with Wall Street.
Got it. Got it. Makes sense. And then in terms of just, you know, the areas where you see the most challenges today-
Yeah
... outside of, you know, the multi-affinity probes, is really that the main focus at this point, with everything else more a function of time and you being sort of relatively confident that it gets there for that mid-2024 launch?
Yeah, I mean, I think when you, when you say like, what's the big area of challenge?
Yeah.
What I would say is, even the multi-affinity probes, we have a body of development experts who run that organization. It is one of the largest organizations in our company.
Mm-hmm.
We have built an at-scale antibody development shop that probably rivals most companies-
Mm-hmm
on the planet who do antibody discovery. And there's a lot of iteration that goes on there, but, there's nothing there where I say, "Oh, that's a huge challenge at this point.
Right.
We've solved the challenges in being able to build affinity reagents that target three short amino acid sequences.
Mm-hmm, mm-hmm.
We've figured out how to have those, those probes be broad enough, get coverage, of a molecule so that we can get enough information out of them. And so that's, you know, time and iteration
Mm-hmm.
But that's not really a challenge. I think the biggest challenge is just bringing all of these components together and having them operate together in a reliable
Mm
every time when the customer pushes go.
Mm-hmm.
and then just kind of getting it out and pushed to launch. So for me, it's time-
Mm.
-not necessarily challenges.
Got it. Got it. And then in terms of... You touched upon customer conversations a little bit earlier, in terms of informing which proteins you would go after on V1. Can you share some color on that? You know, what do customers really want? And you know, could you perhaps, like, narrow the set of proteins to ones found in blood or something like that, in terms of how you choose them?
Yeah. So, much like the mass spec
Mm-hmm
... we don't get a choice of what proteins are gonna come out when we say-
Right
Hey, we do 50%-60%.
Right.
For the customer, that's totally okay. Like, when we talk to the customer-
Mm
-our customers are already using multiple techniques to analyze samples, and particularly the customers in biopharma.
Mm-hmm.
These are customers where this information is absolutely critical to their business-
Mm
of building drugs. And so casting the widest possible net to get biological insight, using the mass spec, use Olink, go and use another assay.
Mm-hmm.
That's a normal part of their process. And so if we're able to give them information, new molecules that they haven't seen with one of the other techniques, for them, for them, that's really, that's really the important thing. And the key thing for customers that are using mass spec is that the mass spec always picks the proteins that are most abundant, which are almost always the least interesting proteins in the sample.
Right. Right.
Our 50%-60% will be a distribution across the dynamic range spectrum.
Mm-hmm.
It'll have some abundant proteins, but it'll have just as many of those rare proteins as well.
Uh-huh.
And so for the customers, they're really excited to see that, and the customers are really excited to see data at the single molecule level.
Right.
With those two things, there's a huge amount of interest, even at half of proteome.
Got it. Got it. Makes sense. Now, you know, obviously, you, you're planning to do early access before the full launch. What are your key aims during this phase? And, you know, will customers have an instrument in, in their lab at this point in time, or will it be them, you, you guys processing their samples?
Yeah. So the predominant model
Uh-huh
...for early access will be customer sends us samples, we analyze them and send them back the data. The goals are to work with customers early that are going to publish and that are gonna wanna talk about what they're doing-
Mm-hmm
... which helps us build the base of papers and publications that enable us to get the next set of customers. The second goal for us is to start building interest in buying instruments and-
Right
... hopefully signing pre-orders for instruments.
Mm-hmm.
Those are the goals of early access. Early access, we've talked about it as kind of activities leading up to launch.
Mm-hmm.
But they don't stop after launch. We will continue to run samples as a service for proof of concepts with customers, for burst capacity
Right
... and for times when a customer, you know, may have submitted a grant proposal, but they need six months, but they want to run some samples. Like, we'll still have that capability for a customer. Right before the launch, we will, as we talked about earlier, ship a few physical betas as well-
Mm-hmm
... just to make sure that everything we're seeing in our facilities is the case with our customers as well. I think you and I have talked about before-
Yeah
... we are spread across four facilities on the West Coast: San Diego, San Carlos in the Bay Area, and Seattle. And so we've already got a bit of geographic distribution and have that kind of, we already know how our instruments operate in different places, so that's helping us to some degree.
Mm-hmm.
Making sure that the instrument doesn't have any particular issues with, with-
Right
where the customer is installing it, but we will put a few out there.
I see. Got it. And then in terms of who the key target customers would be, any color you can share there as well? And how do you see the mix sort of trending between, you know, core labs, academic labs, and pharma as you get, you know, closer to the launch?
Yeah. Well, so the way I would segment the market is I would look at it. Let's, like, kind of look at it academically first, right?
Mm-hmm.
Like, if you take the top level of the proteomics market, about half of it's biopharma, about 30%'s the academic and nonprofit research, 20%'s the applied market-
Hmm
... so environmental, agriculture, and so forth.
Mm-hmm, mm-hmm.
For us, I think that the initial business is going to split 50/50 between biopharma, which is drug discovery and diagnostics, and 50% in academic and nonprofit research. That is, pretty typical for what we're seeing-
Hmm
... for other new entrants in the DX tool space as well. Now, you mentioned some other types of customers, like CROs. I view those as partners and on-ramps for the customers that are the ultimate end customer in those two segments, biopharma and academic research. So a customer who doesn't want to buy an instrument may use a CRO for some-
Mm-hmm
... of those services. The CRO may have our instrument, but still, the ultimate customer is in one of those two areas.
Got it. Fair enough. You know, in the past, you've talked about this as sort of the potential for tunability in the platform, where the customer could do, you know, higher plex, I guess, in exchange for, you know, a narrower dynamic range. How are you thinking about, you know, balancing, you know, price per sample, you know, when considering dynamic range and multiplexing?
Yeah. So the platform itself, in the mode of operation-
Mm-hmm
... where we think mostly the customer will operate it, is where they've got about 1 billion of our spots available for molecules. So that means you could run 12 samples with about 1 billion molecules per spot. You could run 8 samples, you could run four samples. There will be some specialized, and those customers will pay a few thousand dollars a sample.
Right.
There will be some specialized high dynamic range applications where the customer wants to use all of our lanes for one sample to get a very high fidelity type of data set off of it.
Uh-huh.
That will be quite costly.
Hmm.
It'll be something along the lines of tens of thousands of dollars.
Uh-huh.
We've talked to customers who have specific applications to do that, but my guess is that 98% of our volume is gonna be on that few thousand dollars per sample, running as many as 12 samples per day.
Got it. And, you know, you probably don't want to get into too many specifics, but in the past you've talked about, you know, a $1 million sort of price tag for, you know, the instrument, software, and services. Against a sort of challenging macro backdrop, have you had any sort of second thoughts about it? And, you know, with the price of, you know, mass specs going higher as well, could that give you some flex in terms of, you know, perhaps pricing it even higher?
It's funny that you say that. I harass my team about pricing it higher all the time now that the mass spec has pushed their high end up to $2 million. I think that we're still feeling pretty good around $1 million, but around $1 million for the deal size could be one, one, one, two, somewhere in that range. But I think we feel good about the price point of that range-
Mm-hmm.
- based on conversations with customers. We had a recent, formal contract study that we've done, not recent enough to pick up that $2 million Astral price point.
Right.
But, I still feel good about the price point around there.
Got it.
And in terms of macro-
Yeah.
You know, the macro has small effects up and down 10%.
Right.
Which, of course, if you're a, you know, 6%-7% grower, it has a big impact. We're a company that is going into an established market.
Right.
We have no revenue to start it. Those little variations don't affect us.
Fair enough. How are you thinking about pricing consumables and so what's the thought process behind that?
Yeah, this is an area where we have done an extensive amount of work with customers, and we haven't nailed down the exact pricing, and we haven't disclosed the exact pricing, but it's a few thousand dollars a sample.
Mm-hmm.
So call it $2,000-$3,000 a sample, and that's all in. Many of these other solutions require you to have a sequencer and do some sequencing and have sequencing reagents. I mean, you need a depletion column. For us, it's the entire kit-
Right
is $2,000-$3,000, somewhere in that range, and it's sample to answer. There's nothing else needed.
Got it. Next sort of two or three quarters, are there any sort of key publications that we should be watching out for?
Yeah. So, I mean, I think I've told you this before, but I tell it to most investors who ask the same question. The key thing that you should look for is that at some point here in the not-too-distant future, hopefully, we will have a poster or a publication-
Yeah
that shows real protein identification out of lysate. It doesn't matter if it's 100, 500, 1,000, 2,000. Once we are there, all of the components have come together, and it's just more affinity reagents-
Yeah
- and more cycles. Once we're, once we're able to publish that type of data-
Mm-hmm
I think it'll be readily apparent to the scientific community that the approach works-
Mm-hmm
that it's just a few more iterations to get it fully to where we need to get it.
Got it. Makes sense. And then just quickly on the, on the financial side of things, you know, you've, you've been pretty prudent with your spending. You've extended your cash runway into 2026. Can you just remind us of, of, you know, your cash position and also examples of how you're managing costs and improving efficiency without sort of not compromising on, on the, the launch process and your efforts there?
Yeah. I mean, I'll, I'll take my last minute to give ourselves a pat on the back, right? I mean, this is... I wouldn't say we are, you know, mostly prudent with our cash out. I would say that we are- we've done an exceptional job
Mm
On the cash front, right? I, I come from the tech world.
Yeah.
My last company, that I was founder and CEO of, went public in 2006.
Mm.
We were profitable by 2010, 20% positive GAAP operating margin before we sold. So this is, like, in our DNA.
Mm-hmm.
We've raised $450 million, roughly-
Mm
since inception. We still have $287 million of that on the balance sheet. We only burned $48 million last year.
Mm-hmm.
In every corner of our company, we spend time making sure that that spend is incredibly effective, making sure that it's targeted, making sure that we're scaling our processes and our people appropriately. Like, for Anna, my CFO, who sits to my left, and I, it is a day-in and day-out job, and I think we do an exceptional job of it. I think that that gives us, we think, the opportunity to have the capital that we need to finish product development, to launch, build a commercial team, and get started on our scaling.
Got it. And, you know, as you think about the upcoming launch, can you walk us through the phasing of that commercial channel build-out?
Yeah. So in terms of the build-out, this is a million-dollar instrument.
Mm-hmm.
It's got a significant pull-through associated with it, and so with that, it doesn't need to be a huge direct sales force, right?
Yeah.
I often get this question from investors, like: What does that commercial build look like?
Mm-hmm.
How many sales reps do you need? Do you even hire 20 on day one?
Mm-hmm.
What I answer is that, you know, the productivity of sales force is based on what's the average deal size-
Mm-hmm
because transaction to transaction takes a fixed amount of work. How big is the value proposition relative to the competition, and how easy, how easy it is to identify and reach those customers, right?
Right.
We know who the top 1,000 customers are.
Right.
We know how to reach them. We have a very wide value proposition, and we have a high ASP at roughly $1 million-
Mm-hmm
Just for that initial deal. That's the recipe for being able to build an effective sales force, and so you're not gonna see us hire a whole lot of people to start. We're gonna start small, start nimble, and start with direct sales people and their support personnel.
Mm-hmm.
Get those initial sales rolling and then learn before we go and continue to scale that.
Do the specs of the V1, once you lock them down, also play a role in the scale-up?
Yes, to some degree.
Mm-hmm.
But what I would tell you is that the entire range of possibilities that we're-
Hmm
still have a transformational value proposition relative to the competition.
Got it.
At least today, I'm not contemplating shipping anything that isn't well above the capabilities of anything else out there.
Got it. Well, that's a great place to leave it at, Sujal. So thank you so much for your time this morning.
Thank you, Tejas.
Yes.