Q&A

AvatarsBio is currently a Biotech x AI concept lab based in the Netherlands. We build modular digital twins and AI assistants that help scientists plan, simulate, and interpret experiments — faster, safer, and with less waste.

Think of it like an art gallery for scientific ideas. We create and display early prototypes — small, exploratory tools — to test concepts and gather feedback before turning them into full products.

Some remain as open exhibits in our Sandbox; others evolve into validated digital twins or research tools. We plan to build with scientists, not just for them.

Each digital twin represents a virtual avatar of a living model or process — for example, of a real cell — designed to support decision-making before lab studies, not replace it.

AvatarsBio was founded by a scientist-engineer with 20+ years of experience across the life science value chain bridging organ-on-chip R&D, bioproduction, data workflows, and automation. We’ve seen how fragmented experimental planning can be and build tools that make reasoning more transparent, not more opaque.

We currently develop and test early-stage scientific tools, some of which are minimally functional but usable in our Sandbox. We aim to refine the most promising ones into validated products through co-development or pilot ideas with researchers and industry partners, while keeping part of our ecosystem open for exploration and responsible innovation.

We’re also open to selective consultancy or advisory collaborations — feel free to reach out through our LinkedIn page.

There are many definitions, but to us, it’s a virtual model of a biological system — such as a cell or organ — built from curated data. It helps scientists explore hypotheses safely and efficiently before performing physical experiments.

Currently, they’re experimental and educational tools — designed for planning and hypothesis generation, not for clinical or regulatory use.

Yes, in hybrid form. LLMs handle reasoning and explanation, while domain-specific models handle data and logic. This keeps results biologically grounded while benefiting from natural-language reasoning.

Yes — we use AI to assist with coding, mainly in Python, to accelerate prototyping and improve consistency. But all code is reviewed, tested, and understood by us before it’s deployed — not just auto-generated autonomously. We use AI to code smarter, not blindly.

You can — if you have the time, data, and infrastructure. But maintaining a reliable scientific tool is harder than it looks. An LLM alone isn’t enough for scientific reasoning or reproducibility.

AvatarsBio integrates curated datasets, domain logic, and continuous updates so you don’t have to worry about upkeep or changing AI models. It’s science you can trust, without the maintenance burden.

It means scientists remain central. The AI supports pattern recognition and planning, but final judgment always rests with the human expert.

That’s a fair — and healthy — question. No digital twin may reproduce every interaction inside a living system. But that’s not the goal, nor is it necessary in our view. The value lies in simulating specific aspects to plan and understand better, not everything.

We also often see a gap between what in vitro complex systems can generate and what our analyses can interpret. For example, organ-on-chip technologies can mimic flow, tissue architecture, and cell-cell communication — yet their data are often analyzed as if they were simple cell cultures. It’s like building a Formula 1 car and judging its performance by checking the tire pressure.

Consider something as common as a cell-viability assay. In a 3D spheroid or perfused chip, the result isn’t just “are the cells alive?” but also whether and how much of the dye or compound even reaches the inner core. What looks like reduced viability may actually reflect limited penetration or transport dynamics.

Digital twins help bridge that gap. They let us explore patterns, test ideas, and refine experiments before returning to the lab — turning data into insight. In a way, traditional cell models are just partial simulations of biology, capturing aspects such as toxicity or permeability. Digital twins simply extend that idea into the virtual space, where learning happens faster and with less waste.

Antonii — our experiment-planning assistant based on Design of Experiments principles.

Ana — our sample-size and power-analysis assistant with clear, plain-language explanations.

More coming soon.

Yes. Sandbox versions are free for personal or academic use. Future professional editions will include advanced features and integrations.

Absolutely. Try the Sandbox versions and share feedback. You can reach out via LinkedIn.

No. Our public demos don’t store, track, or share any personal information. If you use an AI feature, only the question you type is sent securely to the AI service so it can generate an answer — nothing else is kept, reused, or used for training once the session is closed.

Yes. Our tools are classified as minimal-risk systems and are developed with transparency, safety, and human oversight by design.

Responsibility is built into our design, not added later. We aim for AI that assists reasoning — not automates judgment.

It means tools should adapt to scientists, not the other way around. We design assistants that simplify and make use of human reasoning rather than overwhelm or rely entirely on large language models.

Because transparency builds trust. The Sandbox lets people see how ideas evolve, contribute feedback, and understand how responsible AI can grow in science.

We prefer clarity over noise. Our focus is on science, not scroll effects. Your time shouldn’t be spent on auto-playing videos or endless motion — and honestly as well, we’re bootstrapping our budget.

To be a wisdom company. To make science faster, smarter, and resource-efficient but human in consideration — by combining trusted data with human-centric AI that helps researchers reason with data as naturally as they think. Less weekends in labs, more family time.