Good afternoon, and welcome to today's webinar hosted by Diaceutics to discuss Precision for All. My name is Simon Geelon, and I manage Diaceutics's IR program and will be your moderator today. Before I hand it over to Ryan for his formal remarks, I would draw your attention to the forward-looking statement slide in our presentation, which is on screen now and available to download at diaceutics.com.
During the presentation, all attendees are in listen-only mode. However, at the end of the presentation, there will be a Q&A session. This webinar is being recorded, and a playback will be available. At this time, I will now turn you over to Diaceutics CEO, Ryan Keeling. Ryan, please go ahead.
Thank you, Simon. Good afternoon, good morning to everyone joining. Thank you for taking your time today to come and speak with us. Next slide, please. We have a packed agenda today. Back to my colleagues on the line, which suggests it's too packed, so I hope you all have your AI agents switched on and listening because we've a lot to get through today.
There's a lot of exciting things happening at Diaceutics that we want to help you understand better. That's the goal of today, is really take a deeper dive into a number of things that have progressed in the business and we're excited about. In a moment, I'm going to set the scene, help you understand the viewpoint of our customers. Sandra's going to go through customer insight. Chris will do a product demo.
Then we'll do a fireside chat with a guest who's joining, one of our customers. Nick will take over and talk about scale metrics. Jordan will come in and do Precision for All and Relay. Susanne will finish with non-core growth. We'll leave enough time at the end for some Q&A. Next slide, please. Yeah. I want to help you understand the world as our customers see it.
Traditionally and historically, pharma has relied upon insurance claims data. This is data that is effectively retrospective in nature. It's helping them understand what has happened in the past, not so much what is going to happen, and it's really important. Sorry, just excuse me one moment. Sorry, I have a cold, and I was trying to fight off a cough there, so my voice was a bit shaky.
Yeah, so, sorry, historically, pharma has relied on insurance claims data, and that is effectively health insurance plan data for patients. That is telling our customers what's happened retrospectively, not before the patient encounter has happened. They use this data to rank their customers, effectively the physicians, to understand who is important to them. They decide these and ultimately create a call plan off the back of that data.
That call plan can inform who the sales rep calls on tomorrow, next week, next month, who they use with their omni-channel marketing, et cetera. It's the default way things are done today. It is absolutely ubiquitous and really important for our customers to have access to this data. But we believe there's additional data, we believe there's additional way for our customers to think about their markets, and ultimately, we're challenging the status quo today. Next slide, please.
When we look at insurance claims data, we have a certain view of a patient journey. Today, we're looking at an example here of a six-year-old boy who has a disease called Duchenne muscular dystrophy. Quite emotive, especially emotive for me because I have a six-year-old. What I want to focus here for a moment is what typically you would see for a patient who has this disease, but we're only looking at them through the lens of insurance claims data.
We see in March 2024, the patient initially visits their pediatrician, Dr. Alvarez. We see a return visit in September 2024, about six months later. This patient has some illness, they have some issues with their gait. We see nothing for this patient until April 2025, whenever we see the first time any indication of this disease.
A DMD code is billed for this patient. What we are asking is this the right doctor that we might want to engage for this particular patient? There seems to be a lot of gaps here and things that we're not seeing in the patient journey. What Diaceutics is trying to do is fill in those gaps and ultimately understand better what's happening with the patient between each of these events, augment the view that we already have through the insurance claims data, et cetera, that our customer has access to.
Next slide. What we bring to the table at Diaceutics is some additional datasets alongside the claims. On the left, you see a certain amount of the patient journey through a claims dataset. On the right, we're augmenting that significantly with lab, genetics, EMR.
These are other types of data that we're bringing to our customer in what is a multimodal dataset offering. You'll hear a lot about that through the course of today. What we're trying to do is give a much richer view and potentially go beyond just those billing events into how do we be more proactive ahead of time with this patient.
Next slide, please. When we switch on our data for the customer, what we see here is some additional events, okay? We can start to fill in some of the gaps. It's the same patient, the same journey, but now we can see that in September 2024, there was a referral to a neuro-dev pediatrician. There's a new specialist that's entered into the frame for this patient. We see they ordered some testing.
We can see the result of that test, and indeed, we can see that now there's actually a different physician who we may want to engage, is more likely the person who's making the treatment decision for this, excuse me, for this patient. That allows us to think differently about how we ultimately educate and build awareness about what are the right options for this patient.
Next slide, please. It's not just in rare disease or examples like we've just shown in DMD. Take something really broad like diabetes. In type 2 diabetes last year, we looked at 170,000 patients in our dataset who had more than two elevated HbA1c tests, or greater than 7% in the year. 50% of those patients had no insurance claims associated with them.
If we are trying to articulate and build a case for why you need to augment your insurance claims data, here is a really good example. If you are a diabetes brand, you are trying to ultimately find patients who are diabetic or potentially pre-diabetic, then this is absolutely critical for you to understand, because you will miss a significant number of the treaters, what we call hidden treaters, because every one of those HbA1c tests was ordered by an HCP actively managing an uncontrolled patient.
Next slide. What we are really trying to get to is the care team. We have talked a lot about how we as a business are trying to identify physicians who are eligible and ready to be educated and made more aware about treatment options, but it is much more complex than it seems, and ultimately understanding the full care team is really important.
In this example, the DMD example we showed, it would have been incorrect to go and try to promote a DMD treatment to that initial pediatrician, even though the claims data suggested that is who they should engage. What we need to do is, in this instance, engage the specialist. They are the treater. You might think that is really logical, but if the data does not expose that person, then there is no way for us to know who it is.
Next slide, please. We are getting better. We now have data that we can provide our customers that help us understand more of the patient journey, but what we really need to do is try to switch our mindset to be more real-time in nature. How could we have got to that patient before that treatment decision was made? Remember, everything with insurance claims data is retrospective.
We are seeing it after the fact. With our lab data and other types of clinical data that we have access to, we can see this more in real time. Remember back in that patient when they got the CK test and it came back elevated? That was our first indicator that there was some clinical understanding of the patient and suggestive of DMD. That was a time to engage.
Seven months before the diagnosis was actually made, there was an opportunity to make that diagnosis. That is what we are trying to do with our real-time data, to help our customer and ultimately to help patients get on drug in a more meaningful way. Next slide. The theme we want to introduce here, and what really resonates strongly with our customers, is this concept of clinical intent, and it is the layer that makes this world possible.
What we are trying to do is understand what is the intention of the physician, not what have they done previously, but what is their intention going forward? We are trying to create discernible signals of what a clinician is trying to accomplish for a patient at a given moment, whether that is how they are diagnosed, who they are going to refer to, how they are going to monitor the patient, or indeed, what their treatment intent is.
Ideally, we understand this before they make decisions, which obviously are often irreversible, so we need to think about this in the context of getting out in front of a decision that is being made. Next slide, please. When we bring this back to the patient, and sometimes we do not do that. Sometimes we look through a lens that does not always expose the fact that there is a patient at the end of this journey.
Here, there's a six-year-old boy with Duchenne muscular dystrophy. Are we asking the right questions? Are we doing the right thing by this patient? Are we putting the patient first and the doctor second? I think we can do a better job here for patients. We see this time and time again with the data we have access to, with the engagement we have with the industry, where there are absolutely life-changing drugs, but they're not necessarily getting into the hands of patients and making a real difference.
Healthcare is moving forwards faster than ever. More innovation, more progress, more potential to change lives. But as the science moves on, gaps appear in the system. Gaps in diagnostic readiness, delayed data visibility, and disconnected clinical pathways that mean patients are being missed. Left waiting for treatment. Life put on hold. Where others see complexity, we see opportunity, the opportunity to unlock the full potential of diagnostic-driven therapies.
Together with our customers, we're building a future where every eligible patient is identified, diagnosed, and receives the right therapy at the right time, where precision is not the privilege of the few, but the standard for all. Our intelligence and engagement solutions combine insight, education, and action to improve testing rates, consistency, and adoption, seeing where and why people are being missed to trigger action sooner and change lives. Closing the gap between scientific progress and human impact. Diaceutics, life unlocked.
That last sentence in the video is potentially the most important. If I leave you from anything from this initial section, I want you to think about what it is that is our purpose at Diaceutics, and that is exactly this, closing the gap between science and life. It's really frustrating to see the advancement in science, to see these therapies coming to market, which are fantastic, absolutely life-changing for patients, and the ability to bring drugs to market today is something we've never seen before. What's really frustrating is when we can't close that gap between science and life, when patients don't get access to the treatment.
That's ultimately what we're trying to do, it's what our customers are trying to do, it's what physicians are trying to do, and the more patients we can help to go on the journey to get access to these treatments, the better. Next slide, please. We are very proud of the fact that our customer list is blue chip in nature and second to none.
We work with 18 of the top 20 global pharma companies. That's everything from a Pfizer to a J&J to a Genentech, and a host of mid-size biotech who are doing amazing things, like RevMed, Syndax, et cetera. These are customers, as I said, we're really proud of. They're hard-fought. They're difficult customers to retain. They've a very high standard, and they have a lot of optionality in terms of who they work with.
We're very excited about the fact that for the majority of these customers, we've been working with them for years, some of them over 20 years. Now, the majority of our customers are recurring in nature. We retain them, and we have visibility out multiple years, some up to 2030, in terms of the engagement we have with them. That's a step change for us in our business from where we were previously with customers.
As I said, that 55 customer list and the 103 therapeutic brands is something that is absolutely a differentiator for us, and actually is part of our competitive moat. The distribution we have and the customer list is something that is really hard to achieve, and we're very proud of that. Next slide, please. For those of you new to the story, I want to just recap on the business model.
We deliver subscription products and services that help life science companies engage the right clinicians, accelerate patient journeys, and improve treatment outcomes. That's what we do. How we do that? Firstly, we've built a partner network of labs and hospitals across the U.S. As we sit today, that's over 670 different partners.
They contribute de-identified testing data and real-world testing signals that ultimately we package up and provide to our customers. The data in itself is inherently challenging to work with. It's transactional data. We do a lot of work to ultimately normalize, standardize, and add value to that data. We have to do a lot to infer the clinical intent from that data. It's not obvious from the data alone. We've built an agentic AI framework which effectively does all that for us in an automated way.
Chris and then Nick later will talk about how we're leveraging that in the business, and Nick will talk to how it's really delivering scale and operating leverage in terms of how we go to market. We package the data up and sell it to pharma in the form of subscription products. Our lead product is Signal. You'll hear a lot about that today. You'll also hear about our full solution, Relay, which we've had a lot of success with recently.
But effectively, that's how our customers consume the data and the intelligence and engagement services that we have. Key to this data and our services is actionability. We want commercial teams in pharma, medical, market access teams to use these products to identify opportunity, prioritize, and ultimately improve their execution. Then crucially, what really sets us apart is the value creation for our customers.
The types of services we offer, the data we have, has a very significant ROI attached to it. That means more patients on therapy, stronger top-line growth for our customers, and lower cost to serve through a more effective model, increasingly important for our customers in the current climate. Next slide, please. The moat. What ultimately do we have around the business?
We start with this partner network. I am going to double-click on this in the next slide. For now, we will just focus on the fact it is 670 labs. It has taken us 11 years to build this network. It takes about two years to onboard a new lab partner. We believe we have really high switching costs for those labs in terms of leaving the network, and we see minimal churn. There is something there that is very defensible going forward. The second is the data.
It is not just what we are doing with that data in terms of how we are adding value to it. It is also the fact that the data is proprietary to us. The data we have, you cannot get elsewhere. There is not another vendor, there is not a competitor. The labs themselves are not providing it to the customer, given the nature of the labs that we work with. So, if you need access to this data, you have to work with Diaceutics.
That is the focus of the network that we have built. Clinical intent and actionability. Again, remember, we are looking for data here which is going to be really actionable and surface that clinical intent. The way we built our multimodal dataset really supports that idea of intent. Step four in our moat is how embedded we are. Typically, we are connected straight into customer CRM systems, into their data stack, and effectively their workflows.
There is high friction if they want to off-board us, and that is something that is again leading to the high NRR and the recurring nature of our business. Once we are embedded, we are typically absolutely core to their go-to-market, and that is something that we enjoy and benefit from. We have built agentic AI at scale. Our business is now very scalable.
The AI is not just automating tasks that data engineering, et cetera, would have done before, but it is adding significant value through our diagnostic pathways, et cetera. The last one is our customer distribution. As I mentioned, we have most of the large pharma and biotech we are already working with, and more to come and across multiple brands. That distribution is part of our moat. It is hard to replicate. It is hard to win and get embedded within a Pfizer, a Novartis, et cetera.
These are relationships that we pride and ultimately hold very preciously. Next slide, please. Busy slide, I apologize, and I promise you I am not going to walk through every part of this. This is where your recording will come in. But we have laid the foundations now for the next phase of scale. We have done a lot in the business over the last few years.
A lot of this we are going to talk to you about today. But then if we go from left to right, we have completely changed our operating model and commercial effectiveness. We have built it around a U.S.-based sales team. We have invested in AI agents that do a lot of the go-to-market for us. We have launched a growth marketing capability. Effectively, the way we sell today is completely different to the way we were doing it two years ago.
The tooling we use, the enablement, et cetera, is significantly advanced. This year, we repositioned the business to go after a broader total addressable market. Jordan's going to talk about that in depth later. That's done now, and we're really focused on building on what we have started as it pertains to moving into Precision for All.
You'll see in our numbers that we've a big focus on ARR and our Relay product, and that's really driven the business forward in terms of visibility and quality of revenue. We've also invested in management. You'll see at the table here, we have our Chief Product Officer, Chris Meenan, who joined us just over a month ago. That's the first time we've ever had any significant product capability in the organization with a real view to professionalize and bring new products to market.
We've also created our Chief Growth Officer role, and that's just at ExCo, below ExCo. At each layer within the business, we've invested in management capability, at the same time ultimately reducing overall headcount and managing our people cost. We've created a growth function, which Susanne will talk about later, but effectively to go after the opportunity beyond pharma and biotech. As I mentioned, we've brought in a product function.
Next slide, please. I promised you that I'd double-click on the lab network, so let me go there and help you understand a bit more about why labs partner with Diaceutics and ultimately what's in it for them. This is one of the most important competitive advantages that we have, our network of more than 670 labs and hospitals across the U.S. What's particularly interesting is that much of this network consists of smaller and medium-sized labs.
Individually, their data may have limited commercial value, but aggregated together, it becomes an incredibly valuable and differentiated asset. That's the first thing to remember. These are typically a collection of small and medium-sized labs. Crucially, these aren't simply transactional relationships where we buy data.
We create a two-way value exchange. Laboratories benefit from benchmarking tools, market intelligence, and increasingly our Physician Engage solution, which supports clinician education and awareness around appropriate testing. That creates a really powerful network effect. More labs joining means richer data, better benchmarking, better tools, and education make participation more valuable. That supports effectively testing and further generation of data. This data flywheel concept is very real. We're ultimately helping labs test more patients and educate more physicians.
As those tests are performed, the results come back in to feed our core business, which is ultimately providing that data back to pharma. Really important here that the labs see benefit, that you understand the fact that it's a two-way value exchange. We get data for the services they receive from us, and it's really hard to replicate this network given the proprietary nature of it.
Next slide, please. One of the things that we have been very focused on is building some exclusivity into this data network. Many of you will see this for the first time now, because up to now, we haven't had exclusivity. We've been very much focused on a path of least resistance to build a network. Now we're going back selectively to ultimately build some exclusivity into this.
We want to make the data even more differentiated than it was before, and we've successfully transitioned some partners to a model whereby it's a really straightforward principle. Diaceutics has exclusive access to the first 30 days of the data, after which the labs are free to do with it as they may see fit. Today, most of them don't. Again, given the nature of the labs, they're focused on their clinical service, et cetera.
But we are increasingly building that exclusivity around the timeliness of the data. It does require more favorable commercial terms, but crucially doesn't materially change the economics of subsequently reusing that data. We're not ultimately having to pay more. Remember, this is a value exchange, so we're giving them more service, but it doesn't materially impact ultimately our margin.
We're being very meaningful here in terms of the proportion of data that we want to have exclusive. We're doing this in a timely, staged way, but ultimately what we're trying to do is really build strategic value in the business. As we look to the future, and ultimately where there may be a strategic acquirer of this business sometime in the future, we really want to be able to talk about not just the proprietary nature of our data, but also the fact that we've got exclusivity on it.
Next slide. Last slide from me for now. There are some shifts in the industry, in the macro, that ultimately I want to alert you to. These are real tailwinds for Diaceutics and something that we are enjoying or will benefit from going forward. The first one is the number of precision medicines.
Today, we work beyond precision medicine, but it's still a core part of our business. There are 36% of new FDA therapies approved in 2025 were precision medicines. That was 22% only four years previously. So you can see the trend and direction of travel there. There's more diagnostic-led launches. Patent expiries.
There's a lot of patent expiries coming up amongst our customer base. You'll see the likes of KEYTRUDA, et cetera, the largest drug ever, is coming up on some patent expiries. That means that the new drugs that companies are bringing to market, they have to have high success in the launches of those drugs. The launch period is typically where customers will spend the most, and that's a key area for us to focus on. Physician access. This is critical.
We are seeing a trend whereby less and less physicians are accessible to a pharma sales rep. They don't accept sales calls. In 2022, that number was 60% of doctors that we could access. Today, that's only 45%. So again, you can see the trend, and there's a need for smarter, more relevant HCP outreach than ever before. The days of a sales rep being able to call on a physician are getting more and more narrow. The last, sorry, four here, the prescription TV.
So GBP 6 billion a year is spent by pharma advertising therapies through TV in the U.S. And while it hasn't happened yet, there's definitely a move toward restricting that, maybe banning it, TBD. But ultimately, we're already seeing a redirection of some of those funds away from very broad TV advertising toward more precision-type engagement. That fits really well with our model.
That's what we stand for. We want to identify the exact physician and the exact patient rather than running a Super Bowl ad. Biotech emerging. 70% of clinical stage drug assets are coming from emerging biopharma. Typically, biotechs have a need for more external capabilities. This is why we built Relay. It's our partnering framework typically for biotech, and we're seeing that happen already. Lastly, two points. Oncology, again, big part of focus area for us.
You'll see in Jordan's slides later. But novel oncology launches in 2020 to 2024 were 132 versus only 79 in 2015 to 2019. Oncology is very much back in fashion and very much a focus of the pipelines, late-stage pipelines of our customers. Lastly, in the U.S. in particular and other markets, the drug companies are coming under increasing pricing pressure, and that means there's a greater focus on commercial ROI.
Our data is second to none when it comes to demonstrating ROI. We can lead with that. We can show customers what the return on investment is before they even buy, and that really fits with our narrative. Going to pass now to Sandra Blake, who's going to walk you through some of the customer perspectives. Thanks, Sandra.
Thank you, Ryan. Good morning, good afternoon. I'm our Chief Marketing and People Officer, and delighted to share some of the reasons that customers choose us. First, our network. Some of these Ryan has already touched on, but our network, we've built relationships across 600-plus labs, connecting us to the people and the processes behind diagnostic testing.
Those relationships give us access not only to the data, but also the understanding of how testing works in practice, which is very, very important. Second, Reveal. Reveal is our diagnostic intelligence engine. This is where we turn the complex diagnostic data and activity into decision-ready intelligence. This is where we help customers see where opportunities are emerging, where patients may be getting missed, and where to act next. Experience, thirdly.
We've more than two decades of specialist knowledge across diagnostic science, lab practice, and bringing therapies to market. This expertise really helps us interpret what the data means and translates that into useful commercial decisions and priorities. Fourth, you've already heard about Relay quite a bit. You're going to hear a lot more.
This really takes us from identifying opportunities and helping the customer act on it. So it's really coordinating intelligence and engagement and capabilities and products within that broader partnership. I'll focus a little bit more on Relay on the next slide when we look at product. Finally, DXRX. You're about to see a demo from Chris, but this is our proprietary AI-enabled platform that supports delivery, making the intelligence accessible, and importantly, usable within customer workflows.
It really is the strength from all of these capabilities together that makes a difference from a customer perspective. Our network supplies the data signals, Reveal and our expertise then turn them into intelligence, and DXRX and Relay help customers put that intelligence into meaningful action. It is that combination that makes our proposition valuable and difficult to replicate.
Customers often tell us, amongst other things, whenever they are talking about us versus competitors, that we start closer to the diagnostic activity and we connect that intelligence and that insight into action. I think that is a very important aspect of why they choose us. The next slide just starts to bring to life our product portfolio. Over the last 12 months, we have done a lot of work to really streamline and simplify.
Not long ago, we had about 30 different offerings that we could offer, and today, we have got a much tighter product portfolio, you can see here, very much organized around the customer needs you see across the top of the slide. We start with a specific barrier or the customer challenge, some of which you have heard Ryan talking about already, and that could be simply where are eligible patients being missed in that diagnostic pathway.
Our product focus reflects our maturity as a business, so we have concentrated on high-margin recurring revenue products where we have the greatest right to win. That makes our proposition easier for customers to understand and buy from us. Really importantly, it makes it easier for our commercial teams to focus and sell.
Intelligence solutions, which we have touched on already, Signal being the hero product there, so that is the little dotted bars towards the top of the slide. The products and solutions there really help customers understand the market, the clinical intent that Ryan talked about, identify the relevant diagnostic events, so that little six-year-old boy's test ordering, and determine which patients, clinicians, and laboratories matter most.
It is really about helping customers understand the opportunity and decide what to do next. Then engagement solutions take over. It is really about helping the customers then act on that intelligence, the take action bit, and working with clinicians and laboratories to maybe improve testing or to support treatment adoption in that window whenever treatment decisions are being made. As Ryan has talked about, that is engaging physicians and engaging labs when those decisions are still to be influenced.
Our route into customer relationship often starts with Signal, and that really is our hero intelligence product, and it really does help customers identify where eligible patients are being missed and where they should focus their efforts. That can quite often be within 24 hours of a diagnostic result, and really linking that to the treating physician is super powerful.
From that starting point, we expand the relationship through the additional solutions, and ultimately, we hope that that culminates in Relay, our full suite solution, which brings the capabilities together into that one coordinated partnership across the commercial journey. Really what Relay does is include a blend of intelligence and engagement products. For Diaceutics, this creates a really clear path to growing our revenue per customer, building those deeper long-term relationships.
We're actually going to show you a very short video that brings the Signal product to life. We're then going to jump into the platform for a demo by Chris, then I'm going to come back and talk about some return on investment and some stories around what customers or how customers have benefited from being with us, then we'll move into the fireside chat to bring in a customer perspective. If we could play the video then Chris will pick up after that. Thank you.
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Hi, everyone. My name is Chris Meenan. I'm the Chief Product and Technology Officer here at Diaceutics, and I'm going to walk you through a quick demo just to show you what the front end of the system looks like. In the interest of time, I've already logged in to the system, but we have a very secure system, two-factor authentication, and so forth.
So, this is the front end of the platform where clients can log in and see and visualize their data. Importantly, though, this isn't the only way to engage with the data. This data is also in parallel been piped directly into our clients' own systems, as Ryan mentioned, such as their CRM, their data infrastructure, a Snowflake instance, or an S3 bucket, and directly into their own workflows. We do this because we want the solution to be sticky, right?
Getting it to the right client system is really important. We haven't wanted to do is force clients basically to only access their data through this system. Ultimately, it's more valuable for them and helps us get the stickiness we want. But this system that I'm going to show you today is a very easy way for them to visualize and do any self-service data discovery that they might want to see through this visualization. When I log into the system as I have, I'm presented with my projects and feeds. If I were a client, I'd only see my one project or a collection of projects, and these are all user-defined.
If I am AstraZeneca and I need one person with access to one project and another person or team with access to different projects, I would only see the things I am supposed to see based on my user credentials, and we have a team that manages those sorts of things. What I am going to show you today is actually Diaceutics' Relay program that was mentioned previously by Sandra and Ryan.
I am going to basically give you this real example of the work we do today with PTx around NRG1. If you notice here, I have just logged into the project. When I log in, I see a number of different dashboards and different products that we have, and then there is also a number of downloads and reference materials around the alerts that have gone out in the creative media world, data dictionaries, and all those types of things.
What I will go to first is the Signal dashboard, Signal being the main product that underpins a lot of our revenue and feeds a lot of our other products. Essentially, Signal is a daily feed of data where we can identify, in this case, patients that are NRG1 positive, and that data is also connected directly to PTx's CRM system.
We can take this data and do a number of things with it. We can look at it here. We can also activate it via email, and our peer-to-peer pathologists go out and do engagement and have that outreach, but it is all underpinned by this daily Signal. Here are a number of different views of the data in the dashboard views that you can look at, and everything here is filterable.
You can look at multiple views, and once you click on something, it re-renders and reorients you to show you the best view. What you are looking at here essentially is how many patients have been basically tested on a daily basis, and then how many of those tests are positive. If I pick a day here in particular, and I am going to pick, let's see, October 14, what you see is we have 459 patients that were tested. If I pick that same date here, oops, I have two patients that are positive. This is a very, very rare mutation, and so these are the types of numbers that we would expect here.
What it means in this case, and overall if you look at the trend, is that we are able to identify one or two patients every single day to really demonstrate that we need to activate this field and all the omnichannel marketing around it. One thing that is really important that we talk about is all of these things are linked by an NPI number, and that is a National Provider Identifier.
It is a really important number in the U.S. This number links things like claims data, physicians who are doing the scripts, physicians that are ordering tests, physicians doing visits, and those sorts of things. This number also allows us to link these things together and understand who the treating physician is versus the attending physician and so forth.
We get the NPI number from our datasets, things like first name here you will see, and last names, where they are located, et cetera. We also can apply our own intelligence here on top of that. We get things like, essentially here, if I click off, I clicked on things here.
If you look here, we get things like where these patients are located, essentially what types of testing they have done, those sorts of things. We can actually understand where they are in that testing journey. All of this intelligence and our backend data processing essentially is applied here. Here, if we look down as well, we see more information about NPIs with positive patient value.
Again, their names, whether the patients are non-small cell lung cancer patients, what types of testing they have been through, DNA versus RNA, whether there is a line of therapy, whether it is first line, et cetera. Here, this is the number of patients tested and whether or not they have a positive result.
We can also scroll down and look as well, look at different splits of the data, essentially looking at things like what is the patient volume by testing type, so whether it is RNA or DNA testing. Also look at testing by volume, by line of therapy, so first line, second line, and what are those splits. Look at things like positivity rates, and so you will notice a big spike here. This is an ingestion of additional data that we got in for this client, so we contracted with a different lab and then pulled that in.
That essentially reflects that spike here. Also get a very nice heat map view that shows the geographical spread by state across the U.S. You see here Texas, where we have really good coverage. There are other states as well, such as Ohio or New York, where we see concentrations of these patients.
I can click on a state, here I will click on Texas, and you can actually drill down into the state where we see things, look at things like the city where these patients are and even the zip code, et cetera. We are continuously tracking this data over time, and we can look at the impact of different testing, and that is really important. If I scroll down further, I can see things like RNA, again, as I mentioned, RNA versus DNA testing.
For this drug in particular, you want to be doing RNA testing because DNA testing will not identify as many patients. We want to be sure we talk about RNA testing because that will help find the most patients for this life-saving drug. Again, we can see here things like incidence rates, positivity rates by month.
We can also look at this by institute level, so we can see which institutes where this is being affiliated with, so whether this is NYU or Iowa or Maine, et cetera. We have a wide spectrum of institutes all across the U.S. This is the Signal data. It is a really important part of what we do, but again, the value is that this is at the same time being pumped directly into our client systems and that the sales reps are going after and so on.
But in this example, because it's a Relay program that Sandra mentioned, this data underpins each other parts of the project where we can start to engage with physicians and do our Expert Exchange. Let me show you to the next part of the demo here. I'm going to show you. Let's see here. This is our Physician Engage view.
The scenario here is once a patient's been identified as positive yesterday, what will happen today is that we'll be sending their physician an email on the utility, in this case, of NRG1, and we can track the usual metrics that you'd expect, such as open rates and click-through rates. So we're being really, really targeted in how we do this. This is not a wide spectrum, spamming hundreds of people sort of thing.
Instead, this is a very precise message that they had a patient yesterday, they get an email today to try to connect the dots between what they see in the pathology reports and to what the patient could go on therapy-wise. And we see that in the unique click-through rates that we're way above industry average in these things, and we get really good engagement.
And so you might ask, why is that? It's really about the timeliness. The timeliness of the notification really increases the relevance to the physician. So this isn't an email they get a couple weeks later. This is an email that they get today. And this high relevance really means that the physician's more likely to engage with the content.
And so again, we can map all this back to the NPI, and I'll scroll down through some of this here, map it all back to the NPI, and we collect all this data, and it feeds back into the system. So we have a feedback loop so we know how to make the campaigns better and better over time by what we call physician affinity data.
And so we can tell a lot about the physician, what they're going to click on, and all their preferences, and that affinity comes back into our system to make our engagements higher performance over time and as we go along. And there's this data flywheel here that Ryan mentioned that really helps improve the engagements. So here's various institutes and physician specialties.
So here we're targeting oncologists, essentially medical oncologists that are the top ones we're looking at here, but there are other practitioners that we're trying to educate around these things as well. The important thing is the information has already gone to these physicians, the marketing nudges that we're trying to do. This is just a way to see, did they open their emails? Did they click through the emails? And what's the engagement like?
So this view really helps us understand metrics around engagement. So I'll show you to the next piece of the demo. Another really interesting way to use Signal is in peer-to-peer education. So the scenario again is that a patient was positive yesterday. We see that overnight. We activate one of our pathologists to go have a conversation with that oncologist to talk about the utility of NRG1 and help them connect the dots.
Our healthcare providers come in and sign in every morning. They have a list of who to call. They phone the physicians up on their list. There is really good data here around ordering provider awareness. What we see is sometimes the ordering provider is aware, fully aware. Sometimes they are partially aware, but many times they are not aware at all that they have a positive patient.
In this case, here are 25 patients that potentially never would have gone on that therapy because their physician was not aware of that biomarker and not aware of the clinical relevance. Our pathologists are going in and supporting with education so they can help the clinicians understand the clinical relevance. Importantly, we are not telling them to make a decision. The decision is always, of course, with the physician.
What we want to do is make sure the physician is well-informed, has all of the information in front of them so they can make the most appropriate decision for that patient. We track metrics around this. Did we contact them? Was it successful? Are they likely to consider the therapy? Do they want to be connected to the pharmaceutical company and so forth? We can track this again by state, and really also track engagement to ordering success rates.
We track all these outcomes, and we have a very high connection rate, over 72% with our ordering physicians. One thing that is really interesting is we see the comments coming back in here, and has there been a conversation? What was the conversation like? What did they hear? What is the information that our pathologists are entering into the system? What did the physician know about?
Are they going to talk to their lab or put the patient on the drug, et cetera? If I scroll down here and find an example. See if I can find one here. Here is an example. Dr. Adima in Michigan successfully contacted at Mercy. If you look at the comments, we see things like, "Dr. Adima's pathologist was not aware of the NRG fusions at all.
We contacted the lab and connected them with the healthcare provider in order to find the patient." Really interesting data here about the quality of the communication and effectiveness of the educational content. Signal, again, driving all these engagements, driving that surround sound, if you will, so that the patient has all the education. Sorry, the physician has all the education, all the information needed so they can make the best decision for the patient.
Because we know, according to clinical trials, that NRG1 fusion patients do better on NRG fusion drugs. We want to make sure the patients have the greatest opportunity to see another holiday, make the memories they can with a loved one, and this kind of work supports that. One last thing to show you in the demo.
What I have shown you up to now is our ways to use the data on a daily basis. Very actionable, really driving lots of activity, and really central to the pharmaceutical company customer's commercial engine. But we have other ways of using the data. This is an example where a client could use this data for quarterly call planning, which is really about segmentation, right?
To take a step back at the big picture, this is a good example, essentially where every couple of months, quarterly, six months, you could look at the data and see how did the testing behavior of physicians actually change? We can look at things like testing rate, and in this example, we've done a lot of work with this client in particular to figure out, could you actually segment physicians by persona?
Could you start to think about the different messages that are really important to them, and how do you be most effective? That's, I think, super interesting. You can also look at different testing rates by specialty. How do things vary, whether this is an oncologist or pathologist, and that is ordering the test. Could look at things like testing rates by states?
To really zoom out at a macro level and look again, help us understand national trends or state by state so we can understand, and they can understand the effectiveness of various initiatives. Are the pharmaceutical companies really moving the needle with testing rates or testing behavior? All of these things are filterable.
You can change the criteria and drill down into the data that really allow you to discover numerous insights here. For our clients, this is really important data. It's what drives their core planning, where they put their reps in the future, where they can see how many patients are tested and how many are being diagnosed. We can click into these and see what it looks like at one health system versus other health systems, et cetera.
You still get the breakdown by states to really help you understand different testing behavior. I think this is basically the end of the demo. It's an extremely useful data visualization tool, and it's easy for our customers to use, as you can see. We're going through this quite easily. It's easy to help them see what's happening, help them understand a number of the key metrics we already talked about.
Ultimately, this tool's not where the value is. The value is this data going directly and natively into our own client systems, and that's what drives their daily commercial and strategic activity. That's my demo. I'll turn it back over to the team, and thanks.
Thank you, Chris. Just a little minute while we go back to slides. I'm going to talk a little bit about, I guess, what's the so what from a real-world impact and proof perspective. The examples you can see on the screen, I'm not going to go through them all, you'll be glad to know, just start to show how our capabilities translate into measurable outcomes.
These proof of impact examples come directly from our work with the U.S. leading biopharma companies that Ryan talked about. They're anonymized, but they are real-life examples. I think what's really important is they map very specifically to questions that commercial and brand teams are asking of themselves, questions like, "How quickly do we find patients? Do we reach the right physician at the right time?
Is testing happening quick enough? A lot of what we do is really helping those teams answer those questions, hence the huge value they see in Diaceutics. Starting at the left, just going to pull out a couple of examples. We help customers identify opportunities earlier. You've probably got the message that timeliness is really important. In the AML example, which is a very aggressive leukemia, time every day matters, and there's a very short treatment window.
In this example, we identified the relevant clinicians within 24 hours of the laboratory Signal, compared to using insurance and claims data, which would have been 38 days, probably too late for that individual to go onto the right drug. What you're seeing here is win-win from a human perspective, but also from a brand owner perspective, identifying that patient at the right time, super critical.
Moving to the second column, these examples really show us how we turn intelligence into engagement, so the both sides of our product portfolio. The first one actually is another leukemia example, and this is where Physician Engage delivered a reported seven times return on investment. You heard Chris talking about Physician Engage, that's our digital, the email outreach to physicians.
There's another example here, which is Expert Exchange. Expert Exchange is really our product name for what you've heard Chris talking about, whenever a pathologist can speak to a treating physician at the right time to give them information and education about a drug they may not have heard of. We heard about the NRG1 example in that case. That's a case study on that type of work. The third column really reflects our ability to improve testing itself.
You can see there, one program engaged more than 150 labs in three months, which is a really good rate, while another therapy project achieved a 75% reduction in testing turnaround time. Again, timeliness really matters, so testing volumes, but also testing time turnaround, really critical for ultimately the patients getting onto the right treatment.
Then finally, the capabilities really develop into substantial lasting partnerships, and there's an oncology example there that just represents a seven-year relationship, including work to build relationships with more than 200 laboratories before a brand launch. All different therapies, different customers, different examples, but I think what we hear commonly and what's really important underneath a lot of our case studies is, did we see opportunities earlier? Did we focus resource or help our customers focus resources more precisely? Were they able to act when intervention can still matter?
Are we measuring the things that actually matter to the commercial teams we're working with? What we see is that intelligence plus engagement translating into that customer and commercial value. That's a little bit of some of the case studies. On the next slide, if we want to move there, it's just a little bit more in terms of why customers value us and why those relationships deepen.
I think the first quote again captures the importance of precision, a big theme today. But a customer may have thousands of specialists, thousands of physicians they could engage, but at any moment, there's only a small number that are relevant. Hopefully you've got a sense, even seeing the demo, that our data really helps them understand who's very important for them to reach at that very particular moment.
Our value is really helping them identify who matters, when, and why, so that they focus resources, and clearly there is a return on investment from that. The second quote really speaks to the breadth of our data. This customer in particular recognizing that assembling comparable intelligence would require several partners, and bringing it together makes their work simpler and gives them a more complete view.
We hear that a lot in terms of our customer feedback and research. The third quote really focuses on one of our key differentiators, which is our people. Customers come for the data, but stay for the team. This is really all about our specialist expertise that helps. It is the so what, helps them interpret the intelligence and decide how to act on it. That is really how an initial product purchase can develop into that trusted relationship.
You heard Ryan talking about the depth and the length of some of the relationships we are super proud of and treat very carefully, given how precious they are to us. The four examples that you can see then at the bottom of the slide, I guess just further support expansion. You can see there, a customer describing how easily additional datasets integrate into their workflows, how they can measure returns. We have talked about that.
Particularly important with some of the structural shifts and with pricing pressure that Ryan talked about. Our work ultimately supports commercial objectives, and many of the teams we are working with are focused on commercialization. They also see us helping address questions that other partners are not raising. That is another way that our expertise can really come in and bring positive provocation to teams that maybe haven't thought about things.
Again, we hear that a lot in our customer research, and that is a real value add. As customers use more of our capabilities and see our value, they then have a stronger foundation for that broader relationship. Ultimately, really is the aim. Hopefully you can see that our customer proposition is much broader than just the data.
It is that precise action, it is the measurable returns, it is the integration and expert partnership that we have spoken about with some of these examples, and that just creates that stronger platform. We are going to shift gear. We will just pause for a minute, just let Andrew. I think he is just come into the room. I heard the sound of him coming in. But we are going to shift gear and go into a bit of a fireside chat with Andrew from one of our valued biotech clients.
What we want to do is bring in that external industry perspective. You are very welcome, Andrew. I can see you. I am glad to see you. We are going to really hear Andrew's perspective, not just, or not even Diaceutics, but this is much more the industry perspective that we have asked Andrew to bring in. If you are okay, Andrew, I think we have got a lot we would love to talk about. I am going to keep an eye on time. So we have got about 15 minutes for this conversation. But I think we will just dive in and get going with the first question.
That sounds great. Can you hear my audio okay?
Yeah.
Perfect.
All good. You are very welcome. Andrew, you have launched highly targeted cancer medicines. Why is finding patients who could benefit sometimes as difficult, more difficult than developing the treatment itself?
Yeah, that is a great question to get this session kicked off, and it is a really important one. From my perspective, the holy grail of data is that data offering that is the most complete, or in other words, has the most records, right? It identifies the most patient before, and that is the key word, before a treatment decision can be made.
It is really about timely data. If you do not know the data in our industry, most of it is historical. It lags two weeks or four weeks, six weeks. Sometimes claims data can lag two to three months, Medicare data, all that. It is a historical perspective of where there may be patients. Very few data sets in the pharmaceutical industry literally tell you, "In the present, there is a patient that needs a treatment. Get your rep into the office," right?
People in my position need to purchase multiple data sets from multiple vendors to really get completeness or enough data to act on. I think it is important also to realize that every data set comes speaking, really, the way I think of it, a slightly different language, right? All these different data files from all these different vendors need to be brought into a data environment and then translated, because each one is very unique, so the results can be interpreted, right?
Then once those results are interpreted, they have to be communicated out to the various business partners we have in our organization. As you would expect, this costs a significant amount of money, both to purchase the data, but then you have to interpret it. For startup companies, you do the best you can.
You buy what you can with the budget you have, and really, if you get about 20% of the market, now think about that for a second. If I get 20% of the market of all diagnostics testing out there, I feel like I've done a pretty good job. Hopefully that answers that question. It's a good one.
Yeah, no, thank you, Andrew, and I think it just highlights that system challenge. The system needs help to ultimately get that timely data together in the hands of people that need it most whenever decisions are being made. I think that's been a big theme in this session already. Thank you. Is there any other things from a visibility perspective that a pharmaceutical company typically lacks when they are launching a medicine that is so dependent on diagnostic timing?
Yeah, absolutely. Especially if it's a new therapeutic class of drug for something that hasn't been treated before, absolutely. The way I would think of it is really like three buckets. It comes down to diagnostic testing visibility. What's important for the audience to know is that there is limited insight into which physicians are actually ordering the tests and how often they're ordering them, which patients are being tested, who reads and communicates the result of the test.
By the way, on these tests, sometimes it's a report of 11 or 12 pages. Where is that indicator potentially located within that test? Then it's about getting it early enough, as we said before, to really inform the treatment decision before a decision is made, right?
Normally to do this, what you would do is you look to hire a sales force depending on the therapeutic area. Maybe it's 30 reps, maybe it's 100, and you deploy them three to six months prior to a product launch, and you give them direction to really map out all of our key accounts or our top customers to find all the decision points, who reads the test, who orders the test, what happens after the test potentially identifies a positive patient.
They're out there about three to six months prior even having drug approval, trying to get this information so when you do get the approval, they can hit the ground running and start promoting the product. As you all can imagine, it is very expensive to deploy a sales force, and that also runs into significant issues if there are some or any type of delay in the product approval. I think the second bucket is, it comes down to patient identification and opportunity.
You have to know where in the account these patients are being identified when they are positive. Are there specific institutions? Here you want to think of academic super centers or community centers, and where eligible patients may be missed, and this commonly happens. Patients are sometimes missed due to inconsistent testing practices or how tests are potentially communicated. Then last but not least, I would say it comes down to patient journey and treatment. What happens after a test comes back as being positive? Where does the patient go for treatment?
Do they stay in a community care setting? Do they go to academic? Is it a hybrid of both? Who is treating these patients? Who is caring for these patients? At the end of the day, it really comes down, in my mind, to those three things.
Thank you, Andrew. I guess what I take from that, what I hear is, the ideal is, and we are not in an ideal world, we know we have got industry challenges, but it would be one decision-ready view of testing, patient opportunity, pathway progression, so you can actually take that fragmented data and actually use the intelligence to know what to do next. Would that be
Exactly
a fair. Yes.
That's exactly.
Yeah. No, thank you. We're going to take it a little bit more sort of industry level and future focus. What would you like partners in the industry to do better? Where do gaps still exist? What would you be asking of players who are part of the challenge and also part of the solution?
I think it comes, again, I'll go with buckets of three as the way I like to think. I think it comes down to the scale and access to data. When I think of a specialized partner like Diaceutics, you bring an established team of experts to access and deliver what I'll call fragmented and sometimes dirty laboratory data. They're missing pertinent details in their fields.
Not all the data comes complete all the time. It's not just lab data, but it's other healthcare data sources. I think it also comes down to specialized expertise, right? If you're in my shoes, you have to spend years, which you don't have, and lots of money, which you also don't likely have in a startup environment, to build the framework that's needed, right, to be able to do all this.
The best option for somebody in my position is to call in Diaceutics, tell them what I need, we'll draw up the agreement. The data will typically get delivered in four to six weeks. If there are questions that come up or concerns or we have problems with the data, we have these weekly calls where six to eight of subject matter experts, I guess is the best way to describe it, on the Diaceutics front, will literally, that know their data the best, they'll get any questions or problems that we have addressed ASAP.
I think it would be remiss if I didn't shout out to the team that supports us, because they are the best of the best, and we appreciate all the support they give us. Last but not least, it's the actual insight component, right? When I have conducted benchmarking at the various companies I have worked at, when I have completed benchmarking,
I have been able to demonstrate that an alert of a positive patient that is followed up with some sort of live interaction, or we call them sales calls, within a 72-hour window, has three to five times higher likelihood of initiating therapy, depending on the patient that we did not have an alert on and the rep was not able to get into the office for. Of course, that is highly dependent on the disease state. As you can see, it really does make a difference and make a positive impact.
Thank you, Andrew. I think it is what you have said, any myth around it just being about access to data, it is far more complicated than that. It is the expertise, it is that insight into action. Yeah, so thank you. Very, very good examples. We always have more to do, but thank you for the positive feedback that we are going in the right direction. Thinking a little bit more future focused, looking three to five years ahead, do you see diagnostic intelligence becoming more important or less important to the pharmaceutical commercialization teams that we work with, and why would that be?
Well, it is definitely more important. I think as treatments become more targeted, it is not enough to know who is prescribing your medicine. You need to know where the patients are being tested, where they might be missed, what is happening, as I said before, between diagnosis and treatment. From my perspective, I believe that the companies that can connect those dots the fastest and act on that information the fastest, they are the ones that have the real advantage.
Thank you. Second to last question, if a pharmaceutical CEO launching a new precision medicine asked you where should they invest to maximize chances of commercial success, how important would understanding the data journey be, or what else is in the mix in response to a question like that?
Yeah, so if a pharmaceutical CEO is launching a new precision medicine, what would I recommend?
Yeah.
I would say don't give all the money to marketing. Everyone is competing for resources and dollars when launching a new product, and not every investment moves the needle. With that said, sometimes you do what you know has worked for you in the past, and knowing the ROI may be slightly limited. From where I sit, what does make a difference is getting your sales team in front of the right customers when they have a patient to treat, right?
Because I've had a career where I've been customer facing, I've been in sales leadership, I've done multiple roles in this industry, and there's one thing that I've learned to be tried and true. The most valuable sales call is the one that happens when you are needed by the customer the most, right?
The way to think of this, if I were to create an analogy over it, would be you don't need an electrician in your house when your plumbing is broken, right? So it really is important to be there when the customer has a need or a patient has a need for patient resources. That's where you really connect with your customers, and that's where you can really help patients get what they need.
Thank you. I guess reflecting on your answer there, I think the understanding that diagnostic data journey should make every commercial team more impactful. Potentially you don't need as many sales, as much investment in marketing if you've got the right data at the right time, as you've talked about. So it should hopefully fuel commercial impact across all of those teams.
I couldn't agree more.
Why work with a specialist partner rather than starting to build that type of capability yourselves in the organization? Talk a little bit about the importance of partnership.
Well, here's what I'll say to partnership. Having the capabilities connected with a good partner gives the company an ability, in my mind anyway, to target better. I probably sound like a broken record here, right? But it's about targeting, it's about showing up at the right place at the right time, and really helping patients and impact positively patients that are in need, right?
At the end of the day, that's what this is all about. It's about helping patients. With a complete suite of services that Diaceutics offers, I'm thinking from banner ads and customer EMR to genomic testing to the Expert Exchange program, and probably a ton of other things I should be mentioning but I'm not.
Diaceutics, in my mind, for a small company, even mid-sized pharma, and in large pharma, I've worked at them all, Diaceutics, in my mind, becomes the proverbial one-stop shop, right, for companies.
Thank you very much, Andrew. That brings our fireside chat to a close. You are very welcome to drop off the call, and we really appreciate the time you have spent with us, and hopefully that has brought an external customer and industry perspective into the conversation. I think a lot of what we have heard very much in tune with the conversation so far, in terms of timeliness and ultimately intelligence and then activation off the back of it. Really appreciate your time. We are going to go back to the slides, and I am going to hand over to Nick.
I will just say thank you so much for having me on the call, Sandra Blake. Really passionate about this and appreciate the opportunity to get on a soapbox and pontificate. Thank you for having me. I appreciate it.
Thank you very much from all of us.
Have a great day.
Thank you. Thank you, Andrew, from me and the rest of the team, and Sandra for hosting the session. It was really great. Good afternoon, everyone. Good morning, everyone. I am going to take 10 minutes to walk us through how the work we have undertaken translates into the quality of our financial business model today and going forward.
I would like you to take away two things from this. One, that ARR is giving us more visibility, and two, we have scale levers we can pull in the business, and we are going to talk to both of those. The numbers I will talk to, you can start to see on screen here, are anchored in the actual results for the first six months to June 2026, but they will also look forward to the full year 2026 numbers based on the median analyst guidance presently in the market.
We are increasingly becoming a high-quality recurring revenue company rather than a traditional project-based services business, which we were and where we started some 20 years ago. Revenue is growing, as is the quality. Revenue growth was 20% in H1 2026, 22% on a constant currency basis, and is supporting a three-year CAGR of around 21%.
Growth has been driven by the number of customers, the number of customer brands, so the drug assets we work on, and the value of products sold to each customer. So we are firing on all of the cylinders. At the same time, recurring revenue is up 42% in H1 of 2026 and represented overall 75% of the revenue in the half. If we look at that on a trailing 12-month basis, recurring revenue was 61%, and that is up from 55% in the prior trailing 12 months to the end of June 2025.
Importantly, annual recurring revenue has grown strongly as more customers adopt our intelligence and engagement solutions through the subscriptions, the products you have seen, products that Chris demonstrated. ARR increased by 79%, GBP 29.4 million in the 12 months ended June 2026. It grew 47% just in the first six months of this year to June 2026, and underpinning that growth was net revenue retention of 149%.
Gross ARR churn dropped to just 6%, and customer ARR expansion was GBP 13.3 million, roughly split GBP 4 million from new brand wins and about GBP 9.3 million from upselling to existing brands. As a result, ARR per customer increased 10%, and ARR per customer brand increased 36%. Next slide, please. Importantly, a step change in the ARR is fundamentally transforming the level of visibility we have.
It was not too long ago that we were starting a year with visibility over less than 20% of our revenue target for that year. That meant a substantial proportion of each year's performance depended on winning and delivering new work within that year. The ARR on the 1st of January 2026 was GBP 20 million and covered around about 43% of the 2026 median consensus analyst guidance.
You see that in this graph here. The growth in ARR in 2026 to date and forecast towards the end of the year will be transformational. ARR of GBP 38.5 million on the 1st of January 2027 would, in this case, give us coverage of around about 68% of the 2027 median consensus analyst guidance, which is about GBP 56.3 million for 2027. So we are potentially shifting from 43% coverage of revenue in ARR to 68%.
If we were to project that forward, our ARR growth ambitions in this example, say GBP 50 million of ARR by the end of 2027, we would move towards 75% ARR coverage of that year's, of the 2027 year's median analyst guidance revenue. Again, just showing the fundamental shift ARR is having in the visibility that we have in our business. Next slide, please. The business progress translates further than just top-line metrics.
While the group continues to invest in platform capability, the DXRX platform, AI enablement, data assets, customer engagement, we remain focused on ensuring that growth translates into improving profitability and cash flow generation. Gross margins remain exceptionally strong at around 85%, reflecting the scalability of our DXRX platform and the Reveal data engine behind it. Profitability and free cash flow continue to improve as operating leverage becomes increasingly evident within the business.
We will talk more about the scale in the next few slides and how we are going to do that. Taken together, all of the metrics we have shown and discussed demonstrate a business whose revenue, whose profit, and whose cash flow are becoming increasingly predictable, scalable, and of higher quality. Next slide, please.
I mentioned scale, and I want to speak to four specific areas where we see this happening. Firstly, pricing. Customer research has validated we have pricing headroom with our key Signal product. One-off price reset opportunity is estimated at 18% to 22% for biotech and 8% to 12% for big pharma. In addition, customer research verified a 4% to 6% ongoing annual price increase tolerance, another potential mechanism which we have not been able to fully take advantage of to date, but we want to focus on.
This mechanism, of course, is not guaranteed, and our research shows that many customers will resist price rises at or above this level without clear demonstrated ROI. We feel we have this evidence. Hopefully, you can see that by the session you have just seen, the products, and the meaningful return we are seeing from our customers.
What gives us confidence in this data is that 10 out of 14 identified Signal customer surveys said that Signal is a must-have product. Due to its ROI, seven out of 14 expressed a willingness to pay additionally for additional functionality like faster alerts, more biomarker coverage, that sort of thing. Next slide, please. Secondly, data. What is fundamentally important to the business model is the economics of our data investment.
To date, we have spent around 12% to 15% of revenue per year on data investments, and this helps us underpin the Reveal data engine, which is proprietary and is a key competitive moat. Going forward, we have indicated that we intend to continue to invest in data, and this would be up to around 15% of revenue per year. When you compare this to our peer set, as we have done on the left side of the slide, you might initially conclude that we are under-investing in data, but this would miss the critical point.
Our advantage isn't that we spend less on data, it's that our business model allows us to acquire, develop, and monetize the data much more efficiently through relationships with labs built on value exchanges, through the ability to reuse the same datasets for multiple customers and multiple products, and through the network effect created, the data flywheel Ryan described earlier.
The work of our lab partners is generating more data. We can target investment in areas to enhance data attributes rather than spending more simply because our business needs to grow. We have benchmarked our data investment against peers, peer disclosures from public sources. Please consider this directional rather than factual, but Diaceutics does not need to match the spending intensity of other data business models to compete.
Our differentiated network and our commercial model allow us to invest meaningfully in strengthening our data advantage while driving attractive growth and margins. Next slide, please. Thirdly, AI. AI is both an efficiency tool and a product opportunity within our DXRX platform. We should not portray generic AI as a competitive moat. The defensible asset is the Reveal data engine, the lab network, domain expertise, and improved engagement workflows, and Ryan talked about the moats earlier.
On the left, we have measured productivity benefit. In H1 2026, we disclosed operating measures over the preceding 12 months, which showed that our data volumes processed increased 12%, but at the same time, our AI automation populated over 98% of our test classifications on our data asset, which automated, and human effort per million records reduced by 28%. As a result, processing costs per unit reduced by 18%, scale enabled by AI.
On the right, we show how we're developing our scalable intelligent model. Stage 1 is the existing proprietary data asset we have built. Stage 2 is what I've just talked about, the evidence automation classification. Stage 3 and 4 are prospective conceptual opportunities that we're going to explore where we continue to scale.
AI scale will focus on two main things. One is lowering the cost per unit, and we've discussed how that could happen in compute and processing. The second is higher revenue or price per act, which at this stage is a hypothesis we need to validate through product development, through customer survey, willingness to pay, and outcome data. I'll now hand over to Sandra to discuss the last business area, scaling. Thank you.
Thank you, Nick, and next slide, please. This slide shows how our investment in people is supporting a more scalable business. On the left, you can see we continue to invest in the team. This includes investment from a commercial team perspective, particularly in the U.S. Today, we have 13.5% of our people there. Four years ago, that was 3%. So we've made active investment in the commercial area to be close to customer.
And you can see that annual labor cost has actually increased from GBP 12.4 million in 2023 up to GBP 19.4 million last year. Our management planning shows further investment, and you can see there where we expect to end 2026, and what our management planning assumption is for 2027. I think what's really important is actually the slide on the right, and that shows that investment is scaling.
Labor costs represented over half of our revenue, or 50% of our revenue in 2025. Again, the planning view you can see there is a significant drop this year, then planning to drop further next year. Assuming we will reach an improvement of 8.3% over the last couple of years, that is very much our intention.
Really, some of the things you have heard about today helping that, the simpler product portfolio, the more standardized delivery, greater use of automation and AI, the work that Ryan talked about in terms of operating model, and some of the building blocks we have been working on for the past couple of years are really now helping us achieve that revenue growing faster than people costs. We will continue to build capability, expertise, capacity where we need, and where the opportunity justifies it.
The message here is really, it is about disciplined investment. Every additional GBP invested in our people must support that larger revenue base, so we are taking a much stronger business case and return on investment from every GBP we invest. Really, the central message here is that we see scope to still invest for growth, but we continue to see opportunity for cost efficiency and creating that capacity for even stronger margins.
People remain a really strong enabler of our strategy. You have heard from Andrew talking about expertise. It is one of our points of difference. It is something customers really value. But the model is becoming more productive, more repeatable, and better positioned to support a larger business without recreating cost at the same rate. That is everything, I think, to finish off this section. I am going to hand over to Jordan.
Thank you very much, Sandra. I am going to talk about our commercial strategy here, and it is really simple, and hopefully everybody can understand. We have two things that we really focus on in order to grow how we want to grow. One of them is, can we acquire more brands? Can we work with more brands?
Then, can we increase the value that we derive from each of those brands? We do that through this land, expand, and embed methodology. Again, we have talked a lot about Signal today. It is our hero intelligence product. It is the one where we have real actionability and a lot of good ROI conversations with our clients. That is what we lead with, and we land new brands with Signal, shows them that initial insight and real actionability and a good ROI.
Once we do that, once we prove that ROI, and our customers see the value and they see that it is working for them, we then expand that. We add solutions. We expand to different brands within the same client, so we have both upsell and cross-sell there. Ultimately, we are trying to migrate them towards that Relay conversation where we can embed our full suite of services, where it is a larger value that we capture per each brand, deeper relationship, longer relationship, more meaningful relationship.
So that land, expand, and embed allows us across both of those axes of more brands and more value per brands. We have a couple of data points here that kind of show how that has been working for us. Signal is used across 55 brands up to June 2026, and that is up from 36 brands the year before. Again, a large growth there, 65% growth in that Signal use there. In our Relay program, we have two Relay programs that we have talked about up to June 2026, and at their inception, they had GBP 2.6 million of ARR. During that time, that has increased to GBP 4.3 million of ARR.
Not only are we showing that Relay increases the value that we get from customers, it also can expand and has increased over time in our first two Relay programs that we have talked about. Some more data points just to prove that out. If we look at the number of therapeutic brands that we have worked with, that has grown from 56 in 2022, currently from the half year 2026, that is now 103, an 84% growth in that time period. Again, showing that more brands is something that we have activated on.
On the revenue per brand, we have seen that progress over time from around GBP 350,000 in 2022 up to GBP 400,000 in H1 2026. Really important that, because that is a half-year number and this is an annualized revenue per brand view, we do expect that to increase as we go through 2026. Because if you start a subscription-based ARR product with us in March, we do not see the full year until later in that year.
We are expecting that number to increase. These really are two really important growth factors for us that I am going to delve a little bit deeper into each one of them, the more brands and the more value in the brands. Again, that is around the Precision for All and the Relay. I am going to show you some data points around those and how we think about them internally.
Next slide, please, Madeline. Precision for All. Precision for All allows us to expand the opportunity across multiple therapeutic areas. We have talked a lot in the past around oncology and precision medicine, but if we think about Precision for All, it allows us to broaden that out across other diagnostic areas.
Whilst oncology and hematology will stay a core part, we see that in the graph here, if we look at those opportunities, there are many, many other brands in other disease areas where diagnostics is really central. This is where what we call the P4A score comes into play. This is an internal piece of work that we have done to really quantify our relevance and the fit for Diaceutics services to all of the brands that are in development and on market in the U.S.
We have often, over the years, talked to you around our TAM, around 250 brands, increasing to 1,100 brands that we were talking about last year. We have productionalized this process now to give us a way to disciplinely prioritize how do we go after this. This is the Precision for All data that we use here. Every brand therapeutic asset that is out there that is going through clinical trial or on market, we assess using AI. What is its propensity for Precision for All?
How important is diagnostics in the diagnosis of those patients and the treatment decisions for those patients? For each one of these brands, we assign it a P4A score, again, done on a monthly basis with our data science team, all AI-enabled, so it is repeatable and robust and productionalized so that we can use this across our sales engine.
There's five scores, and I'm going to go a little bit more into that in a later slide, but I'm just going to talk about scores five and four here. So this is where it's the highest fit, so the diagnostic or biomarker-led therapy. The data is central to identifying a patient, and that drives the intervention. Score four is a strong fit, and the laboratory data really helps materially improve patient finding, monitors progression, segmentation pathways, who's the decision-maker.
So it plays an influential part, but it's not 100% critical for that diagnostic decision, but really influential in that diagnostic. So our two highest scores here. When we do the analysis, we see there are over 1,600 drugs that we've looked at that have a very high P4A score, that score four and five, 780 of them in score five, 895 in score four.
Of these, just over 500 of them are marketed today. So they're on market, being commercialized by pharmaceutical companies, and then around 1,100 of them are in phase III, which means there are some commercialization GBP going against them, but they're not on market, and there's still a chance of some of those failing and not getting FDA approval.
But this is a mechanism for us to really have a disciplined way to prioritize what do we go after as a sales organization. These opportunities allow us to really point our sales team in the right place and ensure that we're getting the right data in, the right skillsets in, all of those things to address these different areas.
Again, not just oncology and hematology that's been predominantly Diaceutics's business over the last decade, but as we grow out into other areas like infectious diseases, renal disease, cardiometabolic disease, there is a significant amount of opportunity for Diaceutics to grow into that. If we go to the next slide, Madeline, I just want to show some evidence of how we've seen that transition over the last few months.
So in H1 2025, 12% of our revenue was associated with those non-precision medicine brands. So again, that Precision for All brands, as we talk about. When we look at that in H1 2026, that has increased to 22%. So one in five GBP that we recognize in revenue is accounted for by that Precision for All brands. That's a real increase. There's a huge area for us to grow in there.
What we really want to demonstrate with this couple of slides is to show that this is not hypothetical. This is actual growth that we've seen. This is a market that has expanded and we're moving into, and there is real headroom for us to grow further in the Precision for All market. Next slide, please, Madeline. Again, sorry, a really busy slide, but I want to show you kind of the inner workings of how we look at this. So this is the Precision for All TAM, SAM, and SOM waterfall.
This is looking at all of the drugs that are currently in development or on market in the U.S., and we've scored all of them for our Precision for All score, again, one to five here, and you'll see this in our box on the left-hand side of the slide, the number of brands in each one of those. This is the total universe, whereas in the last slide, I was just showing you brands, scores four and five on this. We know that we would be less applicable to not all of those brands. Again, what we've done is we've looked at the revenue potential for Diaceutics in that and by P4A score.
Looking if it's a, again, high-level P4A score number 5, that's the most likely to go to a Relay program, and revenue can be $2 million plus in those situations. If it has a very low P4A score, the relevance of Diaceutics may be smaller, and therefore we would expect less revenue from those brands. We've put GBP 150,000 in here. That allows us to kind of wait to get up to that ultimate TAM that we're looking at, total addressable market.
If we go to the waterfall chart here now, again, starting at that top TAM as we move through, we have risk-adjusted this for phase III clinical trials because not everything does progress through to FDA approval on those things. We've taken that into consideration and removed a proportion that wouldn't come to market.
We then look at what's the serviceable market for Diaceutics and ultimately getting into what is an attainable market for Diaceutics in 2030. When we look at that TAM, SAM, and SOM, we go from $2.6 to 1.2 billion, ultimately to a serviceable, attainable market for Diaceutics in 2030 at GBP 224 million.
I think it's really important that this is planning assumptions. This is not guidance. This is not targets. This is not forecast. This is a model that we're showing around what does that TAM, SAM, and SOM potentially look like, with some assumptions in there around how much of that would be serviceable and attainable by Diaceutics in that future.
Really wanted to show everybody the level of detail that we've gone into showing this and how we're using this methodology internally at Diaceutics to drive a lot of our sales engine as well as we go after these things. To make that real for people, if we go to the next slide, please, Madeline, really want to show two different drugs here that non-oncology based in that Precision for All mindset. First one by Amgen here, looking at cardiovascular disease and targeting Lp(a).
Again, an area where testing is disjointed. It's not ubiquitous out there. Improvements would need to be made if that drug come to market. It's currently in phase III. On our right-hand side, a drug that's actually just been approved in the last week or two here in infectious diseases. Hepatitis here, again, looking at quantitative levels, really important for decision-making.
Two examples where Precision for All is already influencing the industry that we are in and really relevant for Diaceutics to be working with these types of organizations. Next slide, please, Madeline. It is really important that we also say here that Precision for All brands are already buying data and buying and have relatively large commercial budgets, even though they might have a lower drug price in many cases. It is not that we are having to create new budget in our clients.
Our clients are not having to create new budget for this. What we are saying is, look, using the correct data, using the correct insights will allow you to more efficiently use your commercialization spend because they are all buying insurance claims data, they are buying prescription data, they are using campaign measurements in their digital marketing, all of those things.
What we have heard from Andrew and others today is that by actually increasing the relevancy, really understanding that diagnostic intelligence and when is the right time to engage, you can increase the efficiency of sales teams and marketing spend on here. This is around how do we make their spend more effective for them rather than trying to increase budgets in these things. If we can go to the next slide, please.
Ryan talked about these earlier, this clinical intent. Again, two examples just to show that again. When we look at HbA1c and when we look at claims data, health insurance claims data, when we see patients that are diabetes type 2, we actually see in laboratory data that many patients that could be diagnosed as diabetic, we do not see them in claims data. There are blind spots.
We do not see that whole picture in claims, and that is what pharmaceutical companies are predominantly using. Again, being able to provide more intelligence on patients and physicians will enable those pharmaceutical companies to market and commercialize their drugs more efficiently. The same in the rare disease here.
When you look at just an insurance claim-based targeting list, this is the deciling Ryan talked about, what we see is that only 15% of the most highest ranked physicians are in those top deciles. When you go a clinically informed list, so when we use multimodal data and we use diagnostic intelligence, we can actually see the deciling becomes much more accurate. So you can point your resourcing at where it is going to have the largest impact for you. Just a couple of examples there of where Reveal data can really support better commercialization spend. Next slide, please.
Again, just reiterating this point, this is about precision at scale. Lower price medicines still have large commercial budgets. They still have sales teams. They still spend on marketing. It is about increasing the relevance so that we can make those budgets more efficiently being used on those things. Next slide, please. Penultimate slide from me here. Just really going from the other side.
We talked about more brands there, Precision for All, and the relevance of Diaceutics products. When we then think about, well, how do we take them from Signal to strategic partnership? So how do we go through that, expand, and then ultimately embed? What we have shown in 2026 is the ability to start at Signal, prove value, add additional services and engagement solutions with our clients, and ultimately getting to those Relay programs.
The four Relay programs that we are currently working on have an estimated ARR of $11 to 12 million. We are proving out that we can go from two Relays to four Relays, and we can increase the size of those Relay programs, really increasing the amount of revenue that we can derive from those longer Relay relationships with our clients.
In those last few slides, I have hoped to articulate how we increase the number of brands we work with and how we increase the revenue per brand that we can gain from those. My last slide, if that is okay. Just to bring this back to the patient level, which I think is really important, that human impact. We have talked a lot about numbers and revenue and ARR. I think it is always nice for us to end on what does that mean for patients.
Again, once we do all of that work through what is the TAM, SAM, and SOM, what does that mean to patient lives? Ultimately, Diaceutics can impact one in 10 American lives with our Precision for All strategy and really bring this back to there is a nice correlation between Diaceutics' impact and patient lives. On that note, I am going to hand over to Susanne to talk about some of our work outside of pharmaceutical companies.
Thanks so much, Jordan. Ryan alluded to initially that we have a lot to cover, so we are a little bit behind, so do apologize if I rush through the next couple of slides just to allow a bit of Q&A at the end. I did want to take a few minutes talking about how we see the next dimension of growth for Diaceutics. Precision for All, which we have addressed several times today, give us really two pillars of growth.
Jordan already addressed now how we are continuing to expand our core pharma and biotech business. The second strategic approach to expansion is what we are calling growth ventures, and that is focused on selectively opening for new customer segments and buyer types beyond the traditional commercial pharma model that we have been operating in. It is a key point here that we are not leading with diversification.
We are taking a very disciplined approach to growth, looking at where I am sorry, can you still hear me? Yes. Okay. Got a message that internet was dropping. Sorry, I was saying that it is key here that we are taking a very disciplined approach to growth, looking at where the diagnostic intelligence we have already built can solve problem in additional industry and client segments.
The growth model is built around some core principles. First, it is taking a capital-light approach. We are reusing data and capabilities before looking at sourcing or building anything new. Going to prioritize adjacent markets, come back to in a second what that means. We are looking at where do we see a clear unmet buyer pain, where is there a tested willingness to pay.
And then finally, we are also very conscious to protect our core business, putting in place tight data use and channel guardrails to avoid any cannibalization of current business. It is really leveraging what we already have created to build a growth engine without compromising what we have. If we move to the next slide, you will see here how we are taking a phased approach to market expansion, prioritizing initially four adjacent markets, representing an attractive new market for us.
CROs, Clinical Research Organization and Patient Recruitment, is our highest confidence opportunity. CROs have a very costly challenge around finding the right patients and sites, and it is something that our current data can directly address. It is a healthy market, but probably more important, it receives strong endorsement in the market research that we did, and I will come back to that shortly. We are also looking at consulting firms.
They do represent a smaller revenue potential, but offers a relatively low friction route to monetizing our intelligence, fast followed by diagnostic provider and larger labs. There is a bit more investigation there to understand how we unlock that market, but play a high-value market for us. And then finally on this slide, I have addressed also payers.
That is a very attractive market, but it will require a bit more for us to unlock with longer buying cycles. They have higher governance and integration requirements, so it is a bit of a longer-term play. The principle here is not just looking at market size alone. We start with where there is a clear pain, where we have a right to win, and where the route to revenue is shortest.
If you go to the next slide, and we just look at the CRO market, as I talked about, we did not want this strategy to be built around what we think customers need. We wanted an independent validation from buyers, and the CRO research has been particularly compelling coming out of that. Firstly, the problem is real. Around 60% of trials extend on enrollment, with trial delays potentially representing millions of dollars in both additional cost, but also missed revenue for the trial sponsors.
When we presented, or when the voice of customers was presented with the Diaceutics data intelligence, they did not find that there was any existing alternative in the market, making it, again, a very attractive starting point for us. It also identified that CROs have already allocated budgets, both for the general market intel, but also budgets per patient identification.
We are not trying to create a completely new budget category, but tapping into what is already there. The small and mid-sized oncology and specialty CROs, it represents the fastest way for us to enter, and we will, in parallel, be addressing the larger CRO where there is an enterprise structure that we can tap into. Proving the feasibility of new customer segments is really fundamental to how we built the playbook to apply for growth strategy.
And if we move to the next slide, we intentionally designed an operating model that is different from our established business. As I have already mentioned, we prioritize selling what we have to secure additional value with little investment, including standing up initially only a very lean organizational structure.
We've created a test and learn model, so we can run pilots and test hypotheses before we decide whether it's the right time to scale, whether we need further refinement, or whether we even stop and just not progress further into that segment. The goal is to create a decision quality evidence framework that we can run in weeks and months, not in years, to tap into that additional value.
I talked a bit earlier about the need to put in place relevant guardrails around our data use terms to avoid resale and channel conflict, and that also plays into the playbook that we are setting up. The roadmap is deliberately built with a phased and additive approach. Our current focus is on building the foundation, standing up the function, and at the same time, targeting those near-term and low-hanging fruits.
We already are underway with pilots. We have promising deal discussions ongoing and are starting to fill a healthy pipeline. Small, mid-sized CROs is what will allow us to build proof points and awareness in that segment that will help us scale, and it will help us also go to the larger CRO, where we see the potential for broader enterprise deals to be made.
With a proven growth model and playbook in place, the ambition is to then activate the next segment, and in a couple of years, become a generally multi-segment growth function. Right now, the focus is on organic growth, considering the assets we already have. Longer term, we might be looking at inorganic opportunities if that's going to help us accelerate the overall strategy.
I will wrap it up here, just leaving with that the growth venture is not a bet on a distant future opportunity. It's a disciplined way to monetize more of the value that we have already created. We should have a few minutes for Q&A, Simon. Ryan, did you want to wrap us up?
No, let's go to Q&A. Okay, next slide, Madeline.
Okay.
Yeah.
Please raise your hand if you wish to ask a question. We'll endeavor to get to as many as we can today, but given the limited time we have left, I would ask that our covering analysts please restrict yourself to one question. I mean that, guys. We'll now take our first question from Chris Glasper at Singer Capital Markets. Chris, go ahead.
Yeah, thanks, guys. Yeah, just a question really on new product strategy. How are you going to monetize the lab network, which you've talked about previously, and maybe what gaps in the portfolio Chris has identified following his joining the company? Thanks.
Yeah, maybe I'll take that. Bit unfair to pass it to Chris, just now. Thanks for the question, Chris Glasper. Ultimately, the purpose of us building out the product function, as I mentioned, we haven't invested heavily in product engineering, product mindset, product marketing in the business to now. A lot of what we've built has come from innovation and kind of founder-type invention.
We want to be very much customer-led going forward, so a lot of that future direction of where we bring the product is going to be driven by customer feedback, and ultimately where the industry's going. We have a roadmap, you'll see that in due course, that is being implemented and ultimately augmented with some of that customer feedback. More to come there, I would say, Chris, is the honest answer. But it's certainly going to be more customer-driven than we've had in the past.
We'll take our next question from Christian Glennie at Stifel.
Thanks, guys. I guess, yeah, the one question would be, I guess just interested in the-
I lost you there, Christian. Did you go mute?
Christian, go ahead.
Yeah. Can you hear me now?
Yeah, start again. Sorry.
Sorry. Yeah, just interested, the question would be on the proposal that you go exclusive with some labs, and that's a proposition. Just a bit of a sense for where you're at with those proposals, the sort of, some of the discussions that may have had, and ultimately to clarify, you seem to be saying there was some economic impact or sort of reason why a lab
Yeah
would sign up to that exclusivity, but it wouldn't cost you overall more. Just a bit more for that, around that proposal and where you're at with it, and when those
Yeah
that exclusivity
Maybe I will kick that one as well, just for brevity. I think, Christian, there are a couple of core pieces of that. One is we have already achieved this with some of our partners, so it is proven. Remember, the core to this is that our engagement with lab partners is a value exchange. So when I refer to more favorable commercial terms for the lab, that is not necessarily a higher level of expenditure for us.
It does manifest in that we are giving them more service. So, some of the labs that we have worked with in this way are using our Physician Engage tools, so it is allowing them to do more with that. Now, there is a cost to us in terms of the cost to stand up that service, but it is not a direct monetary cost for paying for exclusivity per se.
And obviously, the reason we are doing it is, continue to build a more defensible moat on the business, but also to be able to talk to that dataset as one that is truly exclusive to us. We do not intend to get it all exclusive. We just need to get a meaningful proportion of it to be exclusive to Diaceutics, and that means that our customers can come here for that one-stop shop that Andrew referred to earlier in the fireside chat.
We will take our next question from Julie Simmonds of Panmure Liberum.
Thank you very much. Just wondering about the customer pricing. How much of the expectation of that buildup is already within your sort of current thinking, and how much of that is aspirational?
Ryan, do you want to take that one? Thanks for the question, Julie. There is opportunity there, as we showed on the slide, and that has been validated by a third party. Think about it within the context of it helps to de-risk our growth objective, both in terms of ARR and revenue over the coming years. We expect to grow in terms of the number of brands, the number of customers, and Jordan was talking about that.
But the other metric is the revenue per brand. That could be in additional products, and we have got a clear path to upsell, especially around things like Relay, where we can move from a Signal to Physician Engage and Expert Exchange.
But to help, if you will, augment that growth driver, to help de-risk it and our, you know, the guidances in the market is we have got this pricing increase opportunity, which largely to date we have not been able to exploit, and through product and marketing, we will be better able to action that.
Okay, thank you.
Thanks.
Great. Ryan, I think we are done there. Okay, thank you, everybody, for joining us. Apologies for many questions that we did not get to. There were quite a few, but please follow up with us at investorrelations@diaceutics.com or directly if you have our emails, and we look forward to seeing you again soon. Thank you.
Thank you very much. Thank you.