Overview
Over the last decade, advances in sequencing and high-throughput assays have caused an explosion in biological data generation, but the infrastructure to standardize and prepare that data hasn't kept pace. Public and private multi-omics datasets are inconsistently formatted, metadata is incomplete or mislabeled, sample annotations vary across studies and repositories, and reproducible pipelines have to be manually assembled for every new dataset. Public repositories like GEO and PRIDE hold petabytes of transcriptomic, proteomic, and metabolomic data, yet researchers routinely spend 2–4 weeks on retrieval, cleaning, and harmonization before any real analysis can begin — time that slows publications, delays drug discovery, and limits how well downstream AI models perform. After speaking with more than 100 academic researchers, biotech founders, and lab heads, the pattern was consistent: data preparation, not analysis, is the actual bottleneck in biology.
Celer Bio is an AI-powered data infrastructure layer that retrieves, cleans, standardizes, and analyzes multi-omics biological data for academic labs and biotech companies, compressing that weeks-long prep process into hours. It retrieves datasets from public and private repositories like GEO and PRIDE, cleans and harmonizes metadata, standardizes formats across studies and modalities, assembles reproducible analysis pipelines, and outputs structured, analysis-ready datasets with clear summaries. Rather than building another end-to-end analysis platform, Celer is intentionally headless — it sits between raw biological data and downstream tools, so it plugs into the workflows, analysis platforms, and internal pipelines labs and pharma companies already use instead of replacing them.