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Artificial Intelligence Can Make Pharmaceutical Companies More Competitive

Quartic.AI Briefing Note

By: Eric M. Luyer, Industry Research Analyst

Axendia was recently briefed by the senior executive team of Quartic.ai, based in San Jose, CA, US.   Quartic.ai was founded in 2017 by veterans in process automation, manufacturing, and reliability in conjunction with data scientists to optimize the practical use of information through Artificial Intelligence (AI) deployment.  The company had been recognized last year as the fastest growing technology leader in industrial AI by CIO Review Magazine.

The mission of Quartic.ai is to enable subject matter experts (SME) to help solve manufacturing problems by using AI. “We look at this as becoming another tool in their tool-belt of problem-solving. What we’re building enables the SMEs to build their solutions with AI”, said Rajiv Anand the CEO of Quartic.ai.

According to Rajiv Anand, their vision and mandate is not to grow their business through providing services, but by providing the technology. Their technology is a complete “smart industry platform” to support process- and pharma manufacturing industries via digital transformation using the concepts of the Industrial Internet of Things (IIoT) and AI. Combining the Quartic platform with its proven approach and expertise, manufacturers are now able to extract more value from their legacy infrastructure and build Industry 4.0 factory capabilities. This will help accelerate the decision-making process in an organization using their IIoT and AI deployment capabilities. It will also increase productivity significantly, improve operational performance and position the company as more competitive.

“We looked at where most of the challenges in the industry are right now. They are around lack of talent, or better said: lack of data science talent. We’re solving that problem by building AI for the subject matter expert, which is based on a lot of automation of machine learning, zero-code, etc.…The other bigger challenge is the integration of all your legacy data. Those are the two focus areas, and that’s how our technology platform is structured: eXponence is the intelligence engine, and Illuminator is the data engine”, commented Rajiv Anand.

Machine Learning Platforms

The technology platform automates the implementation of Machine Learning, but allows the SME to determine the features and aspects that represent the problem to be used to build the models. This allows users to understand and explain the output of models at every step – building, testing, validation and deployment.

“Our eXponence engine really focuses on the automated machine learning development and automated analytics for critical equipment and processes. What this lets them [SMEs] do is not only build intelligence without having the deep data science knowledge, but also deploy the intelligence, being able to have these individual models monitor their processes, monitor their equipment, provide feedback, provide insights, and then turn around and make decisions and changes, can improve their processes. IlluminatorTM provides a fully integrated contextual data pipeline for expert users and data-scientists to build AI applications with tools and libraries of their choice”, remarked Vinodh Rodrigues, Manager, Industry Solutions of Quartic.ai.

Talking about their mission and vision, Larry Taber, Vice President Life Sciences Industry of Quartic.ai added: “If we are speaking to senior executives, our intent is to drive manufacturing innovation to any extent possible on the existing assets, and to speed new products to market. We believe that, by the design of the platform, we’re going to lower the activation energy and the direct investment needed to bring multiple functions from pharma around the table by use of Machine Learning and AI”.

 In Brief

The company’s focus is on late-stage molecules and products, specifically on late-stage development, technology transfer, and commercialization. Their intent and business strategy is to have the technology platform associated with the control strategies for products throughout their life cycle.

From a market perspective and observing current drivers in the pharma industry, these solutions are well placed considering the shift to biologics, more complex molecules, and more innovative manufacturing platforms. Also in view of advanced developments that are taking place with platform-based approaches for rapid technology transfer and rapid product development as well as commercial launches, there are more business opportunities especially for smaller-scale operations.

Using these new technologies and platform, the benefits are getting clearer: First, once you have a better understanding of how current processes are working and have these also digitally available (in a digital twin), you then can make changes digitally in a virtual environment before actually move them into testing. This also helps predict changes in following releases.

Secondly, it assists in responding to questions around regulatory support or data-modeling and validation: e.g. how do you know your model is robust, or how can you test your validation appropriately, or what kind of risk-based methods can you adopt?

More generally, this solution highly contributes to a company’s value proposition using sophisticated technology such as Machine Learning and Artificial Intelligence.  It allows for changing the production and design mind-set from a reactive design to a more proactive design, moving business processes into a future-perspective, trying to find the best and optimized process and result possible.

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