Our client faces a substantial hurdle in their reagent manufacturing QC process. Around 40% of the QC team's time is consumed by manual tasks—spanning data collection, processing, and the crucial traceability required for audits and sign-offs. This challenge is further compounded by the complex technical intricacies inherent to their quality testing procedures. 

We commenced the project with an in-depth discovery phase, forging a close collaboration with the client's QC team to delve into their quality testing procedures and nuances. This encompassed classifying devices, discerning data storage practices, and grasping how the QC team interacts with data.

By conducting a thorough feasibility study, we bolstered the client's confidence in transitioning to a digital QC framework. We pinpointed methods for data extraction from analyzers, ensured consistent data availability throughout the QC sign-off process, and validated the project's potential for success.

Our objective is to eliminate manual interventions, optimizing the QC team's resource allocation and enabling more efficient utilization of analysts in R&D. This effort establishes the pivotal groundwork for weaving AI and Data Science into the client's QC procedures, capitalizing on streamlined QC data storage.

As the data storage infrastructure matures, it will progressively unlock the potential for AI and data science applications, unveiling currently untapped opportunities. Furthermore, we anticipate seamless integration of QC processes with other enterprise systems, foreseeing no substantial hurdles.