Head of Finance
About the job
- Several years of finance experience at a startup that scaled, ideally with early-stage experience establishing the finance function.
- Ability to build a financial model linking operational metrics, including throughput, yield, capacity, and headcount, to cash.
- Ability to write code, with SQL as a minimum and Python ideally; coding is stated as a hard requirement, alongside API-based systems and automation.
- Understanding of the cost structure of a physical business, including reagents, instruments, consumables, capital expenditure versus cost of goods sold, and yield loss; lab, hardware, or manufacturing experience translates, while pure SaaS finance experience alone does not.
- Swiss and European finance experience, including VAT, social charges, and employment law; experience with a US entity alongside a Swiss one is a plus.
- Trustworthiness in handling salaries, revenue, investor communications, and board materials.
- Build and own the forecast, from lab capacity, throughput, and price per protein as well as top-down P&L, burn, and runway.
- Own gross margin analysis, including experiment costs for DNA, reagents, consumables, instrument time, and labour, and identify unprofitable products and customers.
- Own pricing with the commercial team, determining what to charge, why, and the pricing floor.
- Own the close, including group consolidation and intercompany management, and make it fast and accurate.
- Own cash across bank and card accounts in Switzerland and the US, accounts payable runs, accounts receivable collection, and payment timing.
- Manage tax, VAT, audit, payroll, and external firms supporting compliance.
- Support investor reporting and fundraising, and manage treasury.
- Manage equipment leasing and assets, including lease-versus-buy decisions, financing, balance-sheet treatment, and maintaining the asset register as the company expands.
- Automate finance processes and systems.
- Build the company's AI-powered finance stack, extending programmatic ledger, billing, card, and payroll workflows so the model, close, cash view, and margin analysis largely run automatically.
- Work with procurement on costs; procurement sourcing and vendor relationships belong to a separate hire.
- Experience with a US entity alongside a Swiss entity.
- Python programming experience.
Adaptyv is building an automated lab that lets AI agents run biology experiments.
We're entering the era of agentic science where AI models can now design novel proteins, propose hypotheses, and iterate on experimental results. But they can't run the experiments themselves - that's still a manual, months-long process. We're building the infrastructure that gives AI agents access to the physical world.
We are one of the fastest growing biotech companies, trusted by leading biopharmas, frontier AI labs, and the techbio companies pushing the field forward. This is a rare chance to help advance some of the most important work happening in biotech today.
Our automated lab is powered by a deep software + hardware stack: lab instruments worth millions of USD reverse-engineered into API-controllable hardware, dozens of devices orchestrated through complex workflows, full observability on everything that happens in the lab, processing pipelines for messy physical-world data, and AI systems that troubleshoot production results and accelerate assay development.
We’re growing rapidly and are hiring for talented people to scale and support the massive demand for AI-driven wet lab experimentation.
About the Role
We are building the most automated lab in the world, and that does not stop at the bench. A company that AI agents can run experiments in has to automate everything around the experiment too — procurement, hiring, admin, and finance. Finance is one of the most automatable functions in any company and almost nobody has actually done it. We intend to.
That is the job: build an AI-powered finance stack. Our ledger, billing, cards and payroll all expose APIs, and we already close the books against them programmatically rather than by hand. You would take that much further, to where the model, the close, the cash view and the margin analysis largely run themselves, and your week goes on the decisions instead of the reconciliation.
You would be our first finance hire, and it is hands-on. You will build the forecast, run the close across two entities, own cash, and be the person who can say what a protein actually costs us to make.
What You'll Do
Own the forecast. Both directions: bottom-up from lab capacity, throughput and price per protein, and top-down P&L, burn and runway.
Own gross margin. What does an experiment actually cost us — DNA, reagents, consumables, instrument time, labour? Which products and which customers lose us money?
Own pricing, with commercial. What we charge, why, and where the floor is. Pricing at a lab company is a finance question wearing a sales hat.
Own the close. Group consolidation and intercompany management. Make it fast, make it correct, make it boring.
Own cash. Bank and card accounts across Switzerland and the US, AP runs, AR collection, payment timing. Know where the money is and where it is going before anyone asks.
Keep us compliant. Tax, VAT, audit, payroll, and the external firms we work with. You are not a lawyer or an accountant, but you know when to call one.
Investor reporting, fundraising support, and treasury management.
Equipment leasing and asset management. The lab runs on millions of francs of instruments. Lease or buy, how we finance it, what it does to the balance sheet, and keeping the asset register honest as we expand.
Automate all of the above.
What We're Looking For
You have built a finance function, not just worked in one. Several years in finance at a startup that actually scaled, and ideally you were early enough to have set things up rather than inherited them.
You can build a real model. Not a template. A model that ties operational reality — throughput, yield, capacity, headcount — to cash, and that survives someone pulling on it.
You write code, or you are visibly about to. SQL at minimum, Python ideally. This is the hard requirement most finance candidates will fail, and we mean it. We run on Claude Code across the company, and every system finance touches has an API behind it. The best version of this person automates their own function, writes their own tooling, and finds leverage the rest of us have not thought of. If your instinct is that finance is a spreadsheet-and-portal job, we will frustrate each other.
You understand the cost structure of a physical business. Reagents, instruments, consumables, capex versus COGS, yield loss. Lab, hardware, or manufacturing backgrounds all translate. Pure SaaS finance does not, on its own.
Swiss and European finance experience. VAT, social charges, employment law. A US entity alongside a Swiss one is a plus.
Ambitious, and in it for the company. We want someone who intends to be running finance at a much larger Adaptyv in three years, and who cares about what we are building rather than the title on the door.
Trustworthy with everything. Salaries, revenue, investor communications, board material. All of it crosses your desk.
Scope
We are a small company and nobody here has clean edges around their job. You will end up in the lab, in a room with a customer, and every so often doing something that is nobody's job simply because it needs doing.
The centre of gravity is the numbers: the model, the close, cash, margin, pricing, and the board pack. That is the part that is yours and the part we will judge the role on. Procurement is a separate hire and the two of you will be joined at the hip on cost, but the sourcing and the vendor relationships are not yours to run.
One thing it is not: a bookkeeping seat. The mechanical layer is handled, and if that is the job you want, this one will bore you.
Why This Role Is Interesting
Most first-finance-hires at a startup spend three years cleaning up a SaaS P&L. Here, the finance question and the product question are the same question: an automated lab is a machine for converting money into experimental data, and how efficiently it does that is literally the business. You will be building the model of that machine, at a company that is about to expand it several-fold, while it is being used by the best pharma companies and the best AI labs in the world. And you will do it with a founding team that will hand you the API keys and get out of your way.
Application deadline
We are reviewing applicants on a rolling basis.
About the company
Adaptyv Biosystems builds an automated, AI-native wet lab for protein design validation. Users submit digital protein sequences via web or API, and Adaptyv synthesizes DNA, expresses proteins in a cell-free system, and measures binding with SPR to produce experimental data at scale. The service provides an end-to-end workflow with API integration to AI and drug-discovery tools, delivering validated results. Its aim is to close the gap between AI-designed proteins and experimental data, increasing capacity and enabling development across modalities for customers from frontier AI labs to large pharma.
Company Size
11-50
Company Stage
Series A
Total Funding
$48.5M
Headquarters
Lausanne, Switzerland
Founded
2020
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Simplify's Take
What believers are saying
- Adaptyv raised $40 million on August 25, 2026, led by Highland Europe.
- Over the past year, throughput rose more than 5x and customers exceeded 100.
- The company plans triple capacity by end-2026 and expand into developability and harder modalities.
What critics are saying
- Roche, Novo Nordisk, and AI labs can internalize validation once workflows mature.
- London's Q4 2026 launch and 60-person scaleup strain execution across automation, software, biology.
- If Anthropic, Twist, or cloud labs commoditize design-validation loops, Adaptyv becomes replaceable infrastructure.
What makes Adaptyv Biosystems unique
- Adaptyv's in-house DNA, cell-free expression, and SPR deliver closed-loop protein validation.
- By September 2026, its API plugs into Boltz, Chai, Cradle, Tamarind, Latent Labs, Benchling.
- Proteinbase and the Anthropic partnership validate 5,000 designs openly, building proprietary experimental data.
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Benefits
- Hybrid Work Options
Growth & Insights and Company News
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0%Google DeepMind's Nature study on watermarking ai-designed proteins validated with Carterra HT-SPR. New paper published in Nature shows watermarks can be embedded in AI-generated proteins without compromising function, confirmed with binding kinetics data from Carterra's LSA-XT Salt Lake City, Utah, 12 October, 2026: Carterra(R), a leading provider of high-throughput surface plasmon resonance (HT-SPR(TM) platforms for antibody and small molecule drug discovery, today highlighted the role of its LSA-XT platform in new research from Google DeepMind published in Nature. The paper, "Function-preserving watermarking of AI-generated proteins" introduces SynthIDBio, a family of methods for embedding provenance signatures in AI-designed protein sequences and AI-predicted structures. Google DeepMind scientists collaborated with Adaptyv Bio, which used Carterra HT-SPR technology to measure whether the watermarked proteins still bind their targets as well as unmarked designs. Proving that function survives For a watermark to be useful in biology, the protein must still bind its target. That can only be confirmed in the lab. Researchers measured the binding kinetics and affinities of hundreds of AI-designed proteins against three therapeutically relevant targets: the SARS-CoV-2 receptor binding domain, VEGF-A, and PD-L1. The study also describes SynthIDBio-structure, a finely tuned version of AlphaFold 3 that embeds an imperceptible watermark in predicted biomolecular structures with negligible loss of accuracy. This proof of concept has potentially meaningful applications in biosecurity and scientific integrity. Another AI leader needs binding data Google DeepMind joins a growing list of AI leaders, including Anthropic, whose work depends on experimental binding data to confirm designs and to train the next generation of models. In AI drug discovery, algorithms propose candidate proteins and antibodies in days, which moves the bottleneck from design to experimental validation. The emerging answer is "Lab-in-the-Loop" (LitL): AI models generate candidates, high-throughput experiments test them, and the resulting data feeds back to sharpen the next round of designs. Carterra's HT-SPR platforms sit at the center of that loop. Combining proprietary microfluidics with real-time HT-SPR, they deliver up to 100 times the throughput of traditional label-free platforms while using a fraction of the sample, producing the kinetics and affinity data that tell researchers whether a design works and teach models how to do better. Industry analysts have taken notice. A July 2026 Leerink Partners report named Carterra a core enabler of the roughly $7 billion Lab-in-the-Loop market, and William Blair analysts have identified Carterra as a winner in life science tools for AI drug discovery. Carterra's technology also underpins collaborations such as Twist Bioscience's work with AWS Biodiscovery and Anthropic's autonomous protein design study. "Adaptyv's role is to turn AI-designed proteins into real experimental data, quickly and at scale," said Julian Englert, CEO and co-founder of Adaptyv Bio. "We use Carterra's HT-SPR for campaigns like this across hundreds of designs. It gives us real binding kinetics and affinity data, not just a yes/no answer - which is what lets the AI model learn and improve the next round of designs." Investing to keep pace with AI Carterra continues to expand its platform to meet the demands of AI-driven discovery. The Carterra Vega(TM), launched in February 2026 as the industry's first 48-channel HT-SPR system, enables primary screening of more than 20,000 small molecule interactions per day. Carterra also announced recently a collaboration with AstraZeneca "to develop hardware and software aimed at advancing lab-in-the-loop drug discovery." "When Google DeepMind, Anthropic, and other leaders in AI all need the same thing - high-quality binding data at scale - it says a lot about where drug discovery is headed," said Tim Germann, Chief Commercial Officer at Carterra. "The impact of our technology in this new era is driving explosive growth for Carterra, but just as important, we are helping advance the field of AI drug discovery by supplying the all-important data layer."
£6.2m project targets lithium-sodium separation. 09 Oct 2026 A £6.2 million research project led by the University of Birmingham is developing engineered protein membranes designed to separate lithium from sodium, targeting a key challenge in producing high-purity lithium compounds for battery manufacturing. Funded by the Advanced Research and Invention Agency (ARIA), the three-year project brings together researchers from the University of Birmingham and Aston University with industrial partners Fold9, Evove and Adaptyv Bio. The consortium aims to develop large-area membranes containing uniformly sized pores capable of distinguishing between lithium and sodium ions, which have similar chemical properties. The initial application will focus on direct lithium extraction, while the longer-term objective is to create a manufacturing platform for highly selective separation materials. The research forms part of ARIA's Universal Fabricators programme, which aims to use engineered proteins as programmable building blocks for manufacturing materials with precise geometries. Project lead Dr Dominik Kubicki, from the University of Birmingham, said: "Biology can construct ordered materials with precision that is extremely difficult to achieve using existing manufacturing methods. We aim to harness this to create membranes with uniform, programmable pores." The team will work with S-layer proteins, which naturally form highly ordered two-dimensional lattices on the surfaces of many microorganisms. Thousands of protein variants will be designed and screened before promising candidates are produced at larger scale and incorporated into membrane systems. Jude Wells of Fold9 will lead AI-guided protein design, while Evove will provide membrane manufacturing, scale-up and testing expertise. Adaptyv Bio will support high-throughput screening of protein variants. Professor Tim Knowles, project co-lead at the University of Birmingham, said: "S-layer proteins offer an extraordinary starting point for materials engineering because they can spontaneously assemble into highly ordered structures. By combining computational design, structural biology, and experimental screening, we aim to reprogramme these natural building blocks to perform demanding technological functions." The project aims to demonstrate continuous membranes with precisely controlled pores measuring approximately 1 to 50 ångströms. Cryo-EM and neutron reflectometry will be used to examine membrane structures at nanoscale. Beyond lithium extraction, the technology could have applications in critical-mineral recovery, water treatment, chemical manufacturing and pharmaceutical manufacturing. Image: Back row (standing, left to right): Tim Overton (Chemical Engineering, University of Birmingham); Richard Logan (Biosciences, University of Birmingham); Paul Topham and Matthew Derry (Institute of Membrane Excellence, Aston University); Steve Smerdon (Department of Cancer and Genomic Sciences, University of Birmingham); Owen Thomas (Chemical Engineering, University of Birmingham). Front row (seated, left to right): Pooja Sridhar (Biosciences, University of Birmingham); Tim Knowles (Biosciences, University of Birmingham); Dominik Kubicki (Chemistry, University of Birmingham). Credit: University of Birmingham.
Highland Europe leads EUR 40m Adaptyv Series A. #Adaptyv Bio #Highland Europe #Series A #AI biotech #Switzerland venture capital Deal at a glance. * Type - funding · Series A * Enterprise value - €40M * Original amount - EUR 40M * Target - Adaptyv Bio * Acquirer - - * Investor - Highland Europe, Ace Ventures, ByFounders, Y Combinator * Sector - Healthcare * Region - - * Announced - - Deal-ID: MMN-000915 Key facts. * Buyer - Highland Europe, Ace Ventures, ByFounders, Y Combinator * Target - Adaptyv Bio * Sector - Healthcare * Geography - - * Deal volume - €40M * Date - - Adaptyv Bio's EUR 40 million Series A is a bet on scaled, automation-heavy R&D infrastructure rather than point solutions. Highland Europe led the financing, with ACE Ventures, byFounders and Y Combinator participating, backing a Swiss company positioning itself as a full-stack platform for AI-enabled protein engineering. The round was recently announced. Terms beyond the headline amount were not disclosed. Why this deal, why now. European venture funding has tilted sharply toward AI-native businesses, and investors are increasingly concentrating capital into fewer, larger platform plays. Multiple 2026 European VC data points put AI at more than half of the region's venture capital deployment in H1 2026, with one report citing 55% of all VC capital and another pegging AI at 60.3% of deal value. Against that backdrop, Adaptyv's pitch fits the current underwriting: build automation and compute-enabled workflows that can compound over time, then sell outcomes or capacity into large end markets. Adaptyv is described as an automated lab focused on AI-driven protein design and validation, with the Series A earmarked to expand a full-stack platform for protein engineering and AI-enabled biology. Company and ecosystem context. Adaptyv is Switzerland-based, with reporting linking it to the Biopôle/Vaud cluster around Lausanne. Swiss biotech coverage in 2026 has highlighted continued momentum in national hubs such as Basel and Lausanne, and Adaptyv's ability to attract a globally recognised investor set reinforces that Switzerland remains on the map for AI-biotech formation. The investor mix matters. Highland Europe's lead, alongside ACE Ventures, byFounders and Y Combinator, signals international syndication around a Swiss deep-tech life-science asset. For founders, that typically translates into a higher bar for execution and a clear expectation of scaling beyond a local market. Strategic lens: platform build vs tool sprawl. Adaptyv's stated use of proceeds points to a classic platform build-out: integrating wet-lab automation with AI models and validation loops to shorten iteration cycles in protein engineering. In life sciences, where data quality and experimental throughput can be gating factors, the value proposition often hinges on owning the full feedback loop rather than operating as a software layer on top of third-party labs. That strategy is increasingly aligned with where venture dollars are flowing in Europe. Reports citing AI capturing EUR 26.5 billion in H1 2026, and mega-rounds in broader AI infrastructure, underscore a market preference for scaled infrastructure over fragmented tooling. For AI-enabled biology, "infrastructure" can mean lab automation, workflow orchestration, proprietary datasets, and model-training pipelines that are hard to replicate. Integration and execution questions. This is a funding round, not an acquisition, but execution risk still sits in integration across disciplines. Key questions investors will track include: * Systems integration: how tightly Adaptyv can link automation, data capture, model training and experimental validation without creating bottlenecks. * Leadership depth: whether the company has enough senior operators across biology, automation engineering and ML to scale the platform in parallel. * Go-to-market focus: whether the initial commercial motion targets services revenue, platform access, partnerships, or co-development, and how that choice affects margins and scalability. * Customer concentration and churn risk: if early revenues come from a small number of pharma or biotech partners, retention and repeatability will matter as much as topline growth. Repeat backing across seed and Series A, as reported in coverage, suggests continued investor conviction in the build plan, but it also raises the bar for demonstrating measurable throughput gains and differentiated outcomes. What to watch next. * Hiring pace in automation engineering, protein engineering and applied ML, and whether Adaptyv adds senior commercial leadership. * Evidence of platform repeatability: published throughput metrics, cycle-time reductions, or validated performance improvements in protein design. * The commercial model: services-heavy early revenue versus a more productised platform approach. * Partnerships with pharma, CDMOs, or research institutes that can scale data generation and validation. * Follow-on financing signals, including whether the next round tilts toward growth equity as European capital continues to concentrate into fewer AI winners. Companies & investors in this story. Sources.
Adaptyv raises $40 million to build the automated AI lab for agentic biology. 27 August 2026 Key Takeaways * Adaptyv raises $40M led by Highland Europe to scale an automated wet lab for AI-driven protein design. * The startup closes the bottleneck between AI-designed proteins and real lab testing with cell-free synthesis and robotics. * It will launch London lab in Q4 2026, doubling the Lausanne team, and powering agentic biology. AI can now design thousands of protein molecules in an afternoon. But it's yet to find a way to determine if they actually work. Most labs were built to test 20 designs, not thousands of them. For this reason, Adaptyv raises $40 million to build an automated lab that lets AI design, test, and learn from proteins at scale. AI is faster than the lab. AI has made protein design easy. Tools like AlphaFold, Boltz, and Chai can generate thousands of candidates for a drug target in hours. The problem is what comes next. In traditional drug discovery, you hand those designs to scientists who order DNA, grow cells, produce the protein, and run binding assays. That process takes weeks. It's also expensive and built for a world where you only needed to test a handful of molecules. Today's AI models can spit out two thousand designs for one target. Wet labs simply weren't designed for that volume. From digital sequence to lab data. Lausanne-based Adaptyv Bio announced a $40 million Series A. The round was led by Highland Europe, with existing investors Ace Ventures, ByFounders, and Y Combinator doubling down. Ace Ventures also led the $8M Seed in late 2024 and called Adaptyv the solution to the gap between AI design and wet lab validation. Adaptyv has built what it calls "an automated, AI-native lab." You start with a digital protein sequence and end with real experimental data, without a scientist touching a pipette. The core of it is cell-free protein production. Instead of engineering living cells to make proteins, Adaptyv uses just the biochemical machinery needed to turn DNA into protein. What makes it "agentic" is the API. Customers can submit designs through a web platform, but they can also connect directly through an API. That means AI agents can design a protein, send the order to Adaptyv's lab, get results back, and use that data to design the next round, all without human intervention. The API is already integrated with tools like Boltz, Chai, Cradle, Tamarind, Latent Labs and Benchling. Adaptyv is scaling to power agentic biology. The new $40M will fund three priorities. First, building out more automated lab infrastructure to handle higher throughput. Second, doubling the team in Lausanne to support more customers and faster turnaround. Third, opening a new office and lab in London in Q4 2026 to be closer to European pharma and AI customers. Adaptyv is also investing in community. It launched Proteinbase, a competition platform that attracted 680 participants and 10,000 protein designs in a single competition. The goal is to crowdsource hard protein problems and generate more public training data for the field. This funding is about closing the loop between AI prediction and experimental validation. AI systems can propose experiments, run them, learn from the results, and propose the next ones, all autonomously. Adaptyv is building the infrastructure layer to make that real. Switzerland is positioning itself at the center of this shift. The country now has over 55 VC-backed TechBio startups, with a concentration of AI-driven protein engineering companies around Lausanne and Zurich. With this Series A, Adaptyv is betting it can become the default lab backend for that ecosystem. I'm a crypto writer with 4+ years of experience passionate about turning big, technical ideas into content anyone can understand. From blockchain to stablecoins to everything in between, I enjoy helping readers stay informed in a space that never stops moving. Disclaimer VentureBurn is a media platform covering the latest in cryptocurrency, artificial intelligence, venture capital, and the startup ecosystem. Opinions expressed on VentureBurn are for informational purposes only and do not constitute investment advice. Before making any high-risk investments in digital assets or emerging technologies, readers should conduct their own due diligence. All transactions and financial decisions are made at your own risk, and any losses incurred are solely your responsibility. VentureBurn does not endorse or recommend the buying or selling of any digital assets and is not a licensed investment advisor. Please note that VentureBurn may participate in affiliate marketing programs.
Lausanne-based Adaptyv raises €34.35 million Series A to build the automated lab for agentic biology. August 27, 2026 Adaptyv Bio, the Lausanne-based BioTech startup building an automated wet lab for AI-driven protein design, has raised a €34.35 million ($40 million) Series A round. The round was led by Highland Europe, with existing investors Ace Ventures, ByFounders, and Y Combinator doubling down. Ace Ventures led the company's €6.8 million ($8 million) Seed round in late 2024. "AI has made it easy to design proteins. Many teams can now create thousands of candidate molecules for a target in an afternoon. Finding out whether those molecules actually work is still the hard part. That requires a wet lab. But most wet labs were built for a world where scientists tested twenty designs per target, not two thousand. Adaptyv closes this gap," ACE Ventures said. Founded by Julian Englert and Daniel Nakhaee-Zadeh Gutierrez, Adaptyv has built "an automated, AI-native lab" where the process starts with a digital protein sequence and ends with real experimental data. DNA is synthesised in-house, proteins are expressed cell-free, and binding is measured using SPR with built-in quality control. Customers can submit their designs through a web platform, or through an API, allowing AI agents to order their own experiments directly. When ACE led Adaptyv's Seed round in November 2024, the startup had only nine people and a lab that was beginning to see commercial demand. Fast forward to 2026: the team has tripled, revenue has grown roughly 10x, the company has brought DNA synthesis in-house, scaled lab automation, and even launched its Proteinbase competition platform, attracting "680 participants and 10,000 protein designs to one competition." Adaptyv has also shipped a public API now integrated with Boltz, Chai, Cradle, Tamarind, Latent Labs and Benchling. It now counts over 100 customers, including frontier AI labs, top 5 pharma companies and AI-native drug discovery startups. The company recently ran a benchmark with Anthropic, sending 16 targets from its public design competitions to Claude Science and testing the resulting sequences, anonymised. According to the company, 95% of the 1,320 designs were expressed. "Out of 1,320 designs, 354 bound their target, giving Claude an average hit rate of 26.8%. Only a single target yielded 0 binders. Compared to the results of our Protein Design Competitions, Claude's designs had noticeably higher success rates and would have won 5 out 6, yielding tighter binders," reported the company The test showcased how Adaptyv is "becoming the place frontier labs go when they need real-world proof". The company noted that Google DeepMind runs a similar validation loop, Chai Discovery used Adaptyv to validate its Chai-2 zero-shot antibody release, and Roche and Novo Nordisk run therapeutic design cycles roughly 4x faster than their internal pipelines. The new capital will fund two types of growth: horizontal expansion of lab capacity and vertical expansion into harder biology problems. On the horizontal side, the startup plans to roughly triple its lab capacity by the end of 2026 and keep growing through 2027. On the vertical side, it intends to move beyond binding measurements, which is a market estimated to be worth "a few hundred million dollars a year", into developability and full biophysical characterisation, next-generation modalities such as peptides, ADCs and degraders, and eventually cell-based function. For the horizontal expansion, Adaptyv Bio has announced a new lab and office will open in London in the fourth quarter of 2026, alongside an expansion in Lausanne. The company plans to grow its team from 25 to around 60, with a focus on production, automation and software. For its founders, the long-term goal is to build a biological gigafactory with enough automated experimental capacity for AI to learn from physical reality as quickly as it learns from text. Adaptyv's goal is for lab throughput to no longer be the constraint on how fast biology can progress.