How AI powered analysis can help manufacturing and MSAT teams understand batch performance, yield, quality, investigations and technology transfer using data they already have Biopharma manufacturing teams already collect a lot of data. Batch records. Process parameters. In-process testing. Yield and recovery data. QC results. Deviations. Development reports. Tech transfer documents. The challenge is often

How historical AAV manufacturing data can uncover process optimization opportunities and help Process Development and MSAT focus their investigations AAV manufacturing generates large amounts of data across manufacturing systems, process historians, batch records, in-process testing and QC laboratories. But the data is often reviewed one batch or one system at a time. That makes it

27 periodic reviews. 650+ documents analyzed. One clear view of system health. Periodic reviews are intended to answer a relatively simple question: is this GxP system or piece of computerized equipment still operating in a controlled and appropriately validated or qualified state? Getting to that answer is rarely simple. A reviewer may need to look