Societies are facing growing non-communicable diseases (NCDs) closely associated with lifestyle and in particular unhealthy diet. The World Health Organization estimated that, in 2010, 56.1% of the adult population of Europe was overweight, and the prevalence had increased to 58% by 2014. These numbers demonstrate the high societal impact and the associated increase in costs for the healthcare system resulting from unhealthy nutrition.To improve the outcome of a dietary intervention, dietary assessment needs to be improved and use measurements of health-related markers that can provide insights into how our bodies digest food. In recent years, evidence has shown that the microbial communities living in the human gut (gut-microbiota) play a key role in digestion and its composition has been associated with dietary patterns and several diseases.Metabolomics technologies can measure small metabolites and nutrients available in biological fluids (e.g., blood and urine) and the gut-microbiome composition can be determined using sequencing technologies (e.g., 16S-RNA, shotgun sequencing). So, together these technologies can support the collection of dietary intake data and monitoring of the health status of individuals. By measuring these dynamic elements, the nutritionist can also obtain information about the efficiency of a dietary intervention, and thus, promote maintenance of health and well-being, through an individually-tailored and continuous approach. The concept of a “one-fits-all” diet is not up to date anymore, and individuals look for products and services tailored to their personal requirements. To this day, there is no service or product that successfully integrates these complex data in the context of nutrition. The Nutriomix project proposes the development of a dietary-recommendation tool that predicts the impact of specific diets by analyzing the gut microbiota and metabolomics profile of individuals through the generation of personalized models of metabolism, using an approach known as Constraint-based modeling.