How to Build a Lean, Mean Biotech Machine

You know I love systems. And in this episode we are talking with Bogdan Knezovic, who is the founder of Kaleidoscope Bio, which is a platform that helps companies organize the workflow for getting things into the clinic and beyond, really. So we talk a lot about keeping your eye on the prize and how to break down workflows into little bite sized things and keep your team all marching in the same direction without distractions, without shiny object syndrome, which we know is a problem for scientists. I am guilty of this too, and I really liked his perspective on how to trim down the operational fat so that we're saving money, we're saving time and resources, and we're getting to our milestones faster. So this is a really tactical episode where we are talking about an actual tool which is a platform, but we're talking about the overarching need for a tool like this or systems or processes like this within every biotech. It doesn't matter if you started yesterday or you're all the way in the clinic, whatever stage you're at, this episode is going to talk a lot about making systems that work for your team.

Welcome to the Building Biotechs podcast. Over the years, I've helped over 90 biotech, life sciences and venture capital firms strategize and hire thousands of employees to scale companies that impact human health. We speak with those at the forefront of growing biotechs to learn their tactics on building these companies from the ground up. I'm your host, Karina Klingman. I hope you enjoy the show.

Bogdan, thank you so much for joining me. I'm really excited about this conversation, but we like to start at the beginning. I really want to know how you got here. So what did you want to be when you were 7? What are you now and how did you get there?

Thanks so much for having me. Karina. I actually love this question because I know you ask it of all your guests and I think it's a really fun one. I actually was disappointed because I've heard one person give this answer before, which is for me was split between archaeologists and paleontologists. It's probably because as a kid I really loved like Jurassic park and Indiana Jones and those kind of movies and that's what the characters there were. And I think for archeology at least, the person who burst the bubble for me there was like my grade five teacher who told me that it's nothing like the movies. It's grueling, long hours. And so that's kind of where I started thinking about Other things to do. But yeah, that's that. That was me in grade school and now I'm co founder and CEO of a, of a bio software company called Kaleidoscope.

Excellent. And so Kaleidoscope is really a very tech enabled company. Do you want to just describe what you're doing there and you know, what's the value proposition for small biotech specifically?

When I was working as a scientist, I just felt this deep frustration at just kind of the disconnect and lag between what's possible in the world of software and how actual biotechs operate and what the kind of day to day looks like when you're doing science. And so there was for me this like nagging feeling of like this could be a lot better. People could be spending their time in smarter ways. We could be, you know, making better decisions as a field. We could be leveraging the data that we're generating in smarter ways. And so this realization that there was these pretty big inefficiencies in how R and D operates from, you know, how you do your weekly review meetings to how you set kind of monthly or quarterly milestones, how do you track progress against those? How do you connect what's happening in the lab with what you need to do as a business? That process and those frameworks, my co founders and I felt could really be improved. And so that's really what we're trying to productize at Kaleidoscope, we're trying to give biotechs, whether they're small, early teams or much more mature, like well oiled machines, we give them tools to actually make their R and D dollars go farther and make their operations run faster so that, you know, you shave off, you know, months in a given year towards hitting that next milestone, whether that's a fundraise, whether that's getting to clinic, whether that's a drug reaching a patient sooner, all of those things kind of have this knock on effect.

Give me a use case for this in real world terms like when would a company come to you and what would you do to streamline those processes for them?

We have two flavors of, of companies, roughly speaking. One is the biotech that is fairly small or fairly early. They have, you know, data they're generating. It's often coming off in like spreadsheet format and they're trying to really figure out how do we stay on top of that in a more scalable way. And maybe they've gone from just spinning out to now they're hiring and they're, you know, 8, 9, 10, 11 people. And now you're no longer at a place where you can keep things in your head. And so the value prop for that flavor of company is we've built a lot of architecture that will help them organize, catalog, track, label all of those data points and that information in a way that's intuitive and easy for them to review so they can get together as a team and easily see like, what's the latest that we've generated. How does this track against our kind of key milestones that we need to hit? What experiments do we need to go back and rerun and kind of giving structure to that, to that chaos? The second flavor of company is the more like established organization. Maybe they're, you know, already have an asset in clinic and they're working on a pipeline. Maybe they're, you know, really late stage. These could be companies that are, you know, 100, 200, 300 plus people. And there, it's actually, it's actually, funnily enough, similar things. The main difference is that they probably already have a bunch of other tooling in their stack. And so rather than us being kind of the only platform that they turn to, the value prop there is that we will integrate with whatever tools you already have that you're kind of as point solutions. So maybe the tools that your chemists are using, the tools that your biologists are using, the tools that engineers are using, we have integrations into all of those so that we can pull kind of the key summary data that you need again to kind of drive that cadence of review and milestone chasing. So an example use case there, in addition to kind of weekly reviews, might be like your quarterly planning as a company where you need to set your budget for your R and D. And that should be tied to like tangible output that you're chasing, whether that's against a target product profile, whether that's, you know, deciding between four or five lead candidates, whatever that that comparison that you're running might be. We give you really automated and intuitive ways to, to manage that.

That's not something that scientists really are taught to do. I mean, we may be working especially we've got academic founders that I work with a lot. You work toward getting a paper out the door or things like that. So you kind of know that end goal. But I don't think anyone ever really measures that output. And so now you're beholden to stakeholders and investors, right? They need to know that you're on track, that their money is being spent intelligently and toward the most important Goal. And so I, I think that's something that is skipped over or is a shock when people start companies, especially out of academia.

Absolutely. And I think in biotech it's unique flavor too because you often have like a pretty formal board structure early on, which is not, you know, typical in every other kind of startup case. And so you're really early on being held responsible for like pretty existential questions. Like, you know, if you can't show a certain data point, then the company might not exist anymore. So it's very rigorous in a way that I agree with you. Like, it just isn't the case coming out of academia.

Yeah, absolutely. I want to talk about empowering teams. How do you actually help biotech set these priorities and then establish plans to meet these milestones?

To keep it pretty simple, one thing is just being explicit about what the goals are. I actually think that it's not uncommon for companies and leaders that aren't used to operating in such a intense environment to not realize that just saying, hey, we are going after X or like the purpose of this quarter is Y, that can be a really powerful thing. And then doing that at like different levels of granularity, like, you know, what's the goal for the year? What's the goal for the quarter? Kind of getting smaller there. Of course those things can be changed. But just being explicit about them, I think in science there's this interesting trap that people can fall into which is running these unbound experiments because the, like, I don't know if it's scientific curiosity, I don't know if it's just kind of like wanting to follow the cookie trail because again, that's as academics, what we're taught to do. But there's a tendency to do that and not stop and think like, hey, it might be, you know, boring or uninteresting, but the thing I need to show is actually this metric changing. And so is the work that I'm doing moving the needle on that metric. And you only know that if you're explicit about it. As a team member, as a leader, that's one thing. The other, and kind of implicit to the answer I just gave is like working backwards, so starting with the end goal and kind of like breaking it down. So I think this to me was something that became very apparent actually. I used to swim competitively and so this is, I think, a skill that a lot of athletes, especially in sports where there is a cadence of every four years, like Olympic based sports where you're chasing a milestone that only comes every Four years. And the outcome is binary, like you make it or you don't. And so you're spending four years going after something that you have to achieve within, you know, a two minute race, in my case. And so you have to have some way to know what that end goal is. But you can't just rely on the end goal being something four years away. You actually have to break that down to like, okay, for me to get here, what do I have to have achieved the year before that and the year before that and the year before that? Okay, so within this year, what do I have to have achieved at the halfway point? Okay, and to achieve that, what do I have to have achieved by the end of this month? And it's these, like, compounding stepwise gains that you make that get you to that final goal. And I think not knowing what that final goal is and not having done the thoughtful work of breaking it down backwards from there is a dangerous kind of blind spot for folks. So that's another thing that we try. And again, sometimes it's within our actual product. So showing people how you can structure an R and D project or a program, or set milestones that might have different granularities of timelines associated with them and how you can automate it such that as you get new data, we show you your progress against those. But also just advocating for people to do that, whether it's with our tool or whether it's with other tool that they're using or other processes that they're running. How do you actually set up reliable systems where you know what you're chasing, why you're chasing it, and you're seeing exactly how the work that you're doing in the lab does or doesn't move the needle on that as a team or a company.

I imagine when you're breaking down your goals for swimming, it's like you need to get your turns, you need to get like there are little pieces that you can improve each thing. And I, I think that having been a very competitive athlete, you do have that in your mind, but you don't always translate that into what that actually looks like in work. And so I'm curious how you help teams balance the short term versus long term goals. Because some of these goals, if you, if you're talking about starting at the very end, it's like, you know, patients are actually using drugs that you have, you've discovered and commercialized. Right? That's a really long term goal. And so how do you help teams balance that? Because keeping your Eye on that prize versus okay, but to get there. This month's goal is a very small piece of that.

It starts with just awareness and realization that's what the job will be. And that it maybe is a bit counterintuitive for leaders who haven't gone through a few of these cycles. Because you know, when, when you're in an intense period of time, you want to focus on putting one foot in front of the other. And it's maybe counterintuitive to say like, no, you actually need to like lift your head up and ask those long term questions. Start asking, what does this look like? If all the science works out, then what happens? Like, is there a market for this thing? What's the standard of care currently? Like, will clinicians want to prescribe a new drug? Like under what conditions? And then those things can actually inform what you're doing in the lab pretty granularly and what experiments you're running. Because it'll kind of constrain your search space in a way. Because what you don't want to happen is that you do this, you know, five to ten years of brilliant science and everything works out and you, you know, get to an approval and then, and then no one, the drug doesn't reach patients because like no one is prescribing it. So it sounds like again, maybe obvious, but it's definitely not to people who haven't gone through a few kind of drug approval cycles. So the awareness, like, hey, you should be asking these questions really early and thinking about them really early and not kind of kicking the can down the road or lying to yourself that like, oh, you know, R and D is going to take five to 10 years. So I'll think about those problems later. As a leader, not, not being afraid to kind of zoom in and out, you need to not be afraid to occasionally put yourself in the shoes of what someone else on your team is doing and what's their kind of experience in the day to day? Like, which leads me to how do you actually empower your team to do the best work? Because like you said, you're, you're holding in your head something that might be a decade out at the same time as something that you're focused on this month and no one can do that indefinitely. And so you have to rely on your team here. And so I think you need to have this acute understanding of is my team set up to do the best work that they can? Is the system that we have in place conducive to us being able to move quickly on these shorter term goals. And I'll give you a very simple example here. A lot of bio CEOs probably aren't aware of this, but often as a CEO, you'll be sent power index, maybe weekly that have, you know, a summary of a bunch of information. And if you don't stop to ask, wait, what was the process that, that got me this information? You could go on thinking, great, everything is easy. Every week I get this update, it's wonderful. And then if you actually go and ask your vp, SVP director, whoever it is, who prepared that, how did this kind of material come together? What we often hear from folks is, oh, well, I spent, you know, six hours this week compiling it and I emailed, you know, these 15 people to get them to go and screenshot these results and send them to me. And then I had to clarify. And so you learn that actually it's a very manual process. It's a very painful process. And just because you as the senior leader don't see that, doesn't mean that your team is not like feeling the brunt of that. And so asking yourself, knowing that we're going to be in this grind for a decade, is there ways I could be making this kind of day to day easier for my team so that we can still focus on knocking out these short term goals and move towards the longer term one?

I think it's really valuable to say that that's a problem. And the typical reporting structure, the typical way that that information funnels up to the CEO and therefore to the board, like they need that information, but it doesn't work really, really well is what I think I'm hearing you say.

Yeah, that's exactly right. I think it just, it's very often very painful for the people that are involved in compiling that. And then that can complicate things. That leaves you with gaps in potentially kind of bleeding knowledge. It risks burnout, it incurs opportunity costs, like all of these things kind of compound over time.

What happens when a team needs to pivot? Let's say they get a piece of data that isn't supporting the development cycle or something like that. How can teams gracefully pivot without it causing major waves within the organization?

You do have a bit of this kind of binary nature to is the science working or is it supporting what we needed to support in a way that you just don't in, let's say software, where maybe the lines are blurry on whether you're on track or not? There is, I think, a bit more like ego in that world. And people can really affect people who decide to, like, pivot away from a project that they've, you know, been working on passionately that they still maybe believe deeply is like, the right one to make. Whereas in science, I think it does come down to what's the evidence showing us. Like, if what we're seeing is that there's just not a result here or like the talks is just not good, then, you know, we just have to move into a different direction. And again, I think it's, you ask, you know, how do you avoid having ripple effects through the team? I think one, it's framing it as, what's the alternative? The alternative is that we spend the next five years pursuing this and we get maybe really far and then the rug is pulled from under us. So that's not a great alternative. And then I think also just reminding people, like, even if it's not glamorous and even if we have to do this, like, what seems like repeat work because we're pivoting and starting from scratch, the reason why we're doing it is because we're ultimately chasing a drug that we want to get to the market. And so it is a bit of the, like, putting of the ego aside and saying we need to, we have X amount of dollars that we have to put towards advancing this, this R and D as far as possible and to get it to clinic. And so we just have to make pretty hard decisions sometimes to maximize the likelihood of that actually happening.

One thing that I think your software does is to help to reduce some of the operational overhead for teams. Can you tell us a little bit about how that works and why that's important?

Something that I've seen effective leaders do really well is actually map what the cost of any given thing is really well in their organization. So the kind of sharpest, whether they're CEOs, CxOs, SVPs, whatever that I spoken with, know exactly how much a new employee costs them, how much it costs to find one that can do the job, how much it costs to train a person, what it costs in salary and benefits and xyz, what the, you know, expected output of work is and versus what the reality usually is, factoring in all the setbacks. So they're really good at kind of getting it down to numbers in a sense of, okay, if we run a month and a half of R and D with this big of a team focused on this part of our pipeline, it's going to cost us X dollars. So I think it starts with like, building that awareness and understanding how your actual, like, operations look like today. And then also what I've seen really sharp people do well is be able to project out quite a bit. And so I'll give you a very concrete example here based on a conversation I actually recently had where someone was able to, on the spot, kind of do the math of if we get to clinic, you know, six to seven months faster, that means that we have, you know, six to seven months more of patent life, which means we have six to seven months more of sales once the approved drug is on the market, which is X dollars of more revenue. And so they were able to like, take something from the, like, early R and D stage and extrapolate that out and say, you know, not just are we saving, you know, six months of whatever FTE costs and ops and whatever today, but we're also seeing on the other end of this, like, a pretty massive bump up in revenue if we can save that today. So that kind of clear mapping is a pretty important exercise to go through. And that's, you know, really the beginning of, of how you, you can start understanding how much are your operations costing you and therefore, is it worth investing in X, Y, Z to move the needle on that?

Once you've assessed that overhead and you kind of know what that looks like, then what are some data points and strategies that will enable your team to work in a more lean and focused way so that you can maximize your Runway? That is a huge topic right now because money is not flowing the way it used to be. And so we're trying to figure out how can we do more with less and make that Runway longer. And yeah, obviously it would be awesome to bring things to market sooner so that we could get the patent life and make more money there. But often the teams I'm working with are focused on how do we just get enough data so that we can make it to Series B and we can de risk to that point.

We put out a piece several months ago where we just crowdsourced some thoughts from investors on what they're diligencing at seed, at Series A and at Series B when they're doing biotech diligence and breaking it down to like, platform versus asset companies. It touches on, on a couple of stuff that we've already talked about. So one is being clear, I think, with your team on the, like, what's the thing that you're chasing ultimately and what are those inflection points along the way that unlock either the new, you know, round of funding or your ability to file an IND or, you know, getting into clinic. So being very explicit with, like, what are those things? And then how do you actually translate that to the framework I have is like, what milestones you need to hit, what data package do you need to support that milestone, and then what experiments do you need to run to generate the data to support that milestone? And so, again, it sounds simplistic, but I think the exercise of doing this is a really powerful thing. And what that lets you do is keep your team focused on not running experiments just for the sake of kind of scientific curiosity, but like, having a purpose in mind for them. It keeps your team accountable for what are those specific readouts that you need from your assays, from your screens, from whatever it is so that, you know, okay, this step is a check. We can kind of like move on and do this next step. So that framework, I think, is. Is something I've seen really lean and productive teams use, whether they're using it explicitly or whether it's just kind of implicit in their process. Like, I don't actually know, but that's kind of what I observe from the outside. And then, like, what we're trying to basically help people do within Kaleidoscope. I think another thing is if you want to keep your team lean, then you have to be very robust against what I call like, data or knowledge leakage. You can't afford to have experiments either running or not running without your knowledge of them. And it sounds kind of like an obvious. But the amount of times I've seen this happen in large orgs is pretty staggering. And so if you're trying to stay lean for longer, which is a trend that I think I'm seeing more and more, you know, biotechs that are, you know, only 20 people and they're like, entering clinic, which is kind of happening more often. I think if you're going to operate in that world, you need to have. You can't afford to have a leaky ship. And so knowing even something as simple as knowing the status of an experiment, knowing, like, hey, is this thing that we sent to the CRO and we expected back this week, like, is it back or not? Or we said we were interested in assets A, B, C, we now have enough data to decide which one do we want to prioritize? Rather than kind of pushing all three of them forward marginally, it's about kind of allocating resource to the most exciting one. So all of those kinds of questions, I think are pretty critical. And that's what Again, like, from a product perspective, I think this touches people culture, this touches process, and this touches the tools that you use. So we're kind of focused on the tooling side and a bit of the process side. But that is really what we're trying to enable teams to do. And so the way we approach it, for example, is one of our features we call dashboards. It's basically where you set up the kind of stage gates for your program that you need to hit to, let's say, get to an ind. And so we made it really easy for people to encode rules into these dashboards and to say, hey, for this stage gate, I care about these experiments, and for each of these experiments, I care about these values. And so if I get data in for this asset and it's any of these experiments and it meets any of these values or doesn't meet any of these values, I want to visually see that and I want to be notified, hey, there's data. It didn't look good, or hey, there's data, it did look good, or hey, there should have been data, but there's no data. The idea here is you need to make it really easy for people to stay on top of what they're doing and to hold themselves and their team accountable to that so that you can move forward with kind of confidence and speed.

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Okay, this is going to sound really crazy, but I wish I had this even. Even back during my PhD thesis work. Only because what you just said was that, you know, experiments happen and there's this leaky. There's a sort of leaky ship. Well, that's all. That's all labs everywhere. I don't care if they're academic or industry, because scientists be Scientists and we're going to follow, we're going to follow the data. But to your point, that is not following the data does not always mean that we're going to get the results we need for what we're actually trying to accomplish. And I would say that most of the papers that I published probably I could have cut down the actual time to publication by a significant amount, months. If not in one case, maybe like two years if I had had a better, a better plan.

Yeah, absolutely. Yeah, we hear this. It's, it's a pretty common sentiment shared like, like you said, it doesn't really matter if you're academic or small biotech or mid cap or large public pharma. This is, I think one of the universal realities of working in science.

I. Oh yeah, yeah. We have a. Scientists are a certain phenotype. We're curious and we're experimental. And that doesn't always play well in business, unfortunately. I wish it did. But you know, investors really do want to see that you're making that steady incremental progress.

Exactly.

So what is your philosophy then on aligning that data with those key milestones? You said the have these dashboards. How does the data actually get into those dashboards? Who puts it in and how does that process happen?

We try and meet people where they are. As you just said, scientists are scientists. One of our guiding philosophies early on was as much as we can propose, like a much better way to work. We have to meet people where they are because humans are humans. And also we have to make it so that people don't do any redundant work because it's already hard enough to have a scientist, you know, log their data somewhere. If you're now asking them to do that in two places, I think that's a losing battle. And so regardless of whether you're one of those early companies that just has CSVs and spreadsheets of data, or whether you're a much more mature shop that has, you know, six other tools that you're using, we make it really easy for you to get that data into Kaleidoscope. So in a world where the scientists are already logging information, maybe they have an eln, maybe you have a LIMS in place, maybe you have like a registry somewhere else, maybe you have a custom database. So someone is logging those, those things in already in that world. What we do is when we set someone up with Kaleidoscope, we basically the implementation is us like creating these hooks into those tools and asking the kind of leaders, the ones who are looking to kind of summarize and distill this information, hey, which of these values? You're not going to need all of the raw data you're ever generating for your decision making. So of the data you have, what's the stuff that's critical for you to actually track? And so we show them how you specify that, let's say, in a dashboard, and how you point that dashboard to those sources of data. And then you can have Kaleidoscope, for example, update overnight every night and sync with those sources so that any new data that your scientists would have generated appears in that dashboard. That can happen automatically. We built these, I would say, like templates or building blocks that you can kind of modularly put together. And so we show people on that first call, hey, instead of you going and opening a PowerPoint to start creating slides while you're pulling in screenshots, you can, you know, within three, four clicks have a dashboard up and running and, and set it up so that it auto populates with information the side where it's people and spreadsheets, again, we tried to make it straightforward for folks so that they don't have to do a bunch of extra work on their end. And so we have a, we even have like a drag and drop flow with spreadsheets where you can, you know, just pull your spreadsheet in and then we'll do this kind of smart mapping of, hey, this column here actually looks very similar to this other column. Are they the same? So, for example, I saw this in my lab all the time. I might call something mole-id and you might call it mo id, one word. And someone else might call it mole space id. And we don't want these being like duplicates. We also don't want scientists having to go and like edit their files and like agree on what they're going to call it. And so what we've done is make it so that if you kind of two, three different scientists drag things in, we detect like, hey, this is probably the same column. Like, you can say, no, it's not, but if it is, we'll treat it as the same. So we'll do that kind of cleaning and curation and, and labeling for you so that again, you as a scientist are just focused on generating results and then, and then we handle all the kind of hard work downstream.

Yeah, that sounds very valuable. Scientists don't love doing extra data entry.

No.

You see a lot of different companies. Are there any mistakes or things that you wish leaders knew or things that they could implement earlier. That would make things a little bit smoother as companies grow. I know you work with companies of all different scales, but sometimes you come into companies, do you look at them and go, if you've just done this thing would have been so much easier.

I think it's very common for people to not realize the value of compounding gains. And so it's very easy to think, oh, it doesn't, you know, who cares if this takes us an extra several hours a week right now? Like, we have bigger problems and we'll just deal with this again later. And so I, that's somewhere where I always bang my head against the wall and just think to myself, know that those several hours a week is going to be huge. Because over a year now, that's, you know, that's quarter of your time potentially in a year, which is a lot of R and D operation, that's a lot of budget, that's a lot of, you know, existential threat. It's to me, something that's, you know, beautiful and scary about biotech is that you're almost constantly under existential threat of, like, will we exist? Like, will, will the data come back positive? Will we get to run this experiment or will we run out of money? So I just wish that people were much more ruthless with, wait, are we using people's time in the most effective way? And what's the opportunity cost of not doing something? I think that's one. And then the second that came to mind is more like software specific. This is where again, leaders that have been through a few cycles, they're very aware of this. And I think those that haven't, like, they'll learn it eventually. But when it comes to specifically software, and this was one of the motivating reasons that we started Kaleidoscope actually was when we were doing our kind of discovery work early on, before we built anything, it was pretty astounding the number of biotechs that told us, hey, we needed a better way to kind of manage these flows. There wasn't anything built for the space. So we went ahead and hired software engineers, or went ahead and repurposed a bunch of computational people and they're now building these tools for us. And I think that's a very big mistake because in the vast, vast majority of cases that software is not core to your ip. Your IP is the science that you're doing. And so everything that you're spending money and time on should be advancing that ip. And so if you're now trying to also Build, maintain, effectively run a software company within a biotech. That is massive upfront cost and massive maintenance cost. Again, I'm biased here, but for a fraction of that price, you could bring in a tool that like, yes, it'll take your team a few weeks to get a handle on, but it will save you so much more than those few weeks. And so I do wish that more people kind of were open to this, to this idea in general. And I think it's a trend that we're seeing, like that's the bet that we're making is that again, leaders are seeing that they know how painful it is to do once, they don't want to do it again.

Yeah, those are both great points. I call the first one and sort of the second one too. I call that startup math. It's like time doesn't matter, but it does. It matters actually a whole lot. And I talk about that in the context of recruiting and talent acquisition and in your team's time trying to navigate the that process. But what I'm hearing in your answer is like, this could be a whole headcount lost to simply doing stuff that's manual, that shouldn't be done manually in the first example. And then in the second example, yeah, it's keep your eye on the, on the prize. And this is the shiny object syndrome that scientists have. It's like, well, I can envision this software tool, so let's just build this software tool. And I've had to restrain myself from doing that very thing in my company, Applicant Tracking Systems. It's hard to find one you like and you just start to think, well, why don't I just build my own tool? But I've luckily talked myself off that ledge because that is very expensive. It's a long endeavor and I'm not going to do it as well as the custom built ones that were there just focusing on that tool.

Yeah, I think that those are great examples. And it's funny because I think a lot of people actually, if you like, sit down with them and ask them to talk through the math, they'll reach that conclusion. So it's not that you have to convince them that. It's just that they, I guess, don't put the time to do the math. And so it's not uncommon for me to be talking to, let's say a series like pretty early stage Biotech that tells me, yeah, we spend a million dollars a month on R and D. And so if you can save two months in a year of R and D, which We're seeing like our other customers tell us that's $2 million a year saved. So when you frame it that way, like is spending, you know, whatever that the number is on, you know, a small fraction of that on a tool that lets you do that, is it worth it? Like just the math. Within a year you'll more than paid it back.

Yep, math is hard. We don't always think about it. What is the best book that you've read recently? What do you think everyone should read?

I actually recently read Ted Chang's two collections of short stories. So one is stories of your life and others and the other one's called Exhalation. So if you've seen the movie Arrival, it's actually based on one of the short stories in that book. What I really love about it is it's short story format and they're all sci fi, sci fi esque. And so what that actually lets you do is free yourself of the constraints of what's the real world like. And so in a very short amount of time the author can say like, okay, cool, the world looks like X. Just assume that that's what the world looks like. Now we're gonna run this like thought experiment of what would happen if X, Y, Z was true. And so it's like this really nice way, I think, to explore these concepts and ideas without being constrained to, you know, the world of nonfiction or without being beholden to a narrative that you have to maintain when it's a long form novel where you have to do like character development and all of that. It's just exploring the ideas. Nonfiction. This book has kind of stuck with me. It's called Let my people go surfing by Von Chouinard Patagonia founder. And I think it's. It's just a really great book to read on people and culture and purpose.

Yeah, great recommendations I've read Let my people go surfing. I'll have to check out your fiction recommendations, but that's a great book and I think it's on the list of must reads, I think for leaders, good perspective and a fun company. So we are going to link some resources in our show notes, including the resource you talked about earlier that you recently published. How can people get a hold of you? What's the best for you? Is that LinkedIn, your website?

Yeah, I think both work. So if you want to get a hold of either be personally or just keep up with like little snippets of what we're up to As a company, LinkedIn is best. And you can always message me directly on there. You Kaleidoscope a follow, whatever. And then our website's great for more keeping up with like what the actual platform is doing, the kind of new features we're shipping, what our customers are kind of saying about us. So that's Kaleidoscope Bio Super.

Well, thank you so much. We'll put all that in the show notes and this was a great conversation. I hope that folks take away a little bit about organizing their workflow from this because it is an afterthought, I think, in a lot of cases and we don't want that to be the case for your company. So thanks so much.

Thank you, Karina. Thanks so much for having me Building.

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