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Image Addresses Process Development Capacity Constraints with AI-Powered PD Optimization

New innovation delivers optimal product conditions in fewer runs, reducing raw materials costs, and shaving weeks off of Process Development campaigns

SAN JOSE, CALIF. (DECEMBER 15, 2022) -, provider of an enterprise-scale GxP and CFR 21 part 11 compliant platform, applications, and end-to-end solutions that accelerate customers’ industry 4.0 journey, has released its PD Optimizer, a zero-code AI-powered application for optimizing Process Development (PD) and automating Design of Experiments (DoE).

Time to market and accelerating commercialization of drug pipelines are the most important business imperatives for life sciences manufacturers. PD capacity is cited by life sciences executives as a bottleneck that oftentimes rivals manufacturing capacity. Existing workflows are inefficient and highly skilled resources needed for PD are scarce.’s innovation directly addresses these needs. Early adopters of the application have seen significant, tangible benefits.

The PD Optimizer uses a proprietary small-data AI algorithm that integrates process SME understanding and knowledge to efficiently explore the design and operational space while continuously recommending experimental settings. The application serves as an effective knowledge and data management tool by combining process, quality, and PAT data to create a holistic view of the PD design space and the lifecycle of experiments and results.

Explainability is built into the application to provide the “why” behind the recommendations. The parameters that were used along with their relative weightings are provided, along with two-dimensional and multi-dimensional interactive plots to explain how operating ranges of parameters will impact the outcome of the experiments. Control of experiments and processes is often imperfect, but’s models and UI show the impact those imperfections or variances have on the target outcome. This also enables running predictive “what if” scenarios on how varying inputs affect experimental outcomes.

“While the value and impact of AI in product development and manufacturing is well-accepted, most existing techniques rely on big-data based algorithms or mechanistic models. Big data is scarce in life sciences and accurate mechanistic models are often impossible to develop with speed and accuracy. Our team has overcome this challenge by using small-data algorithms focused on outcome optimization rather than predictions,” explains founder and CEO, Rajiv Anand. “Digitalization of PD also addresses another critical need in life sciences manufacturing – tech transfer. When PD is done with a well-accepted digital method, knowledge transfer happens algorithmically, providing certainty and consistency in distributed manufacturing for both internal and contract (CMO).

Deployment is a simple process consisting of a cloud-based application configurable to align with the users’ current level of data readiness, from simple manual data upload to streaming connectivity with lab equipment, data, and systems. was founded by a veteran team of manufacturing engineers, reliability experts, and data scientists who saw the opportunity to apply machine learning and analytics to solve difficult process manufacturing challenges. The Quartic Platform is utilized today in a number of process manufacturing deployments, including optimization and reliability. 

About helps manufacturers predict and control the current and future state of their manufacturing operations by improving batch operations, decreasing waste, reducing the number of quality checks, and delivering better products faster. Purpose-built for manufacturing engineers, reliability experts, and data scientists, the Quartic Platform is used by manufacturers to apply machine learning, analytics, and edge computing to solve very real challenges in their process manufacturing efforts. By harnessing the power of data and AI, is making autonomous manufacturing a reality. For more information, contact at 1-866-QUARTIC or visit