This is like having a super-smart digital food scientist that can invent new recipes for plant‑based foods—mayonnaise, milk, burgers—by learning from millions of real food examples and ingredients, then proposing new formulas that taste and feel like the originals.
Traditional food R&D for new formulations (especially plant-based alternatives) is slow, expensive, and hit-or-miss. NotCo uses AI to rapidly explore ingredient combinations and functional properties to create alternative food products that match or outperform animal-based versions on taste, texture, and cost, while meeting brand and regulatory constraints.
Proprietary formulation datasets linking ingredients to sensory, nutritional, and functional properties; trained models specialized for food-science tasks; and embedded relationships with CPG and food-service brands that make switching costs high once integrated into their product development workflows.
Hybrid
Unknown
High (Custom Models/Infra)
Data collection and labeling for high-fidelity sensory and functional properties across large ingredient spaces; plus computational cost of large-scale combinatorial search and simulation for new formulations.
Early Adopters
Unlike traditional food companies that use mostly manual R&D or generic statistical models, NotCo is marketed as an AI-native food company whose core asset is an ML system for mapping between animal-based products and plant-based ingredient combinations, enabling faster and more flexible formulation and white-label co-creation with large CPG brands.