When we started Adaptyv a few years ago, our core belief was: AI models for biology are only as good as the experimental data they're trained on and the hypotheses they can test in the real world.

Think about it this way - imagine your AI generates code for you but you could never actually run it. How would you know if the model is doing a good job? You can look at the code, check that there are no obvious errors, but you wouldn't really know if it works until you compile and run the code.

Now when you’re designing proteins, the equivalent to compiling and running your code means to make and test the proteins in the wet lab. Everyone can now run code thanks to AWS, Vercel, Modal and a million other cloud platform (or just on your local device) — but do you have a wet lab? Probably not.

So the choice here is either to build your own lab or work with so called Contract Research Organizations (CROs) that will set up experiments for you. If you go the lab route, you're looking at millions in equipment costs, hiring molecular biologists, and spending months developing protocols that might not even work. If you go the CRO route, you're sending emails back and forth comparing quotes, waiting 6-8 weeks for results, and when the data finally comes back, you're not entirely sure how they ran the experiments or why half your proteins didn't express. And then you need to iterate and do it all over again: More emails, more Teams meetings and less time to actually design proteins.

That's been the reality for protein designers. You design something incredible on your computer, and then you wait months to know if it actually works.

We saw that the ability to generated experimental data was the bottleneck to enabling a million people to design new proteins using AI. That's why we went after the hard and unsexy problem - building a fast, reliable, automated lab.

Now, after a year of working with many great partners, we’ve scaled our infrastructure to the point that we're now open to anyone who wants to use our platform!

Today we're dropping the "beta" tag from Adaptyv and launch our new website…

Where we are now

The main thing to understand about AI protein design is: The field is moving incredibly fast.

To give an idea: Last year’s Protein Design Competition brought together 150+ designers and we tested 600 proteins in our lab. Within just the two months between the two rounds, the hit rates went from 2.5% to 13%. Check out our series of blog posts or the community-written preprint about the lessons learned if you want to dive deeper!

And as the models are getting better, a massive design space is opening up, meaning even more proteins to be tested in the lab. Teams are designing de novo binders against every target they can think of. We’ve seen dozens of labs use BindCraft and test their designs in our lab — with success rates that people just 1 year ago were dreaming of. It is now possible to build novel biosensors like we demonstrated in our MBP sensor case study. Cradle is routinely optimizing antibodies to improve affinity, like they showed in our competition and their subsequent lab validation with Adaptyv. Microsoft Research validated their new model EvoDiff with us, Escalante just released Adaptyv data to demonstrate their new framework for multi-objective protein design and Chai Discovery recently released their zero-shot design model Chai-2, benchmarked using our lab.

Overall, this year, over 30 companies started using Adaptyv to validate their protein designs - from some of the biggest pharmas to frontier AI labs to many seed-stage techbio startups. We've run hundreds of experiments, tested well over 10,000 proteins this year and are generating the data that validates the best AI models currently in development. More than 10 preprints have been published in 2025 using Adaptyv data and more are currently being written.

Our team has more than doubled in the last 6 months and we just moved into our new lab and office in Lausanne to have more space to expand. There's a lot to build when you're trying to make biology work more like software.

This is also the right time to announce that we raised an $8M seed round led by Ace Ventures earlier this year, with previous investors ByFounders and Founderful doubling down and LongGame and many great angels joining new. This lets us build faster, hire the right people, and scale our infrastructure to handle this massive demand. A big thanks to everyone that has been believing in us since the beginning!

What’s coming next

The real metric that matters to us is this: Every week, people are uploading new protein designs to our platform. New companies get started and test their proteins with us. People who otherwise wouldn't have been able to validate their proteins can now do it thanks to our platform. That's what we're building for.

So here’s what happening:

• Making the lab faster, cheaper, better - We're pushing hard on all three. Lower costs through automation and scale. More assay types for multi-modal AI models. And we're working toward bringing the turnaround time down even more.

• Alpha test our API - Biology should be as programmable as any other API. Connect your protein design agents directly to our lab. Run automated design-test-learn cycles and let your models order their own validation experiments.

• More open source data, more competitions - We're building something new for the protein design community to share designs, data, and insights. And there will be more competitions too. Stay tuned, we’ll release something cool soon!

• Join the team - We're hiring across bioengineering, software engineering, and lab automation. Also looking for our first non-engineering roles to help scale operations and onboard protein design teams. If you think biology should work more like software development, come work with us.

The protein design revolution is happening now. We're making sure everyone can participate.

• Julian for Team Adaptyv