From Montréal, a new generation of founders is rethinking how scientific research gets done, accessed, and commercialized.

For the founders behind Montréal-based LabGiant and Caveat, the opportunity is to modernize some of the systems that sit behind scientific discovery. While researchers and universities are producing world-class work, accessing shared infrastructure and determining which inventions are ready for the patent process can still be slow, fragmented, and costly.

LabGiant, co-founded by Chen Li, David Gu and Andrew Wu, is building the operating layer for shared research infrastructure, helping researchers find and book equipment, services, lab space, and expertise while helping universities and research facilities make better use of their resources.

Caveat, founded by David Gu and born out of the LabGiant experience, is taking on another challenge within the research ecosystem. Its AI-powered platform provides pre-filing patent examination, helping universities evaluate inventions, assess prior art, and understand commercial potential before committing significant time and resources to the patent process.

Together, the founders are tackling different stages of a larger challenge: how can technology help more scientific ideas move from the lab into the world?

In this Emerging Founders Feature, Chen Li, David Gu, and Andrew Wu share what led them to build their companies, the challenges of building at an early stage, and their vision for modernizing the research and innovation ecosystem from Montréal and beyond.

Chen Li | LabGiant

1. What are you building, and what problem are you trying to solve?

LabGiant is the operating layer for shared research infrastructure. Core facilities and service platforms run their operations on it, and researchers use it to find and book what they need: instrument time, a service run for them, lab and bench space, or the person who actually knows how to do the thing.

Hundreds of world class facilities sit inside universities that almost nobody outside their own floor knows about. Almost every director we talk to wants more usage, and a lot of them specifically want clients from outside academia. Meanwhile the companies that would happily pay for that work have no idea these places exist.

2. What led you to start working on this idea?

I'm a chemical engineering PhD candidate at McGill, so I ran into this myself well before it was a company. Wasted a lot of time getting hold of instruments and expertise that already existed a couple buildings over. Once we started asking around, turns out most scientists have a version of that story.

Nobody had built it though, and there's a good reason for that. Fifteen years ago everything else got matched up online. Rides, apartments, freelancers. Science sat that one out. A ride is one variable. Booking instrument time, or a service, or a room at the right biosafety level, means training records, approvals, safety, and whether the thing can even do what the experiment needs. Way too messy to build back then. AI is what makes it doable now.

3. What has been the biggest challenge so far as an early stage founder?

The stuff that doesn't scale. For a long time, growth meant knocking on facility doors and onboarding their instruments and services into the system by hand, one at a time, because nobody joins an empty platform.

It's where the product came from but glad that phase is over.

4. Where are you currently focusing your time and effort on building or growth?

Most lab software still feels like 2004. People running million dollar instruments through interfaces they'd never put up with anywhere else in their life.

Which is why this era is fun. Building good software got a lot cheaper, so we put all of it into how the thing feels to use. That's been the growth too. We build something scientists like using, they show their labmates, and their facility comes with them.

5. What does success look like for you over the next year?

A facility manager we've never met, at a university we've never visited, running their whole week on LabGiant without ever talking to us. That's when it's a product and not a sales effort.

And organic signups from outside Canada, especially the US.

David Gu | Caveat, LabGiant

1. What are you building, and what problem are you trying to solve?

I'm building Caveat, a pre-filing patent examination. A researcher drops in a raw invention write-up, and we run the same gauntlet the patent office will run: the prior-art search, a simulated rejection, a read on whether anyone would actually pay for it. This way, you know what you have in days, before a dollar is spent filing.

The problem is simple to state. Inventing got fast. Owning didn't. US universities report about 26,000 invention disclosures a year and file on roughly 14,400 of them. The other half sits on a shelf. Not because the science is weak, but because finding out costs around $20,000 and six months, split across three vendors who never talk to each other. Universities don't under-file because their inventions are bad. They under-file because evaluation is too expensive to run on everything. We're making it cheap enough to look at all of it.

2. What led you to start working on this idea?

Caveat came directly out of LabGiant, our first company. LabGiant is a shared lab equipment marketplace, live across a dozen Canadian universities. Building it put us in the middle of how academic science actually runs, and one pattern kept showing up: researchers create enormous value and capture almost none of it. LabGiant went after the access half of that problem. Caveat goes after the ownership half.

The specific spark came from a senior patent agent we work with. She told us her real job is translation. Researchers describe inventions in science language, patent offices deal in law language, and she gets pulled in so late that the file-or-don't decision has already been made on instinct. That stuck with me. If you move the translation to the front, the decision gets made on evidence instead. That's the product.

3. What has been the biggest challenge so far as an early-stage founder?

Bravery. Not the dramatic kind, the daily kind. Early-stage feels like wandering through fog. Advice comes at you constantly, some of it great, some of it terrible, and in the moment the two look identical. You can't connect the dots looking forward, so most days you're running on faith that they'll connect looking back.

What I've learned is that bravery isn't something you have, it's something you borrow. Some of it comes from a sense of duty to the mission. The rest comes from the people around you: mentors, early believers, the team. When my conviction runs low I lean on theirs, and they lean on mine. And when I look back at older versions of myself, I can see how deluded I was about certain things. The more I learn, the more complicated everything turns out to be. Strangely, that's comforting. It means you're moving.

4. Where are you currently focusing your time and effort, building or growth?

Both, honestly. On the building side, AI has collapsed the cost of the manual work inside patent evaluation, and our goal isn't to bolt a tool onto the old pipeline. It's to rebuild the pipeline around what's now possible. We're lucky to have early partners on both sides of the decision, universities and patent firms, sharpening that with us every week.

On growth, the next push is getting Caveat into the hands of more tech transfer offices and law firms across North America. We're also preparing a pre-seed round this fall to fuel that.

5. What does success look like for you over the next year?

I believe revenue follows clarity. When someone hears what the product does and the reaction is "obviously, why doesn't this exist yet," sales take care of themselves. So the first goal is to earn that reaction consistently.

From there, close the pre-seed and bring Caveat to five universities and five law firms within the year. That feels achievable. The bold version is ten and ten, and I'd rather aim there!

Andrew Wu | LabGiant

1. What are you building, and what problem are you trying to solve?

LabGiant is the operating layer for shared research infrastructure.

What it solves is the part of a researcher's week that isn't science. Tracking down whether the instrument you need already exists two buildings over. Approvals. Booking. Reconstructing what happened in an experiment from a note in a margin. Nobody got into research for any of that, so we let scientists do more science.

2. What led you to start working on this idea?

My bachelor's is in biochemistry. I left science to become a software engineer, but the love for it never left.

So when Chen described a marketplace for research, it landed on someone who had been waiting for a reason to come back. Then I looked at the software scientists actually use day to day, and I was genuinely surprised by how outdated it is.

That gap is the whole company for me. I couldn't be the one doing the science. I can be the one who makes the doing of it better. I want scientists to enjoy their days more and spend them on the fun parts.

3. What has been the biggest challenge so far as an early stage founder?

The number of hats. I came in as an engineer and I've had to learn to sell, to operate a company, to sit on a call with a facility director in the morning and be back in the codebase in the afternoon.

It has been incredibly fulfilling and took building to another level. You write different software after you've watched someone appreciate and enjoy the app. You return that appreciation into higher quality work.

4. Where are you currently focusing your time and effort on building or growth?

The app, and specifically how it feels to use. Scientists are precise people. They notice sloppiness instantly, and if a product feels careless anywhere they'll assume it's careless where it counts.

5. What does success look like for you over the next year?

When I start seeing researchers and labs register on LabGiant every single day.