The long term vision in BAYFLEX is to create a radically new product that uses low cost, green organic technologies for probabilistic computing in order to allow continuous and private monitoring of bio-signals on flexible substrates.The vision of flexible green AI sensors with on chip classification, however, extends well beyond biomedical devices and has the possibility to transform sensor data at the edge of large networks. To achieve our goal, BAYFLEX will use physiological sensors based on organic materials that interface smart electronic devices with the soft human body. Electronic circuits will be fabricated in Thin Organic Large Area Electronics (TOLAE) processes. A spiking neuron circuit realized in Organic Thin Film Transistors (OTFTs) transforms non-stationary electrical signals from sensors into stochastic bitstreams and is input into a circuit that performs Bayesian inference using a stochastic computing paradigm to classify the data. The Bayesian inference circuit takes advantage of the unique properties of organic electrochemical transistors (OECTs) to realize low transistor count dynamic Muller c-elements. The final demonstrator (TRL 3-4) will integrate these two organic technologies onto a flexible substrate and targets a circuit size of at least 50 organic transistors including both OECTs and OTFTs. The project brings together a diverse consortium with expertise in modeling emerging devices, biologically inspired circuit design, experts in machine learning involving electrophysiological data and teams with expertise in OTFT and OECT fabrication.