Ramiro Olivera started with horse cloning. Now, as CEO of Calice, he's selling a digital twin for corn and soybeans. The Argentine startup's platform, NODES, uses AI and computational biology to simulate how new seed varieties and crop inputs will perform across thousands of potential field conditions, a process it calls a virtual field trial. For agricultural R&D departments, the promise is straightforward: run more experiments in silicon, and fewer in dirt [calice.ai/eng/about].
The Computational Wedge into Ag R&D
Calice's product targets a specific, expensive bottleneck. Developing a new commercial seed trait can take a decade and require planting thousands of physical test plots across multiple geographies and seasons. The company's software aims to compress that timeline by modeling the complex interactions between a plant's genetics (G), the local environment (E), and farm management practices (M). By ingesting genetic data, soil maps, and decades of historical climate information, the platform predicts yield and quality outcomes before a single seed is sown [calice.ai/esp/]. The commercial claim is that this can reduce the need for physical testing by up to 80%, though that figure originates from the company itself [calice.ai/eng/about]. The initial wedge was crop biotechnology and gene-editing work, which later pivoted to the more scalable software model for virtual trials [startupslatam.com].
A Team Built for the Hybrid Problem
The founding team reflects the hybrid nature of the problem, blending deep biology with operational scale. The technical core is a partnership between CSO Esteban Hernando, a molecular biologist with a decade of CONICET research experience who built the biological modeling framework, and CTO Andrés Rabinovich, a physicist and data scientist with twenty years of software engineering experience [calice.ai/eng/about]. They are flanked by operators with proven scale: CEO Olivera previously founded an equine-cloning venture, and COO Pablo Romero managed 120-person teams across Latin America for Mercado Libre [calice.ai/eng/about]. The most telling commercial hire is Alejo Valverde, the Chief Commercial Officer who spent eleven years at Monsanto, where he led the Argentine launch of the INTACTA RR2 soybean trait [calice.ai/eng/about]. His presence signals a focus on navigating the procurement cycles of the very multinationals Calice needs as customers.
| Role | Name | Key Background |
|---|---|---|
| CEO & Co-Founder | Ramiro Olivera | Founder of Kheiron (equine cloning); BSc in Biotechnology |
| CSO & Co-Founder | Esteban Hernando | PhD in Molecular Biology; 10 years at CONICET/Instituto Leloir |
| COO/CFO & Co-Founder | Pablo Romero | Mechanical Engineer, MBA; former ops lead at Mercado Libre |
| CTO & Co-Founder | Andrés Rabinovich | PhD in Physics and Systems Biology; 20 years in software/ML |
| Chief Commercial Officer | Alejo Valverde | 11 years at Monsanto; launched INTACTA RR2 in Argentina |
Traction and Strategic Backers
Client logos provide the strongest traction signal. According to an industry report, Calice's platform is already in use by a roster of major seed, input, and research organizations, including Syngenta, BASF, Corteva, and the International Maize and Wheat Improvement Center (CIMMYT) [upstream.ag]. This early adoption by industry incumbents is likely what attracted a specific class of investor. The company has raised approximately $3.5 million in total capital, a figure that includes a pre-seed round, grants, and a $2.5 million seed round led by European agrifood tech VC Astanor in May 2025 [upstream.ag, August 2026] [LatAmList, May 2025]. Other investors include Draper Cygnus and GrainCorp Ventures, the corporate venture arm of the Australian grain handler, which cited early Australian trials in its investment announcement [tribu.la] [graincorp.com.au, February 2025]. The company is now expanding its commercial footprint, having launched NODES platform operations in Brazil [fthnews.com.br].
The Technical Breakdown and Scale Risks
The platform's architecture sits at the intersection of two computationally intensive domains. First, it requires robust biological process models that can simulate plant physiology. Second, it needs a machine learning layer trained on vast, heterogeneous datasets,genomic sequences, soil chemistry profiles, hyper-local weather histories,to find predictive signals. The technical challenge isn't just building accurate models, but building models that are interpretable enough for a plant breeder to trust. A black-box prediction of 5% higher yield is less useful than one that explains the prediction is driven by a specific soil moisture threshold during a key growth stage [calice.ai/esp/].
The sober assessment lies in data quality and model drift. The platform's accuracy is fundamentally tied to the quality and resolution of its input data. Soil maps can be coarse, historical weather data may not reflect changing climate patterns, and the genetic datasets from seed companies are often proprietary and siloed. At scale, small systematic errors in input data could compound, leading to confident but incorrect recommendations. Furthermore, the model's validation is circular: it aims to replace physical trials, but its predictions must still be calibrated against them. A 80% reduction in physical testing is a compelling cost savings, but it also reduces the real-world data feedback loop needed to keep the digital models honest. The long-term risk is that the virtual and actual fields quietly diverge.
The Next Twelve Months
Calice's immediate roadmap is commercial execution. A new version of the NODES platform is planned, targeting product development and commercial teams more directly [fthnews.com.br]. The key metric to watch will be contract values with the named enterprise clients. Moving from pilot projects to multi-year, six-figure software licenses will be the necessary proof point for the model's enterprise readiness. Geographically, the push into Brazil, a massive agricultural market, is a logical and critical next step. Given the current seed funding and the capital-intensive nature of both sales and R&D, another fundraising round within the next 12-18 months seems probable. The bet for investors is that Calice can become the computational decision layer for agricultural R&D, a high-value niche where speed and precision directly translate to billions in potential product revenue for its customers.
Sources
- [calice.ai] About Us | https://calice.ai/eng/about
- [calice.ai] Spanish Homepage | https://calice.ai/esp/
- [upstream.ag, August 2026] Calice and virtual field trials | https://upstream.ag/p/calice-and-virtual-field-trials
- [startupslatam.com] Calice Startup Profile | https://startupslatam.com/startup/calice/
- [LatAmList, May 2025] Calice raises $2.5M Seed Round | https://latamlist.com/calice-raises-2-5m-seed-round/
- [tribu.la] Argentine AgTech Startup Calice Secures $1.5 Million Investment | https://tribu.la/argentine-agtech-startup-calice-secures-1-5-million-investment/
- [graincorp.com.au, February 2025] Beyond field trials: GrainCorp Ventures invests in Calice | https://www.graincorp.com.au/beyond-field-trials-graincorp-ventures-invests-in-calice/
- [fthnews.com.br] Calice launches NODES platform in Brazil | https://fthnews.com.br/