Sensors Network

Rearing multiple biological species is complex as they are very sensitive to external parameters, which eventually affects their nutrition profile, quality and growth.

Data Collection

An in-house internal network of monitoring sensors (CO, CO₂, Light, Humidity, Temperature etc.) provides us real-time control ofall critical parameters and react accordingly if needed.

Making sense of data

Alerts are set for each parameter so we can pay attention when it is required without any delay.
Data is also stored, for further analysis using AI-powered algorithms.

Optimized acoustics

In-house acoustics model has been developped to ensure our workers a healthy working environment at all times.

Wastes Real-time valuation

Real-time valuation is performed at suppliers door step , through our Facility Design  |  Extra feactures are past transactions history, availability on both Android and iOS platforms.

Optimized upstream collection

Best collection routes are automatically displayed on waste-pickers and truck's drivers screens, and offline mode takes over when the mobile has poor reception.

Optimized downstream delivery

Real time delivery status can be provided on buyer's screen, and placing an order is also possible for some finished products as organic fertilizer.

Labor Scheduling

Daily production routines are optimized and assigned to the floor staff, according to the production needs and sonstraints, to ensure manforce and production are correctly aligned.

Inventory Monitoring

As in any warehouse, inventory and stocks are carefully controlled day after day, to ensure clients delivery will be just on-time, or early detection of any delay.

Big data

Many internal and external data (different feedings, geographical locations, climatic conditions etc.) will be gathered along the rearing and hatching processes, and from all our plants around the world.

Predictive Analytics

Predictive Analytics will be used mainly for optimizing upstream wastes collection predictions, in countries that do not have existing data or statistics on waste management.

Machine Learning

In-house developped Reinforcement Learning algorithms will process all collected data for improving and optimizing more and more our processes, increase our efficiency and profitability.