Case overview
Cymise Digital Twin was deployed at a mid-sized poultry hatchery to enhance the hatchability rate of eggs through precision monitoring, real-time analytics, and AI-led environmental optimisation.
Involvement
The Brief
The poultry farm faced a critical operational challenge — only 60% of fertilised eggs were hatching successfully. Inconsistent temperature gradients, ammonia accumulation from waste, and poor micro-environmental control were causing embryo mortality and reduced productivity. The goal was to create a digital twin model that could continuously learn, simulate, and predict the ideal conditions to improve hatch success rates while reducing human supervision.
Our Approach
Our solution combined smart sensing, predictive analytics, and virtual modelling to replicate every environmental and biological variable impacting hatch success.
The Results
The Cymise Digital Twin enabled the hatchery to transform from reactive management to predictive optimisation — maximising yield, ensuring consistency, and reducing energy and resource wastage.
