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AI is the new buzzword in bioprocessing. It’s promising faster development, better control, and smarter decision-making. But in reality most teams struggle to turn AI into something useful. Too often, AI is layered on top of fragmented data, missing context, and workflows that were never designed to support biological variability.
In this session, BioRaptor CEO Ori Zakin breaks down what trusted AI actually requires in real bioprocess environments, how to capture context, connect upstream/downstream lineage, and implement AI iteratively to reduce risk while driving measurable process improvements.
Topics and timestamps
4:44 - why we can’t get ChatGPT-like answers in bioprocessing
8:05 - how much data do you need to start seeing AI benefits
14:28 - examples how context changes insights
18:31 - implementation playbook: adoption, trust, traceability, and the 4-stage iterative journey
36:27 - demo + Q&A: grounding results, structured capture, and practical AI value
Watch the recording
Ready to get actionable insights from YOUR data, get in touch with us and we’ll show you what’s possible.
If this session resonated, BioRaptor helps teams capture, harmonize and structure bioprocess data, and has an AI engine that works quietly in the background, constantly connecting data points across your process to surface insights no human could spot alone.
