What was the gap you saw in the life sciences that led you to co-found WhizAI?
The amount of data companies collect today is astounding, and we saw a need for a platform to help these companies leverage their data in a way that is fast, scalable and easy to use. In doing so, these companies can improve their operational efficiencies, market shares, and drug discoveries.
We live in an increasingly connected and on-demand world, where everybody is available online. We do not see a reason why analytics should be any different. We want users to be able to have key facts on hand instantaneously rather than having to sift through various dashboards and reports.
Who are WhizAI’s target clients?
Our key clients cover the life sciences and healthcare space. Our strong suit is commercial pharma, and we are advancing in manufacturing, supply chain, clinical R&D, and recently patient services. We recently found success with Medtech companies and are currently expanding to payers and providers, helping customers in the US and Europe. WhizAI’s platform can be used for a variety of functions in an organization. We believe every function in the life sciences and healthcare industries can optimize operations based on structured data and insights.
What did the process of training your AI look like?
We invested a lot of resources into four main aspects of training our AI. The first level was the data itself. We began working with industry experts as well as our own customers to bring in novel use cases that could contribute to language models. This was successful; for example, on the commercial side we probably have the industry’s largest question bank to date. The second level was to then understand the data models for quick deployment. To do this, we have been working with many of our customers and partners to build accelerators to go live in days and shorten the time to value for our customers. The third aspect of our technology is the zero code environment to build, share and collaborate on analytics. Analytics processes that used to take weeks are now done in minutes in WhizAI. Lastly, we look at insights, which is domain-specific. For example, there is a difference in relevance between someone who is running clinical trials and someone who is an area manager overseeing 600 doctors. Together, these four training components have led to a highly comprehensive system that only continues to expand its capabilities.
Are patients able to witness benefits from these tools firsthand?
While patients do not interact directly with WhizAI tools, people in pharma who impact the patient experience absolutely do. For example, people within patient service roles at big pharma companies see clear benefits by providing a holistic view of the entire patient journey through each step of the process, with real-time insights, from referral through titration and adherence. Here, the benefit is how instantaneously users can get the lowest-level details to then figure out what is happening in a unique situation. From a patient’s perspective, what used to take days can now happen quickly, improving their overall experience and potentially their health outcomes.
What role do you see AI playing in the life sciences over the next few years?
Although AI is relatively new today, in 10-15 years every application will be AI-enabled. I see a lot of room for advancement within certain IoT applications in which it is humanly impossible to track all the data. Additionally, there is a lot of unstructured data in the form of patient or doctors’ notes, and we see a tremendous potential to apply AI towards molding this into actionable information. Of course, on the clinical side, AI enables researchers to discover molecules faster. In my mind, AI is everywhere. In 10 years, it will be like the internet — we do not talk about it, we just use it.
How do you see WhizAI growing in the near term?
WhizAI is just getting started. We started the company only five years ago and are now seeing large pharma adopting our platform, trusting it to run their analytics. This is a testament to how hungry our customers are for companies that offer products like ours.
I anticipate we will double the size of the company by the end of 2022, and we are on track to do 300% revenue growth in the same timeframe.