SLAS Europe: Meet the Startups Driving Lab Innovation

From AI-enabled drug discovery and laboratory automation to advanced cell models and high-throughput screening platforms, the tools available to researchers today are more powerful and interconnected than ever before. Yet many labs still face persistent bottlenecks in scalability, reproducibility, data integration, and workflow efficiency.

For emerging companies, these challenges also represent an opportunity. Startups are driving innovation by developing specialized technologies designed to solve highly specific pain points in research and lab operations.

This focus on innovation is reflected in the Innovation AveNEW program at the Society for Laboratory Automation and Screening (SLAS) Europe 2026 conference. The initiative supports emerging startups working across laboratory automation and life sciences technology, giving selected companies the opportunity to showcase their platforms to an international scientific community.

“SLAS has always prioritized providing life sciences technology start-ups with the invaluable market access, user feedback, and business counsel to grow and scale their business,” said Vicki Loise, chief executive officer of SLAS. “Supporting innovative companies with these critical tools and services is a key part of the SLAS mission to catalyze multidisciplinary innovation across the sector.”

The participating companies are also eligible for the SLAS Ignite Award, which recognizes outstanding scientific and commercial potential among the Innovation AveNEW cohort. The winner is announced during the conference.

To explore the problems these startups are aiming to solve—and why they believe now is the time to strike—Technology Networks asked several Innovation AveNEW companies the following question:

What major challenge in today’s life sciences or laboratory environment is your company solving, and why is now the right time to address it?

Garry Pairaudeau, PhD. Chief executive officer, DaltonX

“Molecular complexity is increasing rapidly as we seek to modulate biology in ever more sophisticated ways, from glues and degraders to complex multi-specific antibodies. At the same time, the industry is energized by the application of AI to deliver computationally-enabled solutions to these challenges. However, many organizations remain constrained by siloed data and fragmented tool stacks that limit the real-world impact of AI on their pipelines.

“Our self-learning, agentic AI software is transforming drug discovery by streamlining how decisions are made, delivering cumulative learning across entire pipelines, solving complex optimization problems, and eliminating unnecessary experiments and their associated costs.”

Kris Philippe, BSc, BA, MBA. Chief business officer, Ensynble

“Manual work being done in the labs is currently a black box, but it is a data-rich environment. People are still using pencil and paper to write down observations of critical findings. New technicians need to shadow senior scientists to be able to replicate processes. Every time a process is transferred from one person or organization to another, high costs arise, and there is a real risk that the partner cannot replicate the process with full compliance.

“ARGO is opening up this black box through vision AI and augmented reality. Now, lab scientists can frictionlessly capture the value they are creating. They can seamlessly add media-rich context to their workflows, and other scientists can see what they are doing more easily.

“The time to address this was yesterday—or, failing that, as soon as possible. The longer companies wait to unlock the value in their physical data, the further behind they will fall. There are substantial cost savings from reducing training time, manual data entry, and failed experiments. And then there is the larger picture: the growing need for real-world video data to train models that enable physical AI and human‒humanoid collaboration.”

Salvatore Azzollini, PhD. Chief operations officer and co-founder, Lutèce Dynamics

“We are targeting one major challenge in cell biology: how to image dense tissues without perturbing them. With the advent of 3D cell culture models, such as organoids, spheroids, etc., and their establishment as a major tool in drug discovery, there is a growing need for microscopy modalities that allow to image these structures in their entirety (500 µm–1 mm) with precision, which is exactly what our product VertX is doing.

“A further push from institutions like the US Food and Drug Administration (FDA) or European Medicines Agency (EMA) for new approach methodologies (NAMs) as animal-free alternatives in drug discovery is underway lately; hence, we feel this is the right moment for our breakthrough technology to address this market.”

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Zehra F. Nizami, PhD. Head of business development & scientific partnerships, PartitionBio

“The challenge is fundamental and longstanding. Getting biological cargo reliably into cells is one of the most critical bottlenecks in life sciences research, drug discovery, and the development of cell and gene therapies. The dominant tools available today—lipid nanoparticles and viral vectors—come with significant limitations. Lipids can be toxic to cells, are temperature sensitive, require complex preparation, and struggle with certain cargo types and difficult cell models. Viral vectors, while effective, are expensive, complex to manufacture, have cargo size limitations, and carry immunogenicity risks.

“The timing is right for several reasons. Cell and gene therapy is no longer a niche field. It is a rapidly expanding clinical and commercial reality, with growing demand for better, simpler, more versatile delivery tools across the entire pipeline from basic research through to manufacturing. At the same time, the scientific understanding of liquid-liquid phase separation and biomolecular condensates has matured enormously in recent years, creating the theoretical and practical foundation for a genuinely new approach. BubbleFect is that approach; non-lipid, non-viral, temperature stable, cargo agnostic, and simple to use.”

Hür Köser, PhD. Founder and chief executive officer, Scalables

“What we’re solving is the gap between manual lab work and traditional lab automation. In many life science environments, the existing options are still too expensive, too rigid, too bulky, and too hard to reconfigure for the way modern labs actually operate. Researchers need to move quickly, adapt workflows constantly, and generate proof-of-concept data fast, but they often do not have the budget, bench space, or in-house automation expertise to support that with conventional systems. That is the gap Scalables is addressing with Daisy: affordable, modular, plug-and-play automation that can scale and evolve with the lab.

“Why now? Because the pressure on labs has never been higher to do more with less, and AI has finally become practical enough to lower the skill barrier to automation. At the same time, experiments are changing faster, teams are leaner, and people want systems that can be set up in minutes or hours, not weeks or months. So, this is the moment when modular hardware and AI-assisted software can come together to democratize lab automation in a way that simply was not realistic before.”

Adrien Rennesson, MSc, MBA. Chief executive officer, Syntopia

“In vitro models that aim to reduce or replace animal testing have been discussed for years. Recently, however, regulatory agencies such as the FDA and EMA have accelerated the push toward NAMs that can truly integrate into the drug development pipeline. While the scientific community has long recognized their potential, adoption at scale has been slower than expected.

“At Syntopia, we believe the hesitation does not come from a lack of interest. Drug developers want more predictive, physiologically relevant models. The challenge has been practical: many proposed alternatives are complex, require additional equipment, demand more biological material, or disrupt established laboratory workflows. We are addressing this gap by offering a solution that increases physiological relevance through controlled 3D perfusion, while remaining simple to implement in a standard laboratory environment. Our platform is designed to fit seamlessly into existing workflows, reduce biological material consumption, and generate more robust and reproducible data.

“The timing is perfect because the industry is no longer asking if alternatives to animal testing are needed, but which solutions can realistically scale. We believe the future belongs to technologies that combine scientific rigor with operational simplicity.”