Microbiome Balancer: A healthy gut for a healthy living

Microbiome Balancer: A healthy gut for a healthy living


“All disease begins in the gut” – Hippocrates 

Microbiome research has an effect on everything that is related to symbiotic, pathogenic and commensal microorganisms that are present in the ecosystem. A better understanding of the microbiota can help human beings in different walks of life namely, healthcare, tackling pollution (protection of coral reefs), agriculture, ecosystem, waste management, preventing epidemiology, biotechnology etc.

There are many studies suggesting that heart disease, diabetes, Alzheimer’s, Parkinson’s, multiple sclerosis, Crohn’s, autism and other conditions, are affected by the state of gut bacteria. Even a person’s moods and emotions are affected by them. Harmonizing gut bacteria has become a groundbreaking therapeutic treatment for many chronic diseases.

Considering the growing interest in microbiome research and acceptance of Ayurveda in modern society, we propose employing advanced technological concepts to assess individual gut microbiome structure and propose remedies to improve the same. 

  • Assessment Engine: A self-learning algorithm that utilizes data management and cognitive capabilities to analyse an individual’s gut microbiome signature. Using high end statistical and computational methodologies this will help classify an individual into a healthy or unhealthy group with respect to the gut microbiome status.
  • Visualization Engine: This will apply graphical and statistical techniques to analyze and visualize the data at hand. Various concepts of microbiome analysis namely diversity, richness etc. will be presented graphically on a dashboard. 
  • Recommendation Engine: A self-learning algorithm employing high-end concepts of statistics and cognitive science to propose dietary, therapeutic or lifestyle changes for gut microbiome enhancement. The system will use inputs from research papers, past recommendations and time tested remedies to determine the recommendation for an individual.



  • Help in differentiating healthy and unhealthy human microbiome signature.
  • Graphical presentation of microbiome data.
  • Recommendation system helps in suggesting corrective measures based on its knowledge database.

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