Hi HN! We're building Novaflow to help life scientists analyze their experimental data without needing to code. Life science researchers produce massive amounts of data, but analyzing it typically requires advanced coding skills, specialized knowledge, and heavy computational resources - all of which are in limited supply. The bottlenecks we've seen are striking: small labs spend over $100K/year per analyst while large labs spend millions, yet still outsource analysis due to sheer data volume. Most labs have a 5:1 ratio of experimentalists to analysts, creating constant backlogs. The core issue is that analyzing biological data requires both extensive coding knowledge and deep understanding of biological context. Most researchers have one or the other, rarely both. Making matters worse, existing tools are often custom-built, poorly maintained, and not scalable. Many researchers are stuck using analysis tools that are 15+ years old. We built Novaflow to put analysis capabilities directly back in researchers' hands. Here's how it works: researchers upload their raw data files (CSVs, FASTQs, HDF5s), ask questions in plain English like "What genes are most differentially expressed in this file?", and get instant, publication-ready plots. Behind the scenes, we use LLM-powered pipelines that generate and run the appropriate bioinformatics workflows. The technical challenge is ensuring scientific accuracy. We've built extensive validation systems to ensure the generated code produces reliable results. Every analysis comes with exportable Jupyter notebooks and reproducible Python code, so researchers can verify and modify our approach. What makes this different from general data analysis tools is the domain-specific understanding. When a researcher asks about differential expression, the system knows to apply appropriate statistical methods, normalizations, and generate the right visualizations - things that would require extensive configuration in generic tools. We're focusing on life scientists blocked by slow or missing bioinformatics support - academic labs doing genomics, transcriptomics, and proteomics work, biotech companies trying to accelerate R&D cycles with leaner teams, and clinical groups using high-throughput technologies. We'd love to hear from anyone who's dealt with similar bottlenecks in scientific computing.