By delivering AI powered services in the pharmaceutical space, Datafoundry is an AI startup venture established in the year 2015 that operates on a set of guiding principles of inclusivity, collaboration, innovation, empowerment, and dedication. The company efficiently utilizes the emerging technologies of data science and cloud services to solve critical business problems in meaningful ways. Vivek Kalagara, Founder, engages in a conversation to display an informative picture about Datafoundry’s solutions and products.
In conversation with Vivek Kalagara, Founder, Datafoundry
More companies investing in and adopting AI technology for business give birth to a strong interest in AI future trends. As an AI technology domain player, how has Datafoundry placed and adapted in this space?
A critical success factor for AI is the ability to manage and process large volumes of data. In the healthcare space in which we specialize, data is being collected, processed, and analysed at different points in the system. Our vision is to build an AI platform that can intelligently organize and process data, and fix the current problems by reducing errors, speeding up processing, reducing cost, and ultimately ensure that safe drugs are
made available with a reduced cycle time to patients who need them. Our AI platform will also be compliant to existing regulations and will provide all the necessary evidence for the safe as well as ethical deployment of AI.
Our AI platform will also be compliant to existing regulations and will provide all the necessary evidence for the safe as well as ethical deployment of AI
We look for areas within the existing business processes that can be improved through AI automation. As you rightly point out, a majority of data out there is unstructured, and that’s true for the Pharma space as well. Our methods use smart AI algorithms that initially digitize the unstructured data and then extract the informational content. We apply modified versions of the state-of-the-art deep learning algorithms that extract the entities in from an image, then categorize and tag them to understand the context. This context then helps us to capture the semantic information of the data. We apply NLP algorithms to extract the content and numerical to recognize the context.
Security of data is the main concern across the entire AI spectrum. How do you deal with these challenges?
We use highly secure servers that are on the cloud, replicated for disaster recovery and business continuity purposes. The data access is highly
controlled with different roles having permission to look at different parts of the data. Another level of security is the de-identification of personally identifiable information. We are also devising a new biometrics module that will allow us to safely identify who has initiated a request to access and consume data. This module will be build for humans before extending to non-human objects so that the number of fraudulent access to data can be limited. Finally, there is data encryption to store sensitive information such as social security or Aadhaar numbers or passwords.
Kindly cite a good example on how your AI powered solutions have earned considerable benefits to that organization or industry.
We have delivered a Digital Labelling product for a pharmaceutical company that is managing 55,000 product labels. Our product increased efficiency by threefold with 100 percent adherence to compliance needs, and also enabled automation for most of the manual processes. We have invested in an AI Practice team that assists our customers at every stage of their transformational journey. We have devised a repeatable AI framework that educates our customers on AI and also provide tools, methodologies and ROI calculators.
What further innovation do you plan to include in your AI technology offerings?
Our architecture is built on a robust but flexible framework. This allows us to continue leveraging the latest technologies and algorithms that becomes available in the future. Current legacy system have not been constructed that way, which is why many companies are running traditional systems and are unable to change. Our approach is to adapt parts of the emerging edge technologies for the pharmaceutical space, and continue to focus on our area of expertise.