OpenAI built Rosalind Workbench to solve a real problem in life sciences research: your data lives in one system, your analysis happens in another, and the documentation of how you got your results sits somewhere else entirely. The tool consolidates everything into a single environment where researchers can work with their preferred scientific tools, test new biology models, and build reusable analysis workflows. It's built on top of GPT-Rosalind, a specialized model trained for life sciences work across areas like medicinal chemistry, genomics, and wet-lab assistance. Right now it's available as a research preview through ChatGPT, where you can pick from guided tasks and customize them to match your actual data and research questions. The workbench walks you through research in stages that mirror how actual science works. You start by selecting a biological question—say, protein design or genomics analysis—then connect the specialized tools you need to answer it. The system keeps everything connected as you move between steps: if you're designing nanobodies, you can rank candidates, plan binding assays, and inspect molecular structures without losing track of your original question. For genomics work specifically, the NGS Analysis Workbench handles the messy parts like matching files to metadata, checking quality, identifying replicates, and picking the right statistical approach. Each step stays tied to the others so you're not constantly rebuilding context. The tool operates in two modes depending on what you're doing. Explore mode lets you ask general scientific questions and work through ideas with standard ChatGPT. Research mode handles complex analysis and advanced workflows, though that access currently requires verification through your organization (individual access is coming). The core idea is straightforward: keeping your question, tools, analysis, and evidence together cuts down on the time wasted coordinating between systems and rebuilding context, leaving more time to actually think about what your data means.