Leveraging AI for Digital Transformation in Bioprocess Document Management
A global leader in pharmaceuticals and diagnostics faced challenges with their paper-based documentation in bioprocess manufacturing, which was labor-intensive and error-prone. To address this, they partnered with Netlight to develop a digital solution using advanced optical character recognition (OCR) and Generative AI.
About the client
The client is a global leader in pharmaceuticals and diagnostics, dedicated to advancing healthcare by developing innovative medicines and diagnostic solutions for complex diseases.
Challenge
In the client's bioprocess manufacturing facilities, vast amounts of printed and handwritten manufacturing documents are generated daily and stored in archival folders. These documents are crucial for optimizing existing processes and planning new production facilities. The reliance on paper-based documentation posed significant challenges, as the manual effort required to manage, retrieve, and analyze data was labor-intensive, time-consuming, and error-prone. The client recognized the urgent need to transition to a more digitalized workflow to enhance the efficiency, accuracy, and accessibility of essential information.
Solution
Netlight developed an advanced data extraction and manipulation tool within the framework of a PoC. Netlight integrated advanced optical character recognition (OCR) technology and Generative AI to digitalize, process, and query unstructured documents, including machine-written and handwritten content in various layouts. The team collaborated closely with the client to ensure the solution was tailored to their needs, enabling a seamless transition from manual to automated processes. This digitalization initiative aimed to reduce the reliance on paper, improve data accessibility, and streamline workflows.
Impact
- Boost of operational efficiency by enabling tasks that previously took a week to be completed in just 15 minutes.
- Simplified workflows by allowing users to query documents using natural language, making data insights accessible to a broader range of employees.
- Enabled novel analysis by opening up various new use cases that rely on processing handwritten documents (e.g., wastewater mgmt.).
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