By Autumn Demberger, Global AgInvesting Media

Rainbow Crops, a pioneer Agtech company that engineers complex traits to develop resilient, high-performing crops, closed an $11.25 million seed round backed by a mix of institutional VCs, corporate arms like Corteva Catalyst and Paulig, and the Gates Foundation. CEO and cofounder Giacomo Bastianelli shares insight into the company’s strategy, its founding, and his own connection to crop sustainability into today’s world.

Global Ag Investing: How did you find yourself gravitating toward agribusiness, and then, by extension agtech? 

Giacomo Bastianelli: I came to agriculture almost by accident. My background is in pharmaceutical biotech and bioinformatics, so I originally approached biology through the lens of human health and drug discovery. 

During the last year of my PhD, around 2009, I was helping the newly formed European life sciences investor Kurma Life Science Partners identify and evaluate technologies and patents coming out of academia. One of the technologies we looked at was targeted recombination in yeast. At first, it was not an agriculture story at all. But as we explored the biology, we realized there could be very powerful applications in plants, particularly in how recombination could be used to improve crop breeding. 

That work eventually led to the creation of Meiogenix in 2010, a company focused on targeted meiotic recombination in crops. That was my entry point into agtech. Since then, I have become increasingly convinced that agriculture is one of the most important areas where advanced biology can have real-world impact. The challenges are enormous, but the leverage is also enormous: if you improve the genetics of a major crop, the impact can scale across millions of hectares and people. 

GAI: What inspired you to co-found Rainbow Crops? What was the definitive moment you realized this technology needed to move from the lab or concept phase into a commercial venture? 

GB: The definitive moment came when I saw the results in maize generated by the researchers Dirk Inzé and Hilde Nelissen at the VIB. The results were very compelling scientifically, but also very practical. They showed a powerful way to combine genome editing with breeding to generate and screen many genetic combinations, rather than focusing on one gene at a time. 

That was important because many of the traits agriculture needs most — yield stability, drought tolerance, heat resilience, input efficiency — are not controlled by a single gene. They are complex traits, shaped by networks of interacting genes. The original BREEDIT work demonstrated that multiplex genome editing could be used to create large, diverse populations of edited maize plants and identify promising combinations for complex traits. 

When I saw that, I felt this was more than an elegant academic technology. It had the potential to become a platform. At the same time, major seed companies were showing strong interest in collaborating with VIB around this approach. That combination – strong scientific validation, a clear market need, and industrial pull — made it clear that the technology should move into a dedicated company. 

That is how Rainbow Crops was born. We built the company around what we now call Trait Foundry: a platform that combines AI, multiplex genome editing, breeding, and automated phenotyping to rationally design, build, and test genetic diversity for complex crop traits. 

GAI: Can you share where the name “Rainbow Crops” stems from? 

GB: There are three reasons/layers to the name. 

The first is historical. One of the most iconic examples of biotechnology in agriculture is the Rainbow papaya in Hawaii. In the 1990s, papaya ringspot virus was devastating Hawaiian papaya production. Scientists developed a genetically engineered papaya resistant to the virus, and Rainbow papaya was commercialized in 1998. It became a powerful example of how plant biotechnology, when applied responsibly, can help protect farmers and preserve a crop. 

The second layer is technological. A rainbow is made of different colors, and for us each “color” can be seen as a genetic edit or genetic variant. A single color is interesting, but the full effect comes from the combination. That is very similar to how we think about complex traits. 

The third layer is cultural. The rainbow also symbolizes openness, diversity, and a progressive mindset. Science should be rigorous, but it should also remain open-minded. In biology, it is easy to become attached to established dogmas or single explanations. We want Rainbow Crops to reflect the opposite attitude: curiosity, diversity of thinking, and the willingness to test bold ideas with data. 

GAI: In your words, why is crop improvement such an important area to be concerned about? What’s happening that crop stability is necessary? 

GB: Crop improvement is becoming more important because agriculture is being asked to do several difficult things at once. We need to maintain or increase productivity, reduce environmental impact, and adapt to a climate that is becoming more volatile. 

For farmers, the issue is not only average yield. It is yield stability. A variety that performs well in a normal year but collapses under heat or drought is a major risk. Climate change is increasing the frequency and intensity of stresses such as heatwaves, droughts, floods, and disease pressure. The FAO and WMO recently warned that extreme heat is already threatening the livelihoods, health, and productivity of more than a billion people connected to agrifood systems, and that for many major crops yield declines begin above 30°C. 

This is exactly where genetics matters. We need crops that can maintain performance across variable environments. But this is biologically difficult because stress resilience and yield are deeply interconnected. A plant can become more stress tolerant, but sometimes at the cost of growth or productivity. The challenge is to find the right balance: crops that can withstand stress while still delivering value to farmers. 

That is why Rainbow Crops focuses on complex traits. Heat tolerance, drought resilience, input efficiency, and yield stability are not single-gene problems. They require understanding and optimizing networks of genes. This is where we believe multiplex genome editing, AI, and high-throughput phenotyping can open a new chapter in crop improvement. 

GAI: Given current supply chain volatility and rising input costs, what specific vulnerability is Rainbow Crops solving for growers? 

GB: The vulnerability we are addressing is unpredictability. 

Farmers make decisions before they know what kind of season they will face. They choose seed, fertilizer strategy, and crop management plans without knowing whether the year will bring drought, heat, excessive rain, disease pressure, or a combination of these. That makes farming a very risky business. 

At the same time, input costs are volatile. Fertilizer is a good example: the World Bank reported that its fertilizer price index rose by more than 12% in the first quarter of 2026, reaching the highest level since 2022 in March 2026. When inputs are expensive, farmers have less room for error. A crop that fails under stress is not only a biological problem; it is an economic problem. 

Rainbow Crops is trying to reduce that biological risk by developing crop genetics that are more resilient across multiple stresses. The goal is not to give a farmer a “drought crop” for a drought year and a different “flood crop” for a wet year, because farmers cannot predict that in advance. The goal is to help create genetics that perform more reliably across volatile conditions. 

Advanced genetic engineering gives us a way to approach this differently. Instead of waiting for useful combinations to appear naturally, we can rationally design genetic diversity, test many combinations, and identify those that improve resilience without sacrificing yield. For growers, the long-term value is more predictable harvests, better return on inputs, and greater resilience in a more unstable climate. 

GAI: What role do you foresee AI playing as your company continues to grow? 

GB: AI is central to how Rainbow Crops works, but we see it as part of an experimental biology platform, not as a standalone software layer. 

In our workflow, AI helps us at several levels. First, it helps prioritize which genes and genetic combinations are most relevant for a trait. We integrate biological knowledge, genomic data, functional data, and phenotypic data to generate hypotheses about which edits could improve performance. 

Second, AI helps us design better experiments. In complex trait engineering, the number of possible genetic combinations is enormous. You cannot test everything. AI helps narrow the search space and identify the most informative combinations to build and test. 

Third, AI helps us learn from every experimental cycle. Trait Foundry™ is designed as a closed-loop platform: we predict, edit, grow, phenotype, analyze, and then feed the data back into the model. Over time, our proprietary data should make the system better at predicting which combinations are most likely to work. 

This is very different from using AI only to analyze public datasets. The value comes from connecting prediction to real plants, real phenotypes, and ultimately field performance. That is the loop we are building. 

GAI: You just closed an $11.25 million seed round backed by a mix of institutional VCs, corporate arms like Corteva Catalyst and Paulig, and the Gates Foundation. What are some of your initial milestone priorities for this seed capital? 

GB: The seed round gives us the resources to move from early validation into systematic deployment of the platform. The round raised €9.7 million, approximately $11.25 million, and will support the further development of our technology platform, expansion across multiple crops, and growth of the team. 

Our first priority is team building. This is a multidisciplinary company, so we need strong people across plant science, genome editing, breeding, AI, data science, and project execution. We have nearly completed our recruitment, and I feel fortunate to be surrounded by such a talented team. I am excited about the impact we can make together in agriculture. 

The second priority is delivering on our first co-development partnerships. We want to show that Trait Foundry can generate value not only as an internal platform, but also as a collaborative engine for seed companies and breeding partners working on high-value traits. 

The third priority is technology maturity. That means strengthening our multiplex editing capabilities, improving our phenotyping and validation workflows, and expanding the quality and scale of the data that feed our AI models. 

Finally, we want to continue moving toward field relevance. Greenhouse and controlled-environment data are essential, but agriculture ultimately happens in the field. Our ambition is to connect rational design, biological validation, and field performance into a platform that can repeatedly generate better crop genetics. 

GAI: Is there something about plants or crop science that you think people often underestimate? 

GB: I think people often underestimate how sophisticated plants are. They may look passive, but they are constantly sensing and responding to their environment — heat, drought, light, nutrients, pathogens — through very complex genetic networks. 

People also underestimate how complex crop genomes can be. The human genome is already enormous, with about 3.2 billion DNA letters. Maize has a genome of roughly 2.3-2.4 billion base pairs, so it is in the same order of magnitude as the human genome. Bread wheat is even more striking: its genome is around 14-16 billion base pairs, several times larger than the human genome. 

So when we talk about improving crops, we are not dealing with simple systems. We are dealing with very large, dynamic, and highly interconnected biological systems. At Rainbow Crops, we are trying to understand and guide those systems rather than treating traits as simple on/off switches. That is what makes the science difficult, but also very exciting. If we can learn how to tune those genetic networks in the right way, we can help crops become more resilient without asking farmers to carry all the risk of climate volatility. 

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