At last week’s LBSP Tech Talk on AI in the life sciences, our CTO Bas Cloin shared DataChaperone’s vision for the lab of the future; one where fully automated, AI-driven systems continuously execute the scientific method without ever needing a coffee break. Reaching that point requires taking the right steps now to build momentum.
Many companies get bogged down in the implementation and maintenance effort required for AI-driven automation, even in relatively simple scenarios. Reducing that burden is essential, and choosing the right tooling for each use case is one of the fastest wins. But how do you make those choices?
We use a spiderweb plot to map use cases and tooling, offering clear guidance based on factors such as the stability of input–output relationships, the need for interpretability, and the reliability required for the system to be fit for purpose.
The optimal choice also depends heavily on context: the same challenge in an exploratory workflow can have very different requirements than in a production or GxP-regulated environment.
With this framework, we help you choose the right approach and deliver production-grade workflow automation within weeks.
The energetic discussions afterwards showed that this pragmatic and transparent approach resonated. We look forward to continuing these conversations and helping more teams move toward scalable, automated data analysis!
Why scientific software is being rebuilt from the inside out
Lab software is shifting from tools to connected workflows. The next leap is governed analysis and interpretation that runs across your existing stack.
