Automated laboratory workflows can reliably execute predefined assays,
but adapting experimental conditions and selecting the next action still
require considerable scientific input. This case study explores whether AI can
build on an established automation and data foundation to direct assay
development with less manual intervention.
• Establish an automation-ready foundation by designing standardised
proteomics data pipelines alongside flexible analytical routes for specialist
scientific questions
• Increase unattended laboratory throughput through barcode-triggered
workflows that select assays, prepare reagents and execute predefined
protocols automatically
• Advance towards AI-directed assay development by enabling an agent to
select reagents, adjust experimental conditions and determine the next test
required to reach a defined endpoint
Check out the incredible speaker line-up to see who will be joining Carsten.
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