Minfy Unlocks New Possibilities for Ayur.AI with AI- Healthcare Solutions

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In recent years, there is a trend of technological transformation in the businesses all around the globe. We are in the midst of digital transformation, where every organisation is putting resources to foster innovation, and to adapt with the market and customers need. Organizations are democratizing the data with collaborative efforts. Ayur.AI is in a similar bandwagon, where Minfy is helping Ayur.AI in solving some of the pressing issues of our time in the healthcare domain, collectively.

Ayur.AI has a deep domain knowledge and understanding of holistic healthcare practices to transform healthcare into a more proactive, preventive, and patient-centric experience by combining Ayurveda with modern science.

To support Ayur.AI achieve their vision, Minfy identified three broad areas of collaboration in their service and product offerings as below:

1. AI had patient data in unstructured formats such as images and PDFs. Hence, they faced challenges in utilizing that data to advance their service and product offerings.
2. AI wanted to track a patient’s progress over time and to perform advanced analytics to achieve a more proactive and preventive approach.
3. AI wanted to better manage their ayurvedic nutraceutical products listing by understanding the emotions around a particular product so that they could make better, and data driven recommendations to the customers.

Solution Offerings

Solution 1: Data Digitization

The process of converting unstructured data into structured/semi-structured data involves several steps, including data extraction from documents, data cleaning and pre-processing, data mapping, data standardization, and data normalization. After these steps are completed, the data is typically stored, analysed, and used to generate insights.

AWS offers an array of tools and services to assist healthcare stakeholders in unlocking the full potential of their data. Our solution utilizes various AWS services like Amazon Textract to process a small sample of documents, extracting relevant data and standardizing for further processing.

Extraction: The foundation of utilizing unstructured healthcare data lies in the extraction of relevant information from various sources such as images, PDFs, and scanned documents. This process involves leveraging the power of Optical Character Recognition (OCR) algorithms and tools, such as the widely available services like Amazon Textract. These algorithms and tools enable us to recognize, extract, and define output from the unstructured data, making it machine-readable and ready for further analysis.

We used Amazon Textract to perform the OCR and digitize the data. An instance of a sample lab investigation report ran through Textract to get the extracted information in the CSV or Excel format.


The corresponding output file of the unstructured document:


Processing: Once the data is extracted, we are standardizing it to a uniform format for meaningful comparison and calculations. Natural Language Processing (NLP) techniques play a vital role in extracting valuable information from textual data. This feature allows a healthcare provider to look from the complete document and get an understanding of mapping of what were the diagnoses, symptoms, treatments and qualities etc.

Data Mapping, Normalization, & Standardization: The extracted data is diverse and needs to be transformed to a common scale to facilitate meaningful comparison and calculations. We transformed the data into the standard forms to ensure homogeneity of medical concepts across different datasets.

Because data ontology plays a crucial role in establishing a common ground for understanding the data, thereby enhancing the accuracy and relevance of analyses by defining the relationships between different medical concepts.

Solution 2: Advanced Analytics and Visualizations on Patient Data

We have successfully extracted, cleaned, and pre-processed the data. As a result, it is now structured and ready to be used for analytics and visualizations. We started with performing a descriptive analysis of the patient data to gain insights into their current health status. We used Amazon QuickSight to create dashboards that visualize the medical data.

For instance, we had patient lab investigation reports, and we had extracted the information along with unique identifiers such as UHID. We then performed the necessary preliminary steps to ensure that the data is usable.

Our descriptive analytics involved creating a dashboard that displays vitals trends, health parameters abnormalities, a timeline view of medical events, the distribution of normality, and the classification of parameters based on abnormality. We also included a health parameter value change indicator to compare the changes during a specific period.

These powerful visualization techniques would enable healthcare professionals to determine patterns, trends, and correlations within the data - providing valuable insights into patient health, current stage of the disease and potential medical outcomes.

Solution 3: AI Powered Sentiment Analysis of Nutraceutical Product Reviews

For performing the sentiment analysis, Minfy used their in-house solution – Sentiment.ai. Sentiment.ai is a solution that uses AI/ML to analyse the sentiment of text data to understand the emotions and opinions. ​

With the help of Sentiment.ai, we enabled Ayur.AI to better understand the customer emotions about products, make data-driven and informed decisions, and provided real-time analytics, trend detection, and sentiment tracking to capture the pulse of customer sentiment.


The analysis helped Ayur.AI to accurately understand the sentiment of customers, proactively addressing the detected issues or concerns, and finally to use transparent and data-driven approach. Overall, the solution enhanced customer satisfaction, personalized marketing strategies, optimized product assortment and improved Ayur.AI’ s reputation across retailers.


The following outcomes were achieved by Ayur.AI during their collaboration with Minfy:

Effective Data Utilization: Ayur.AI successfully transitioned from unstructured data formats like images and PDFs to structured data. The data digitization process enabled Ayur.AI to harness valuable insights from previously untapped data sources.

Proactive Healthcare Approach: With the ability to track patient progress over time, Ayur.AI adopted a proactive and preventive approach to healthcare. Advanced analytics provided a comprehensive view of patient health, leading to informed decision-making for doctors.

Data-Driven Decision-Making for Products: Ayur.AI gained a deeper understanding of customer emotions and opinions regarding their nutraceutical products listing. Real-time sentiment analysis empowered Ayur.AI to make data-driven decisions, updating listing of products in real-time.


Healthcare systems are at a critical juncture in the dynamic world of smart healthcare. While fundamental principles and systems have been in use for a long time, the introduction of new technologies opens a wide range of opportunities for growth and advancement.

The adoption of technological developments has moved from voluntary to mandatory in a fast-expanding healthcare market. It is the path that will allow us to provide efficient, patient-centric care, ultimately leading to better health outcomes for everyone.

As a result, it is critical that we work together, push the boundaries, and truly embrace the transformative force of innovation as we journey towards a future where healthcare’s potential is limitless.

Co-Author - Dr. Bala Pesala | Founder & CEO, Ayur.AI

Co-Author – Yedhu Krishnan B | Senior Full Stack Developer, Ayur.AI

Author – Gaurav Lohkna | Data Scientist, Minfy

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