Enhancing Scientific Reasoning in Biopharma with Christopher Li, CEO & Co-Founder @ BioBox

Nectar: Chris such a pleasure to have you on really excited to talk about Bio Box. I know we've had a few conversations offline and yeah, you spoke about, like how do we, how do we up level biotech in Canada, right? So I think that's one thing I wanna get when speak about today, but maybe just to start off things simple, man, like what's you start up?

What does it do? 

Chris: Thanks Nectar. Thanks for having me on the pod. I'm happy to share more bio box. What we do well, what it is it's a knowledge and reasoning infrastructure for biopharma RD. And so what we do is we help scientists scale up hypothesis generation in testing in early drug discovery.

Now, this is really important 'cause 90% of drugs will fail and whenever they do it's costs a lot of money. So we can make them fail less by testing more hypotheses because this helps us reduce scientific uncertainty. And what makes Bio Box such a powerful platform is because it helps pharmaceutical organizations build collective intelligence like a knowledge arbitrage in a way.

And so at the end of the day, what differentiates the winners and losers in biopharma is asymmetric information. In other words, what do you know that nobody else does? Bio box really helps these organizations magnify and leverage that asymmetric information. This allows 'em to move faster, they can make more precise data-driven decisions all across the early r and d phase of drug discovery.

Nectar: Yeah. Fascinating. Fascinating. Chris, maybe so let's double click on your solution, right? So one thing that you guys try and solve is this notion of reasoning and hypothesis testing, right? So it's maybe give us a, like how does it work today? I'm a scientist, I have to come up with a hypothesis.

I capture this inside the company I'm working in and then I have to do testing. Maybe unpack that and then where, how you guys kinda like building a new solution in that space. 

Chris: Yeah. So let's walk through an example of how a scientist would use the platform. What it offers is a single integration point for all their biomedical research data.

So we're talking about multis data, we're talking about literature, we're talking about knowledge bases, databases that they're curating. Internally, what we'll find is that they have a lot more private data. Proprietary data from their assays or from their experiments, then you know what's out there in the public domain.

The goal is how do we accelerate or make it easier for scientists to ask complex research questions about their data and also make connections about these data points to form new hypotheses the easier we make this. The more hypotheses they can test, the higher quality of the hypotheses. So what's an example?

Hypothesis would be something as is this gene that I'm thinking about targeting expressed in the right populations, in the right cell types at the right time. And whenever a scientist is thinking about, let's say a problem like target identification, you're really trying to figure out what is the rules or criteria or framework you're using to say.

A good target for this disease. Different scientists have different perspectives, and this is that asymmetry, right? It's really the quality of the questions you're asking and the type of scientists you have, and it becomes very hard to transfer that knowledge over time. So as an organization, a big enterprise, you might have 50 different research groups.

How do they share information? How do they transfer these tribal knowledge that they each team has effectively? How do you use all of this to make reason decisions? So what we do at Bio Box that's very unique is we focus on capturing the reasoning logic and then we digitize them into. Reasoning models.

Now there's two avenues that we could work with them. These reasoning models, they kind of function like digital experts. Someone who's how would they, how would an immunologist interpret this target identification problem? How would an oncologist look at this? How would a chemist look at this? What are the different.

Lines of evidence they're looking for as they're traversing this knowledge graph, that central data asset that we help them make and a human can interact with these models. Or now what we're seeing more and more we're working with our customers is building agentic systems that are talking to these models.

What it ends up feeling like Nectar is a scientist, will have access to a digital round table of experts, and this really helps them accelerate the amount of questions they can ask and the quality of the responses that they're getting back. 

Nectar: Yeah. And how does that look like at a very simple level, like I'm a scientist, like you mentioned, I need to come up with these hypothesis.

Am I today, am I embedding this in some like old school legacy platform or is this living in like Excel, PDFs, et cetera, and then you create a new platform? Or do you embed with let's say a legacy system? 

Chris: So it, most of the times, believe it or not, it's just. PowerPoints and CSV files in a SharePoint somewhere, and part of your discovery process is figuring out what have we tried and finding the right.

You presentations and try to figure out and piece together what happened. Obviously there are, drug discovery research has increasingly become more of a digital has a digital biology focus. So there's existing data infrastructure, data lakes great data science teams and computing teams at these pharma companies.

And, we'll obviously plug into that. But at the same time we offer a way for these scientists to quickly onboard and, single sort search portal, like a Google search, but across all of your enterprise domain knowledge in one place through the bio platform. Yeah. 

Nectar: It's fascinating.

It's fascinating. Yeah. It so it's it's still super old school to a large extent, in the way that it's done. And the, by the fact that you're dig digitizing, it allows, these scientists to like really up level. How do you then, yeah. Do the coordination thing, you mentioned like being able to like have this more institutional knowledge graph, is that just like basically I'm scientist working in department A, I'll be able to see what, my colleagues in department B are working on. Like how do you think about that flow?

Chris: Yeah. So when we think of collective intelligence or building knowledge systems, I think the devil's always in the details and it happens in this case to be, what do words mean in our company?

So we've built a lot of these systems and implemented them over the years, and the challenge is biology is a science of context. And the way that these pharma companies think about scientific frameworks is very dogmatic, and they differ from pharma to pharma. So an example of gene X is implicated in disease y what does implicated in mean?

So being able to onboard a team and being able to transform at first the ontology is what we call it. What is the governing scientific framework that dictates how you as an organization think about science through there is where all the integration begins and sets up this collective intelligence for these pharma companies.

Nectar: So it's almost like. Converting the knowledge and like these abstract words into code to a large extent, right? What the word itself means can be encoded and basically programmatically across the organization. So it's it means the same thing across all stakeholders that are using the platform.

Is that correct? 

Chris: Yeah. Yeah. This is the foundation of reasoning, right? First, you need to make sure that everyone understands what these concepts are, how they relate to each other, and that way the data that's feeding it you can start building up more and more complex layers. And the important thing about decision making in pharma is everyone is very clear on why this decision was made and being able to rationally explain.

The underlying assumptions, and that again, comes down to breaking apart this tribal knowledge that we have, and it's through no fault of their own. When we work with customers, what ends up happening is there's an immense pressure for them to. Basically identify these targets, launch these programs, and when you're coordinating with a central data unit or central data teams, sometimes it's just a little bit faster, Hey, I'm just gonna whip up a Postgres and start curating my own databases or put it in my own format because we need to hit these objectives so it takes.

A large organizational kind of momentum to get over that activation energy of saying, okay, look, we're gonna have a digital strategy. And over the past couple years we've seen them happen transforming a lot of pharma enterprises having this digital strategy. And now we're at this next layer, which is, okay, we're great at setting this data infrastructure up.

Now let's think about what's that next system? That's where that reasoning component comes in. That's where ontologies matter. And to be able to increase. That reasoning capability as an organization. 

Nectar: Yeah. Fascinating. Fascinating. And then you mentioned this notion of AgTech, like being able to do work itself.

Can you maybe just provide a little bit more info, like what does the system then do and at maybe at what level is it at the hypothesis level? Is it at the testing level? 

Chris: Yeah, so we don't necessarily build an agent. We think of more ourselves, more as like an infrastructure that agents can tap into to get a little bit more juice out of the data.

And here's what I mean by that. Our platform allows these teams to curate an ontology, stream their data and feed this knowledge graph that is semantic data. And what this allows us to do and what we created as a retrieval pipeline is. At the time that an LLM or an agent is trying to access this graph, we can actually swap in these ontologies.

Kind of think of it as like a memory unit. Say, okay, interpret these questions or these things that you're trying to accomplish. Using the concepts and relationships right defined in this ontology, these ontologies can change, right? They can grow over time. They can evolve, they can be completely different ways of looking at the same knowledge graph.

And this enables the agent to almost get a nuance or a bias take where something is very focused on a certain section of the graph. This is that akin to, interpret this question as a chemist, interpret this question as an oncologist or as a translational biologist, X, Y, Z discipline.

And that opens up a lot of powerful opportunities for these age agentic systems to create novel reasoning kind of threads going back and forth, conversing amongst themselves, accessing this. Digital Roundtable. Yeah. So that's how we inter interface with these AI agents that our customers are already building.

And it's been pretty exciting lots of interesting combinations that we're seeing these days. And I think we're only at the beginning we're seeing, recently Microsoft Discovery just launched, which is there are, platform for building AI scientists. That was massively.

Encouraging to see because central to that whole premise of building these AI scientists was graph based, pro reasoning, structured knowledge, right? Something that can iterate and grow over time. And I think we're gonna see a lot more of these this type of work in the next three to five years.

Nectar: Yeah, that's interesting Chris. I like how, you position it as a, as this new infrastructure that doesn't exist yet and then, you'll be able to, create APIs and all sorts of cool hooks. And then how do you like your current ICP today? My understanding is like other bio biotechs in essence, right?

So I'm not sure what size, but let's just say biotechs. Like how do you think about time to value for them, right? So it's like, how soon from implementation and then what is the metric they look at? Is it just like time to market for their drug? Curious to understand the value for your customers.

Chris: Yeah. Yeah, a hundred percent. In terms of the customers Bob Box works with today, we work with stealth stage C-level biotechs all the way through to enterprise pharma. And in terms of time to value, how we operate is we usually run a 12 week pilot process. Now, the goals can vary in between, and the metrics of what it looks like for success depends on the companies, but by and large, it's typically around how much busy work can you automate away.

How much less time am I spending on data wrangling, building reports? How many more hypotheses could I test per unit time? Some initial benchmarks, right? Some customers of ours increased throughput of hypothesis testing by up to a hundred x, right? Within 12 weeks we were able to get three targets nominated, going into ime, enabling studies, those types of ROI and.

In terms of what is the kind of key thing that they're looking for is really, it comes down to automation. You'll be surprised to find that, up to 30% of a translational biologist time is spent building these target reports and compiling this information for a weekly meeting. This is that, PowerPoints in a SharePoint somewhere all the way down is just piles up and a lot of that is.

Is a challenge when you have scientists leaving programs and, jumping between different companies. They take that procedural knowledge with them, and a big challenge is how do you stop this knowledge attrition, right? If everyone has to go back and rediscover, or re relearn or repu together reports it's huge waste of time.

And through our work with our initial set of a dozen customers. Putting together all of these reasoning models really allowed them to even onboard new staff for some of our seed stage companies to get up to speed very quickly. And we, that's an area that we focus a lot on and we love working with our customers on, is helping them recover that time from a drug perspective, this takes about five to 10 years almost to get a drug into the market.

It's hard to. It's hard to see that all the way through and say, look, we saved you X amount of years. But what we do know is in that early stage where we participate in that preclinical stage, we were able to recover six to eight months just from the target ID and identifi in indication selection process, which is massive.

Nectar: Yeah. Already. Yeah. Time is money. So it's, I think the way you describe it makes a ton of sense. And then I guess it begs the question then, how do you then price it? Is there anything funky in terms of your business model or is it just a classic like SaaS way to do it?

Obviously you follow in this new bucket of AI tools, right? But we're just curious to hear about, the business model side. 

Chris: Yeah. So it depends. But by and large it's seat related. So we do it on a per seat basis. Everyone loves on-premise deployment, VPC. So we charge a deployment fee.

We charge by seats, and depending on the needs there'll be different amounts that we required. But we try to keep our pricing as simple as possible. That way it's not a headache for procurement. And yeah. 

Nectar: Yeah. And then what about the like the actual usage side? Do you have to like, embed teams on your customer side?

So make sure that they're using the software the right way? Like the onboarding piece, is that is that where you see objections? Like maybe, I guess my real question is like. Where you're seeing friction today in terms of like the actual selling is because, oh, this is so new and like we've already done it this way and we're not convinced, is it other areas where you're getting a little bit of pushback and then how are you solving for that pushback?

Chris: I think there are three areas that we are seeing friction and the strategies that we have in place to overcome that. The first is we are in some ways introducing net new tools. One of the things that comes with that is there is a resistance, not because they don't like new things. If you tell them you can solve a problem for them, that's gonna be great.

But when they see, oh, there is a huge lift to this process, I gotta develop this ontology with you. I gotta get all my data in place and be able to set all this up first. There's an activation energy there. So when we work with customers in our pilot process, we almost pro become like an embedded data team.

We help you curate that ontology, and this is that service that we provide exclusively during that pilot process in the future, right? We want to be able to automate that as much as possible too. So one of the areas that we pride ourselves on is customer support, customer service, and we have a dedicated engineer that will work with you during that time to help you overcome that lift to get that en that data in.

The second friction point that we see happening is. When you influence drug program decisions, they touch a lot of stakeholders. It's not just your computational teams. It's not just your data teams. It's not your AI ML teams. It's a little bit of everybody. So when you need to align everyone on the same page, there's a little bit of friction in terms of this is crossing a lot of different functions.

It's a pretty complex sales process, and I think what has been very powerful for us there is being incredibly intellectually honest with these teams about what it is we can and can't do, where we are developing, just so that they have an understanding of how it looks like when it gets deployed. Of course it helps to have existing case studies and big time logos on your product.

But at the end of the day, I think when we're talking to these stakeholders, they're taking a bet on us to implement this new technology before they, bring it to a. Their bosses or to scientific leadership, we need to be very clear and upfront with timelines, expectations, and goals. And where we quantify that so that they know going in what to expect.

And I think the last challenge is around.

How to talk to the stakeholders in terms of allocating budget, because it's not cheap, I'll say. And so when you're a, when you're sitting on the other side as a customer and say, look, we have proficiencies, right? We do target identification as a company, we do, we build software as a company. We have all these people, we're Johnson and Johnson, or we're Pfizer.

Why use this? Amount of money and give it to a vendor when we can just spend it ourselves and build it, right? This build versus buy friction point. And so it's incredibly important to communicate with customers that. This is not replacing a core pro proficiency of yours, but this is an enablement, right?

We are building tools for your teams to use, because I don't think there's a single pharma company that will be like, I'm gonna trust the target that came out of XY Z's platform. When I don't know what's happening under the hood. No one's gonna bankroll that project. But when you've put it this way, you say, look, I'm, I talked to the AI scientists, I talked to the ML scientist.

These are tools that they're using to accelerate their workflows. You're not gonna re-engineer your own IDE if you're a programmer, that kind of scenario. So there is something to be said about building tools in the life sciences space where you do really need to work with your customers and understand the end users as well because of the way that they think about build versus behind core proficiencies.

Nectar: Yeah. Yeah. There's so many interesting insights there to unpack. Chris, you mentioned, the challenge of, the IT challenge, right? Which I think everyone faces. But then there's also the novelty around what you're doing, right? It's like this notion of a scientist with a hundred x companion basically being able to do more, the vision is pretty actually, maybe, yeah. Maybe before we go to the genesis, I wanna go to the b broader idea and the vision, right? Because it's like you're collecting so many interesting data points. I imagine over time there's some, network advantage and flywheel of, Hey, we work with a thousand biotechs.

Here's what we'll be able to learn and teach you. Is there a broader vision? And is there a way that you can articulate it? And at least for what you have in your mind now. 

Chris: Yeah, you just said a very dangerous sentence. So biotechs and pharma do not share data with each other, and it's very important that we also tell them there is no kind of collect, we don't collect this data.

All the data ownership is theirs, because that's, again, the asymmetry of the information. That's where their advantage is.

Yeah. Biopharma is very sensitive about the data that they use and traditionally they don't share data.

And it's very important to tell our clients and also to be very clear that we don't store their data. They own all of their data as well. And in terms of where we see the vision of what we're building our goal is more to build the operating system essentially to help them close the loop and fully automate the scientific process.

Now it starts with being that data and logic layer, but where we wanna build to is evolving into the operating system for the modern biopharma enterprise. 

Nectar: Yeah, it sounds like an endless opportunity. 

Chris: Yeah. And then in the near term, we're proving out already that Bob box is massively enabling for early discovery, doing biological interpretation tasks there, things like target ID indication selection, but along the way, like the path to the clinic, there is a series of many decisions, right?

We're just in the opening innings. So for example, the chemistry that comes after the trial designs, et cetera. And at Bob Walks, we intend to support those two as we scale. But right now in the near term, focusing on the early discovery part of the journey, 

Nectar: can you maybe walk us to the beginning of what, like why do this?

Like why do this like thing that doesn't exist that seems that seems very perilous and fraught with danger to create something that doesn't exist clearly, like you've hit upon a value prop. I think that makes a ton of sense, but. You're also operating in an industry that has, at least maybe this is my external perspective, we could talk about the industry.

It feels difficult to get into right. In terms of like new entrants. So yeah, maybe start with a why, and then think about okay your plan to like shape the world the way you want to. Yeah. 

Chris: Yeah, I could start with how we started Bio Box. Where we came from.

We were, myself and my co-founders were academics at first. About a decade ago, I met my now co-founders in grad school. So we were running the bioinformatics group for our research department, and pretty much on a daily basis, we worked with tons of brilliant scientists. They had the most in depth and nuanced understanding of disease biology.

They were asking incredibly deep, thought provoking questions. Brilliant intuition. But the problem was biology was already becoming rapidly digital. This means new tools and new skills were needed to develop for these scientists to keep pace. And that lag of being able to answer those questions, brilliant questions that they had because there was no tools was.

An early motivation for, to break out of what we were doing and to build the analysis infrastructure. 'cause at the end of the day, there, there are real patients and real consequences that happen when. Pharma RD slows down and our mission from day one has been always to help scale up scientific reasoning.

We're delivering on that mission today, but it wasn't always the case that we were building this type of Symantec technology. At first, we were building a lot of bioinformatic pipelines and analysis and cloud workflows visualizations, and I think. What we came to realize was that it goes back to what does it mean to help those scientists ask those questions is now we're at a place where there's lots of great tools in the market to run these workflows.

Cloud is becoming more and more accessible. If you look at the problem space, it's goes back to how are we able to help these scientists capture the reasoning. That goes, that's the most important part of that cycle and and where we folks spend a lot of time building our technology today. Yeah. 

Nectar: WW it sounds like I was gonna ask the question on what excites you the most, about what you're building. And it sounds if maybe my reading is like what you just said, right? It's like building this thing where you've taken the step back, you've really thought about it from first principles, and you're like, Hey the reasoning thing is where we can create a ton of value.

And that's, I think there's a, on, like the part in the business buzzword blue Ocean, right? Is that accurate? Like, how would you maybe respond.

Chris: Yeah. I think that specifically when it comes to reasoning, I think any pharma, even if you're like an early stage biotech, digital biology or building data infrastructure is going to be part of your plan. I think historically we think of drug companies. They imagine lab rats and pipettes and tissue culture.

But again, the transformation, especially in the past two, three years, digital biology, computational biology is a core discipline no matter where you go. What gets interesting is when you start colliding the world of biology in the world of technology together. And so we've gotten really good at building data infrastructure for, storing data, but we don't do enough work capturing context.

That's where that Blue Ocean opportunity is because it's never been easier for you to launch, bi tools or vibe code analysis together. But the scientific reasoning is what sets these pharma companies apart, and if you can operationalize that. You can see massive gains. And at the end of the day, from a business perspective, the closer you are to influencing drug program decisions, the more important those decisions is where you can make money.

And these pharma companies, they have a lot of money. So you know, making workflows X percent more efficient or making data a little bit faster to retrieve, like those are great cost savings, but it's not that attractive thing that they're looking to really innovate on. But instead of, if we focus on how do you launch more drug programs, right?

How do you be more confident about the decisions you're making? How do you build on the shoulders of giants and keep growing that asymmetric advantage that you have? That's the area that a lot of pharmas spending a lot of energy and resources in developing. 

Nectar: Yeah, I can understand why now people don't wanna share data.

Like to your point of like secure data and it's, yeah. It's really you have almost like keys to the kingdom, right? If you, if it were, if what you're, what you guys are building works, it's all the secret sauce is in there. 

Chris: Oh, a hundred percent works. It works. So we're like in the early stages, right?

Yeah. But yeah, that's that's where we're building to and we're excited. 

Nectar: Yeah. That's awesome. Maybe give us a quick sense you mentioned it works, so a quick sense attraction, just some data points and where you guys are at today. 

Chris: Yeah, so we work with again, customers from early stage biotechs all the way through to enterprise pharma.

We're actually seeing a lot of inbound traction now. I think the. Pursuit of these reasoning engines is becoming more and more mainstream. I think as we're seeing a lot of companies look to build out their own agent systems, this is that super supercharging infrastructure to help them get a little bit more smarter or their agents to get a little bit more smarter from a traction perspective, we've worked with a dozen customers. We've doubled our revenue numbers in this first half of 2025. And we're currently. Fundraising to accelerate our growth of market. We literally don't have enough hands on keyboards to take on new deals.

And we're working now with bigger and bigger enterprise level customers, so we're really excited about the growth for the second half of this year. 

Nectar: Yeah. Yeah. Very cool man. Very cool. You, maybe a last talking point, wanna talk about biotech, right? And I'll share maybe my perspective to start.

It's as a new vc, it's I feel oh shit, biotech, or, healthcare in general, it's stay away. It's feels complicated, right? My guess is you probably agree with that assertion that, it feels like the unloved industry, like in tech. But at the same time it's just massive market, right? It's we'll go into some of the numbers, right? But there's proof that there can be outsized returns. So again, a two part question is like. What is your answer? When you're pitching you're, you mentioned, fundraising. What is the answer to, to that objection?

Oh, biotech seems complicated, VC doesn't understand, and then second is, what's the current state of biotech in Canada? Should we be more proud, like what's going on? 

Chris: Yeah, so lots to unpack there. I'll start with, now there's a lot, I, this is not investment advice. I'm industry adjacent to biotech, so we have some visibility.

What has been challenging, I think, for the industry over the past five years is, it's market dynamics, so there's been a lot less. Novel drug programs being launched, and a lot of now Me Too programs in the sense that the most popular one is let's say GLP inhibitors, right? It's scientifically de-risked.

Biological risk is gone. There's an approved therapy, and then you're competing for, better delivery or better safety, or better efficacy, different formulations, whatever it is. Then now there's a lot of commercial risk with that. So you're always in this ebb and flow between, platforms that can launch novel drug programs and then platforms that are, launching more efficacious therapies, et cetera.

I think we're starting to see the pendulum swing the other way, especially when we go low level and look at the work that we're doing with Biopharmas there. It's a big push towards novel biology, and I think that's where investors get a little spooked sometimes is, there's a lot of risk in biotech, and like you said, there's huge outsized returns if you win.

And so when you're looking at the bets, you really want to get a sense of, again, is the decision for these programs? Well-reasoned, is it well principled, right? Do I trust the science and the quality of the science coming out of these companies? And that's your only real aperture into likelihood of success.

So there's a great presentation, but I think by RA capital that kind of gave a formula for like how you appreciate how look at the risk factors for biotech. And obviously it's a volatile sector, but I think there is a growing kind of push towards being able to quantify the scientific uncertainty and the commercial uncertainty and and de-risk some of those investment decisions Now.

Working with these companies, I think that it's important for vendors to start looking to support enterprise customers. The reason being is for us anyways, in the early stage discovery, they have evergreen discovery programs. They're always looking to launch new drugs, but with biotech is a little bit phasic, right?

So sometimes in the early days you capture them. Then they only have so much capital, they're gonna have to push that in towards their clinical studies and they might shut down their discovery engines, et cetera. I think that moving up market is critically important for vendors in the space. But biotech at large, pharma at large yeah.

You're really betting on the science and the ability for these research teams to back up, their decisions.

Nectar: Like I find it was it's a good insight into, like the whole industry, right?

In that essence, it's it's also like you're just building a software team, like a software platform today, right? You're not trying to build a new drug, like you're gonna, you're gonna empower those people doing that, right? So to your point of being adjacent, and then what's your take on.

Biotech in Canada? Yeah. What is it a sector that you see, a lot of your peers working in and building stuff? I know we have some of the big pharma companies here, but, yeah. Curious as what's your, as someone that's in the trenches building, what, what's going on?

Chris: Yeah, so we have some of the best AI scientists and drug discovery translational biology research centers in the world. Here at home in Toronto where we're based out of. And I think that Bio Canadian biopharma is definitely on the rise. We have some epic companies here that are a, like leading that AI drug discovery, like Deep Genomics based outta Toronto.

And protein Cure genomic ai, and I think that we don't. We're not loud enough as compared to our colleagues down south. We're not as boisterous and out there. And I think that we're doing ourselves a disservice. 'Cause we work with clients across the range from, from Silicon Valley biopharma to the, the hubs in Bo in Boston.

And I think the Toronto science scene is just as competitive. Ju putting out incredible research, has incredible talent and I think that there's a lot of opportunity here. From a investment perspective for potential VCs to really focus to, to take a look under the hood, what the biotech scene in Canada's looking because we have a blend of.

Just in a couple kilometer radius of our offices here vector Institute, we have, the new the research centers all around the Discovery District, Mars, and it creates this. I guess like this space for exciting conversations and company collaborations to collide and develop new products.

And that the talent that's coming out from the Waterloo area, from the, from U of T is just incredible. So if we can foster that and galvanize, the commercialization side, the biotech RD side, the investment side, I think Canadian pharma biopharma could be a serious contender on the world stage.

Nectar: Yeah. Yeah. Amen. Amen. Hopefully you keep fighting that good fight and keep pushing. Maybe a last question, Chris, is okay, so it's been of years you're running this startup. What surprised you the most in the journey of, being co-founder, CEO of this company, 

Chris: surpr, what surprised me the most.

Nectar: Or a key learning, something that you find is oh, like this lesson that's now been like tattooed into your brain. 

Chris: Yeah. Okay. Okay. This might be a little bit of a controversial take. I had to unlearn a lot of habits I had as an academic to make it in business. So one of the bad habits that I took was you.

You have this tendency to overanalyze and you wanna have the perfect idea and the perfect go-to market. And 'cause that's, we don't wanna share this until we're like intellectually convinced that this is all gonna work out, and then nine times outta 10, it's gonna be wrong. In the startup world, your initial business idea is gonna be wrong.

Your go to market's gonna be wrong. Your projection's gonna be wrong. It took me a little bit of time to get comfortable with that idea of it's okay to be wrong. You'd rather be wrong fast and really early and that's totally fine as long as you have a plan for like contingencies. That was a big learning that I had that was really surprising is sometimes you just need to just do more reps.

Try as many new things and then see what happens. See what the market tells you. Compared that to back when we were in academia, you had to be very certain before you write that paper or, do that meeting or et cetera. And business is all about execution, right? We're building the startup. It's all about how much can you get done per unit time and some of that.

Philosophy really I guess that's a core philosophy at our company is it's all about productivity and I think it's creates a very interesting parallel when we talk about our scientists that we work with is for them. On the commercial side, it's all about productive science. How many more hypotheses can I test?

It's fine to be wrong. So that was a very interesting learning going from academia into startup world. But other than that, it's just in incredibly hard everything that we thought of. Oh, how hard could it be? Yeah, it's really hard doing customer discovery, doing, finding the right people to talk to in biopharma, learning how to sell software, learning how to.

Build relationships. These are things that are massively important for survival and to, to really, thrive as a startup. And I, I'm grateful that we had great mentors along the way. People who believe in the vision to support us, great institutional backing, Mars and UT here.

Gotta give them a shout out. And these are just like lessons you learn along the way, but it's like things you can't read in a textbook. 

Nectar: Yeah, there's, yeah, there's only one way to do it, right? You gotta learn it and it's the hard way. I very much appreciate that feedback, Chris. I can't think of a better way to, to end the conversation, man.

And be respectful of your time. Maybe final closing question. People wanna follow the journey from where you guys at to like big multinational bio, software company. What's the best way? 

Chris: Yeah, the best way is, find our website, bio box.io. You can also find us on Twitter, on LinkedIn.

Feel free to reach out directly to me on all the socials. For Twitter, it's at Bio box under Sir Chris. On LinkedIn. Just find the company page, especially if you're a scientist in biopharma and you're interested in learning more. Would love to find a time to chat with you. We are also fundraising right now.

So if you're a VC that's interested in this space and would like to learn more, happy to. To do a call and do an intro together. 

Nectar: Yeah, Chris, thanks