Data rooms are central in biotech: they not only underpin key events like fundraising, asset licensing, and M&A, but also actively inform R&D roadmap decisions. BD partnerships, pharma collaborations, asset sales, clinical prep, board updates, financing events – all depend on stakeholders having a reliable window into your data, with access to the latest information on your assets.
Getting your proverbial ducks in a row today means you are ready to pivot on a moment's notice. And in a fast-moving, uncertain market, that agility can become a path to survival, or even a strategic advantage.
In this webinar, co-hosted by Kaleidoscope Bio, we discuss:
View the full session or read the transcript below!
Kevin Tate: Hey everyone. I see everybody's jumping into the webinar. We're gonna give it just another couple of minutes as folks are still joining. So hang tight and we'll get started in just a minute.
Alright, let's do this thing. Hey everybody. I see some familiar names on the list. Thank you for joining. For those that we haven't met, welcome. I'm Kevin from Kivo. I'll be our MC today, and thanks for joining today's webinar with Kivo and Kaleidoscope on the importance and best practices for data rooms.
Excited to turn it over to Toban and Bogdan here in a minute. First, a bit of housekeeping. We are recording the session and we'll share that afterwards with all the attendees and registrants. Know that will be coming your way. Also please do light us up with questions.
You can use the Q&A or you can use the chat. I'll be monitoring both and we should have plenty of time for questions at the end. Also, if things come up along the way, that's part of why I'm here. I can jump in and ask questions about what Toban and Bogdan are talking about. So please don't be shy on the Q&A.
With that, a little context. I know some of you may know one company, but not both. So today I'm excited to have Bogdan Knezevic from Kaleidoscope. His team gives pre preclinical teams clarity to navigate their R&D data. And then we've got Toban from Kivo. Kivo helps sponsors and service providers to power their drug development cycles.
What's interesting about this topic is that both companies are working with sponsor teams and service provider teams who are creating key data at different parts in the drug development process. But that all needs to be put together in the context of something like a data room, so Toban and Bogdan talking together about those different parts of the process and then showing how all that works.
So without further ado, I'm gonna turn it over to you, Toban.
Toban Zolman: Thanks Kevin. We're just gonna briefly explain what is a data room so that we're all operating on the same footing, look at why they're important and what's really unique about data rooms and drug development. 'cause there's a lot of, nuances here where traditional data rooms for like legal and financial transactions don't really make sense all the time in drug development. We'll unpack some best practices on managing data rooms and then we've actually set up a sample data room, so that you can see how that would look for someone going through diligence and how you would navigate between documents and data between both Kivo and Kaleidoscope. So that's where we're headed.
Let's dive in. I've got just a couple of slides I'm gonna show here. And then we're gonna keep this fairly informal, Bogdan and I will go back and forth and talk through some of these concepts.
I think what's been interesting especially for me is when we created Kivo initially, if you told me we would be used as a data room, I wouldn't have seen that as a core use case. But increasingly our customers leverage us more and more for that. And we'll talk through what that looks like, what it feels like, and share those perspectives on both the the science and the documents that science yields.
So first, let's just dig in on what a data room actually is. I don't think there's a ton of mystery here, but I just wanna make sure we're all on the same page, that really what we're talking about here is a centralized repository used to store and share confidential information. Typically these get used through during a business transaction. These data rooms allow authorized users to access sensitive data and intellectual property, financial records, scientific data. And do that in a way that's trackable, compliant and essentially ensures that all of the information needed in a transaction is accessible.
Why is this a priority? It turns out that these sorts of activities are incredibly common during drug development and we'll unpack that a bit in a second. In today's climate, this is actually more important than ever. We're seeing across our customer base drug development companies struggling to raise money, having funding from NIH maybe get into unknown situations. So having a data room that is ready to go and can actually accelerate fundraising, diligence, licensing, partnerships, all of those sorts of activities is super critical. This really spans the entire drug discovery and drug development lifecycle, and there's actually a lot of times where you need a data room through that process.
We actually consolidated a list of the sorts of activities that drive this sort of diligence. It's pretty wide.
You've got fundraising seed round series A, B, C, D, E, whatever it takes through that process. Virtually every company has a couple of bridge rounds or convertible notes on average in industry. There's one to two of those from the time you go from a seed round to some sort of liquidity event. An IPO, an acquisition, et cetera. There's licensing options. There's inbound asset or in-licensing IPOs, M&A, all sorts of facility inspections, CRO inspection, quality audits. There's a lot. And so ultimately what that means is in the five to seven years, it typically takes to go from a seed round to a phase two study, there is on average 10 to 18 diligence events. Which means you're going to be going through this process two to three times a year. And I think what we have seen with our customers is what often starts with a panicked effort for an early, pre- IND stage company, over time matures into a really a whole set of machinery within a drug development company to ensure that all of the documents and data that are needed for this are always in place and ready to go.
So that's really what we wanna focus on in this presentation today, is talk through with that as the backdrop that you're going to do this two to three times a year. It, you need to be able to do it increasingly faster and more efficient. How do you actually go about setting yourself up for success?
Bogdan Knezevic: Just double click on there. From our perspective I think teams are often surprised that in, in other non-bio contexts, a data room is just a tool to give access to people. But in bio it's often the thing that gives the person on the other side an image of, is this a company I want to work with? Is this science that I believe in? It's really like a chance to put your best foot forward and highlight just how compelling your data is and how tight of a ship you're running, which, like you said, in the kind of markets we have today where you need to be able to opportunistically take advantage of this stuff as a biotech, it becomes that much more important to just have everything in place to accelerate that process whenever possible.
Toban Zolman: Yeah, a hundred percent.
When we look at what goes into a data room, oftentimes we get questions from customers, like from the CFO as an example on, "Hey, we need to set up a data room. How do we get data out of Kivo to put into some traditional SaaS based data room system?" And what they're typically focused on are things like financial stuff, cap tables, balance sheets, legal stuff around IPs around IP patents, licensing, basically things that demonstrate the freedom to operate. But typically what happens is they realize very quickly that this is really the tip of the iceberg. And to Bogdan's point, really what differentiates a company in the drug discovery drug development realm is the actual science. And that's not going to get thrown into a glorified Dropbox account. You need to actually give investors or the folks doing diligence access to a richer set of data.
Typically that looks like commercial information from Reg Affairs on the market and competitive landscape. Often for companies that are deeper in the process, it may mean literally exposing submissions so that they can understand what's gone to the FDA. What nonclinical and clinical studies show, and then all of the underlying scientific data, which Bogdan can talk about how that gets manifested in Kaleidoscope.
Bogdan Knezevic:Yeah, a lot of that is common, not just in investor diligence, but like pharma partnerships, co-development, any kind of asset licensing where you have the BD and finance teams digging into the first few points that, that Toban has up here. But then you also have like pretty competent scientific experts digging into the underlying data to understand not just what data are you saying you have, but what was the context of that data? When was it generated? Under what conditions? What are the constructs in question and what combinations and permutations, all of that becomes this massive web of things that you have to be able to point to quickly to give someone a convincing reason to believe that you're worth the partnership or the investment. That's where we see teams who aren't proactive about this really scramble. And again, everyone knows this, that like momentum is one of the biggest determinant factors of whether deals can get done. You actually want to be able to lean into that and have very clean and concise ways to pivot and index your data so that when it's being scrutinized it's clear to the other person what you've done and why.
Toban Zolman:That makes a ton of sense and I think really aligns to what we see when we work with customers in this space. Which really leads us into fundamentals of how to set up and manage a data room.
And I guess I would add a short preamble to this, which is, you know when Bogdan and I first started talking about how both of our companies were supporting customers through these sorts of diligence activities. What was really interesting to us was how much pattern recognition there was around what the drivers were and what the needs were of customers.
But ultimately, how that data needed to be consumed was very different if it was a cap table spreadsheet versus experimental results in the lab. And so what we wanted to do here is really unpack how you go about building a data room and then how you surface both the documents and the data in that in a really hyper efficient way.
So let's dig in. I think the first fundamental that we wanted to hit on is that you build it on day one and then you have to keep it alive. And this goes back to how many diligence events you're really going to be managing, where you don't do this, walk away and worry about it two years later.
If you assume that diligence typically takes, a quarter, maybe two in many cases, especially around fundraising... by the time you wrap that up, you're literally starting your next event. You could take 90+ days to close a financing round and then 90+ days to do a licensing agreement. This is really a constant process that requires care and feeding at all times. And this is super important on the data side, which I'll let Bogdan speak to because it's constantly changing.
Bogdan Knezevic:Yeah, it's constantly changing and you're involving so many different teams. So you have your medicinal chemist, you have your bio team running assays. You have the CROs that are sending you results. And really what we focus on at Kaleidoscope is how to let users define like the key things they need that will inform that that deal and then appropriately route new data so that at any given point in time have the latest on whatever experiments you've run at your fingertips. And then be able to present it in clear and concise ways.
Toban Zolman:What we've seen as well within Kivo is we've literally, and we'll talk about this as we go, but literally built user roles specifically for diligence, which lets them see, as an example, only approved versions of documents. And the reason we set it up that way was some level, of course you just want them to see approved documents, but the secondary reason is a lot of the documents that you'll be giving them access to, you'll be revving and approving updates to those literally while diligence is going on. And so it gives a way to automatically surface that new information to them as soon as it's ready. So super critical on that side.
The second thing that, that we've really seen as a common theme is a single canonical table of contents. And this isn't necessarily that Kivo or Kaleidoscope literally has a table of contents. But what investors or auditors really anyone in the diligence process needs to understand is at a glance where everything is, how to navigate it and what the source of truth is. And so in Kivo, we do that through a visual hierarchy in our document management system, and then I'll show how we have the ability to punch out to content that exists both in other systems of record like Kaleidoscope, as well as other areas in Kivo so that you can manage just the data room and then pull assets in from other areas.
Bogdan Knezevic: This matters a lot because at the end of the day, it's humans that you're interacting with that are making these decisions on whether they're to purchase or sign a deal or invest. We also, similarly to you, Toban. I had this realization that you need to give people ways to jump between high level and in-the-weeds information. Because if it's too overwhelming from the get go, you lose the plot. The way we approach this on our end is giving people the ability to create very high-level dashboards, like pass fail, red, yellow, green across, your multi-year journey.
And then for each of those, be able to click it and see what's the data driving those pass fail and then, what was the context behind that data? So we do that propagation and indexing. But yeah, like you said it's all in service of communicate your core message as quickly as possible to get the team rally and excited in the right ways.
Toban Zolman: Granular permissions is highly important. Both Kivo and Kaleidoscope have this out of the box. But the fundamental piece of this, I would say is you're really controlling a narrative through this process. And in the olden days and I can literally remember doing this...You set up a conference room that's comfortable, but not too comfortable, and you bring the folks into that and you literally retrieve a document at a time so that you're not exposing too much information and you're managing binders. Lord, all the stuff that, that those of us that, that have gray in our hair remember... the challenge that we've seen with electronic systems is a lot of them treat data rooms as a shotgun approach. Where and we've seen this at Kivo and past companies where we've raised venture funds and you use a traditional finance-centric data room where it's just here's everything that you need. Cap table, finances, everything. The investor goes through and analyzes everything and off you go.
With drug development, there's some storytelling involved and there's positioning of the underlying data. What we learned that our customers needed = the ability to take disparate documents and data that exist throughout Kivo and other systems, surface those in an easily digestible way, and then be able to essentially light up content very quickly as it's requested without having to just shotgun everything out in one shot. I'll show you how that works in Kivo when we get to that part of it. I've got essentially a prebuilt data room, and we can talk about how you surface content into that, but it really reduces the friction and lets you split the difference, so to speak, between the old school, "put 'em in a conference room and bring them a document at a time" and a lot of these data room platforms that just shove everything out in one shot. And Kaleidoscope handles that in a whole host of ways. I'll let Bogdan address how that works there.
Bogdan Knezevic: From our end, because we're dealing with the actual data that's behind all the IP it became immediately obvious to us that what biotechs really need is peace of mind that they haven't given someone the wrong access to the wrong thing. So we have a permissioning system whereby you can add guest reviewers and control with pretty high precision what they see. Whether you want to constrain it to a specific project, whether you wanna go to a much more granular level and say they should only be able to see this kind of data on these compounds, and I wanna hide these fields from them... we have that flexibility in place so that as you're working with this growing web of nodes, whether they're pharma partners or CROs or other biotechs or investors you only give people access to the context that they need and nothing more.
Toban Zolman: Along those lines, Kivo's baked two concepts into what we surface. One is redaction, where you can redact documents directly in Kivo and then just choose that redaction as what the user sees. A critical piece to all of this, I think, is to really ensure what, what gets surfaced. Final thing on that is we also let you control blinded and unblinded data and essentially ensure if the person doing diligence should see one or the other and control all of that as it goes through.
Bogdan Knezevic: Scary to think how teams do this without these kinds of tools. I've seen some really wacky stuff like email attachments. I've seen some people use social media platforms and private messaging there to send. And when you realize like this is like... all that you worked for five to 10 years, you definitely should not be doing things this way. So it, it sounds obvious to us on this end, but I think when people aren't aware that there are tools out there, they revert to these highly inefficient and risky ways of sharing information.
Toban Zolman: Yeah, a hundred percent. We've seen some sketchy stuff go down on this front as well.
The fourth fundamental that manifests itself, I think on both of our platforms and in interesting ways, is really searchability and metadata. I was reflecting the other day that, Kivo's been in market, what, over three years? The only time we've ever had bugs reported on search is during inspections or diligence because it gets used so heavily because those users don't know where stuff is. They're relying on search to try and find stuff. Having really effective search and document classification using metadata, which of course ties into search on the document side, makes that super critical to A, let them find everything quickly, and B, help them understand that you actually have everything and just 'cause they can't find it, assume that it doesn't exist. So that's super important on the document side and on the data side is as well.
Bogdan Knezevic: Very similar on the data side. I think how we see this manifest is different people, different teams are gonna want to dig into different types of pivots or permutations of your data. And how we handle that on our end is by doing all of the relational mapping and indexing across lineage, across context, all the caching. So that when you do set those searches up or when you create a saved view it can load really quickly. You can navigate through point and click to jump between contexts. Everything you're saying Toban about like how do you make it easier for someone to navigate and find what they need because they're gonna be changing their mind or asking you questions as they go along. So just equipping them well for that process.
Toban Zolman: Enterprise grade security, also super critical. Some of this is optics, some of this is compliance and some of this is practicality. On the optics side speaking of sketchy processes that we've seen, sending a Dropbox link with all of your company's IP to an investor probably doesn't demonstrate the right optics around how you're going to run the company that they're considering investing in.
In terms of practicality Kivo and Kaleidoscope both support single sign-on as an example, which opens up the possibility for a single account to authenticate into both systems, seamlessly jump between them, and then to manage those accounts through a centralized way. So if you're looking at a partnership with a big pharma company, it offers opportunities on how you're going to authenticate them potentially even enabling them to authenticate in if there's going to be a longstanding partnership there, using their own credentials, things of that sort. And so really having a foundational level around security makes all of this go much better.
Final thing I would say is when you are using systems like this that are Part-11 compliant and have strong audit trail capabilities. Our customers are able to literally run reports on users and understand what assets have they looked at if you've given them the ability to download, which isn't something we support in by default during diligence activities, but have they downloaded anything. All of that stuff is super critical, both to manage access and then just have context for if this partner or investor is really serious, are they actually looking at the documents and data? So all of that we found is super clutch.
Bogdan Knezevic: Table stakes is my view on this. You have to have this so not much to add here to, to what you just shared.
Toban Zolman: Final thing that I think we've both identified is really having a clear owner and update cadence to this content. So this is less infrastructure and more process, but what we found is that there needs to be clear owners of who is managing this content over time so that it doesn't rot on the vine, so to speak, and you're not in a fire drill mode when comes time to surface this information.
Typically what we see that look like is the CFO or finance owns the sort of corporate docs, and those often live in Kivo or get pulled in quarterly. If the company's venture backed or private equity backed, most likely there's an investor pack that gets produced quarterly. That gets pulled into Kivo. And then Reg Affairs often owns consolidating everything else. That doesn't mean that they're in charge of things like preclinical data or clinical data, but they're at least consolidating that information into program repositories in the data room, delegating tasks out to folks in clinical and nonclinical to drive getting that content in there.
Bogdan Knezevic: You want your tools to be in service of the job that a person needs to do. A lot of what we've done at Kaleidoscope and what you've done at a Kivo is put yourself in the shoes of what's the journey of this person? What are the questions they're trying to answer? And if we know that they're gonna be the ones responsible for it, we just need to make sure that they're well equipped to find what they need, when they need it. So that's, I think, been core to both of our ethos and how we built our tools.
Toban Zolman: So those are the fundamentals that we often look at when it comes to building and maintaining a data room. I think some, some conceptual things to focus on is first really be deal ready, not deal rushed. In other words, start building this and we had pre-series A, I think realistically you start this at the seed round you may not have a system like Kivo yet. I think we are increasingly seeing earlier stage companies leverage Kivo as they transition from drug discovery into drug development, and they leverage us in that fundraising. And frankly, we are increasingly seeing investors who have portfolio companies that use Kivo, push other portfolio companies or even companies that they are evaluating investments in to have Kivo to really facilitate that because they have become used to easy access to the information know how to navigate around it, and that hygiene around, surfacing that information early is really critical and really becomes reusable infrastructure.
Bogdan Knezevic: I think when you and I were chatting about this be before we let people in, the analogy that comes to mind for me is, if you were going for a job interview, you wouldn't wake up in the morning and then go and figure out what clothes you need to buy to get ready. You'd have everything ready in advance. And so I think it's only more on the line here when you're trying to commercialize a therapy and get it to patients who need it. We're seeing a similar push from, the most recent example is like from a pharma partner that just was really impressed when the biotech that we work with had their data presented in Kaleidoscope dashboards. Because for them it was a clear sign that they were taking this seriously and that they had a clear goal and milestones in mind, which only made them want to work with them even more. Yeah, completely echo this.
Toban Zolman: It's funny. I think as much as we talk about the logistics or practicality of how you do this and the technical pieces and the features that enable it, if you zoom back, so much of this is really just about the optics and how you're presenting and positioning yourself, your company the leadership team, and that there's almost more value in that than the actual features and practicality of the whole thing. So it's it's an interesting side effect I guess of doing it the right way. Often demonstrating that you in fact know how to do it the right way matters a lot.
Let's talk real quick about auditability as a feature. This is an area that I think we just continue to lean into at Kivo. And that is creating richer and richer permissions around how we expose data and control user access around limiting downloads, limiting printing, we literally have a secure viewer where you can't even copy text out of what you're previewing. It doesn't cache it locally. It streams a page at a time down to ensure that there's really no way to sniff that IP and pull it off or pull it out of a local cache.
Then we layer audit trails on all of that. And as I said earlier, we've seen customers come back to us and say that it's really helped them gauge the interest of the partner, the investor. Of course you're maintaining compliance through all of this, but there's a lot of intangible benefits to how all of this fits together.
Bogdan Knezevic: For us, I think a complimentary thing here is for internal use as well. So when you're dealing with your actual data it's really important to know what are all previous versions of that and who did them and why? So we similarly stamp and log everything so you can see how value has changed over time, which is a strong indicator of are you getting better at the commercialization process over time.
Regardless of whether it's an action someone takes in Kaleidoscope or they upload new data to Kaleidoscope, or we sync with another tool if any value changes we stamp it and can present people with a log of that.
Toban Zolman: Final takeaways. This just really reiterates the themes we've been focused on. An organized data room is a leadership signal on how the company operates. And is frankly going to spend someone's money, whether it's a licensing deal or an investment. It accelerates diligence, which is super critical to have that move quickly these days. Having all of your content be dynamic permissioned and auditable really increases investor confidence. Bogdan, anything to add on this before we jump in?
Bogdan Knezevic: Yeah. Jump into it.
Toban Zolman: Final thing I'll show here is just how Kivo and Kaleidoscope function together here. Really Kaleidoscope is managing a lot of the data. This data still needs to be accessible much deeper into the process. But really all of the core data from the preclinical phase, drug discovery phase through IND is gonna be accessible in Kaleidoscope. Kivo is essentially the entry point into this and is going to expose all of the documents and data or all of the documents related to the various programs, finances, as well as regulatory activities.
I'm just gonna switch over to Kivo here. I'm gonna show part of this in Kivo. Bogdan's gonna show part of this in in Kaleidoscope.
I've landed here on a data room cabinet that we've set up inside of corporate. I'm logged in as a user with really restricted permissions so I can see individual documents. This is an overview of Zoman Pharma start here. A lot of our customers build out slide decks. I just have kind of a placeholder here, but that literally orient the person who is doing diligence to what they're seeing.
I didn't turn it on in this environment, but we also have customers who will literally create a video that shows someone how they're going to navigate Kivo and Kaleidoscope where everything lives. And then add that as a training course in Kivo where when the person doing diligence lands in Kivo the first time, the first thing they have to do is go through that training and then that unlocks access to all of the content. So there's a lot of ways to really structure this with features that facilitate understanding how to use and navigate the system.
But you can see this is a very simple data room setup. We have slide decks around the company and program details. We have all of the legal information broken out around HR and IP other agreements. All of the finance information with cap tables and 409A valuations. And then within diligence we can break all of this out with FAQs, pipelines per asset. So if I take a look here at Artemis you can see how this can manifest itself in Kivo, where we've got a preclinical program overview. You can see this links directly into Kaleidoscope. So Bogdan will show that the other half of that on his side in just a second. We have the ability in Kivo to take a document that exists in some other location. So this one exists in a regulatory cabinet and surface it into the data room, which means you're not downloading a bunch of documents and re-uploading them, you're literally just creating a pointer to it. The user doing diligence doesn't have to have access to regulatory if you don't want them to. They just need access to where this alias is what we call it is at. And so it's a really easy way to kind of surface that data exactly where you need it.
And then finally, we have the ability to link to other content. An example of that would be something like an actual IND. So if I log in here, this is my actual ECTD submission. This gets used a lot for situations where you're doing licensing with another pharma company and they want to see your entire IND, so they understand the scope of the NDA if they're going to take over stuff in the US or for EMA or whatever it is. And so all of this gets pulled in here. They can see all sequences and navigate the full ECTD. So in a nutshell, that's really what most of our customers are doing within Kivo is surfacing all of the right content from the correct context directly into a single data room. Give access to just that data room, and then you're good to go.
So I'll stop sharing. Let Bogdan fire his up and show what the world looks like on the data side. Just keep in mind that the typical flow here would be they get access directly to Kivo, and then we punch deep links out that take you straight to asset and program dashboards in Kaleidoscope.
Bogdan Knezevic: Exactly. So I'll pull up one of those here. The same programs you just saw Toban talk about in Kivo you could see in the sidebar here in Kaleidoscope. So this is one of the dashboard we support, which is your main asset and the critical data you've collected on it. The really powerful thing with a tool like ours is that we maintain awareness of all versions of an experiment you might have run. So here we can tell you, hey, there are five values because you've run it in different cell lines at different doses. What do you want to do? Do you want to pick a representative value and have that pinned? Do you want to calculate some kind of permutation of it? You have the flexibility to choose that.
The other really, cool thing here is that we preserve all the data you've ever generated on that compound. So you can also within a single click see the different parameters all the different experiments you've run all the different relationships, in different tissues or at different doses. All of that's preserved and captured in Kaleidoscope and it's relationally propagated throughout the app. This is like a target product profile. But you might also want to look at data in this very zoomed out view. So this is an example of something that our customers will often share restricted access with their pharma partners so that they can show them, Hey, this is where we are in key milestones before we're ready to go to clinic.
And again, any data, because these could be configured to be customized to your science, any data that you generate against these milestones that you've defined will be pulled into one spot. And so you'll be able to, at your fingertips, navigate all of that information across all the sources that you might have. And then where to Toban's point, how this connects back to now a system like Kivo is the fact that you can, within Kaleidoscope, open, let's say your project page see the different phases of work you have, you can navigate to more of a list view or switch between that and let's say a Gantt that can be expanded, and each of these now points to experiments you've run. So you're really centralizing critical aspects about your R&D and where you are in that process in one spot. And then capturing the metadata that you might need for these different initiatives.
So here I have a link out to the Kivo environment, which will then take you back over to what Toban just shared. So now you have something that points to where all the critical documents are going to be stored. The goal, I think with, what Kivo and Kaleidoscope set out to do and what using both of the systems that at the same time actually lets you do is create this like very interactive ecosystem of all of the pieces you need to commercialize your science because at the end of the day, the whole purpose of biotech is the commercializing of your IP. One of the worst things that can happen is if you fail as a biotech -and failure's inevitable in many cases- but if you fail, you don't want it to be because you weren't ready or you made a mistake or you slipped up somewhere that you shouldn't have. You want to control everything you can. And that I think really begins with proper hygiene around data and how you tell your story and how you organize all of the information you've accrued over 10 years of R&D.
Toban Zolman: Agree with all of that. That brings us to the tail end of what we had planned. Kevin, I saw a few things bounce up on my screen as folks were submitting questions, so I'll let you MC.
Kevin Tate: Yeah, that was great. It was great to see those things working together.
A couple of questions for the brain trust here. One is, as you mentioned at the beginning, you could have a lot of these over the course of a company and a life cycle. But it's hard to predict when it's gonna happen. So I guess how, what's the right amount of ready to be? How do you think about if it happens, we'll be ready?
Bogdan Knezevic: Yeah. Go ahead.
Toban Zolman: You start, I'll tack on.
Bogdan Knezevic: I think on the data piece, it's very much you want to be ready as soon as possible because the data's only gonna get more complex. And it's the saying like, best time to plant a tree was 20 years ago. Second best time is today. I think that really applies here. The sooner you do it, the less lift it is and then the marginal cost to maintain that over time is pretty low when you have a system, like in our case, Kaleidoscope, but Toban, I don't know how you think of it from the document side.
Toban Zolman: A lot of folks on this call may have a better context of TMFs than they do of data rooms. And with the TMF, you know all the content you're eventually going to need. And so you, when you start building your TMF, you create placeholders for all of your expected content and can even define when you think you'll have that. We recommend the same thing for a data room, frankly, where you may not have results from a particular experiment yet, or you may not have an annual financial statement yet. But literally just keep layering in all of the stuff that you know you'll need so that when you get an investor meeting, get a licensing call, you essentially have a living table of contents. Even if you don't have everything, you know everything that you're ultimately going to need to collect, and it's a lot easier to do that if you're organically building that proactively.
Bogdan Knezevic: I love that because I'm glad you said that because that same thing manifests on our end where teams can schedule out their program and then go through the exercise of what are the key studies that get us that data? Even if they don't have it right away, and then over time they can see where are they checking boxes off. But the indirect effect of that I love hearing back about is, oh wow, this gave our team clarity on what are we doing and why. Because we literally made the roadmap of this is what we have to hit. And the net result is like people are more autonomous and you need less meetings where you're reminding people like, Hey, do this thing. Everyone is gunning towards the same direction more clearly.
Kevin Tate: You brought up costs, and our next question is about cost. I know it's nuanced because really what you were showing today is that in the case of Kivo and Kaleidoscope, the data room is a built in capability. But more generally, can you provide a bit of guidance around the costs one should expect around setting up and maintaining a data room?
Bogdan Knezevic: Interesting. I guess there's capital and then there's time. So the way we've approached at least at Kaleidoscope is because things like data rooms and things that bring people together are inherently collaborative, we give you a choice of how advanced of a license you need and that license is for your team and so you don't have to worry about who you're inviting and how, because a lot of people are involved in generating the data so that because we're data focused, we wanted to unblock teams that way.
I think on a time from a time investment standpoint, we worked really hard to be a resource for folks when they get going. So if you're someone who wants to just get up and running and you wanna follow our standard out of box templates and read our in-app documentation, great. You probably need a couple of hours to configure your initial workspace and you're up and running. If you want us to pair with you on that over several calls, maybe run a workshop for your team, happy to do that. Again, our goal is to meet customers where they are. If they want kind of white glove, great. We provide it. And if they want self-serve, great. They can.
Toban Zolman: To your point Kevin, often data rooms are a side benefit of organizing everything in Kivo. Not necessarily the core use case, so to speak. So it's a little tough to say it costs X dollars. But generally speaking, I think what we're seeing with companies that are moving out of drug discovery into development is that they're able to essentially roll a near out of the box style configuration to Kivo. There's not a massive amount of like regulatory submissions or QMS docs that they're migrating in. It's very much a near blank slate. In those sorts of implementations, we measure those in days to implement. At the end of that, it's validated. Everything is turnkey and ready to go, including a data room structure that we can supply out of the box. And the cost really just comes down to how many users you have. That's what we base that on. So those initial implementations are usually in incredibly reasonable compared to any other system because we're largely focusing our cost on users.
Kevin Tate: Thank you. Thank you. Somewhat related question, as you point out throughout the presentation, there's work here. There's a little work upfront, and then there's ongoing work. Who's the best executive champion for that work? If this is a priority for your team, how do you go make sure you've got the right person on the exec team to sponsor it?
Bogdan Knezevic: On our side, I think we've seen pretty consistently that like a VP of nonclinical is a good starting point. 'Cause ultimately they're the ones trying to empower their team to do their best work and stay on track. We're able to basically get going pretty quickly as soon as there's a VP involved. Sometimes it's the VP of themselves that are like, Hey, I want this, or, it's someone who directly reports to them.
Toban Zolman: Oftentimes we get a call or an email from finance, chief strategy, corp dev officer, who says we need to set up a data room for diligence and are trying to figure out how to get all of this, all of these documents and data into this other system. We jump on a call and they, we all figure out it should be reversed. It should just be pulling finance stuff into Kivo 'cause everything's already in vo. So we typically see two owners, so to speak. We see finance or corp dev or whoever's driving that investment diligence, licensing process, driving the data room and then getting data out of regulatory, TMFs, preclinical, et cetera, into that. Just due to the size of companies we're dealing with, it's typically the head of regulatory mainly 'cause they're just, it in a small emerging company, there's just nobody else. And so that's how that usually shakes out.
Bogdan Knezevic: I like the point on pulling data in. That's something that we've also try to be very clear with folks, is like, Hey, if your scientists and chemists are happy with the core tooling they use day in and day out, great. They keep using that. We have integrations into those tools, so you're not requiring everyone to rip that out and recreate it in Kaleidoscope. We handle that just via our integrations.
Kevin Tate: Perfect. Toban and Bogdan, thank you so much. It was great to see both the best practices and the systems to support it to our attendees. Thanks for coming and we'll be sending out a recording of the session shortly. Thanks so much.
Bogdan Knezevic: Thanks.
Toban Zolman: Thanks everyone.