AI-based Software Ecosystem

The EnoGen AI ecosystem consists of four integrated modules that work as a unified, modular framework for predictive and precision fermentation. Each software unit processes structured and unstructured data using advanced AI models, including NLP (Natural Language Processing), supervised machine learning, and multi-agent simulations. These tools enable reverse engineering of sensory targets, genome-driven yeast formulation, and text-based sensory decoding — all within a scalable, API-ready architecture built for interoperability and decision-making under uncertainty.

TANNINO

Extracts semantic descriptors from textual input and maps them to a multidimensional organoleptic matrix for blend prediction.

YEAST

Utilizes genomics and variant mapping to design customized yeast blends optimized for strain compatibility and functional traits.

SENSORIAL

Leverages deep learning models for NLP to interpret tasting notes and correlate them with measurable sensory profiles.

FERMENTUM

Simulates multispecies fermentations using agent-based modeling to anticipate aroma evolution and metabolic interactions.