Only 7.9% of new medicin is approved for phase 1 clinical programs - life saving cures are being scrapped because of poor reproducibility. Today, researchers designing complex in vitro and in vivo experiments jump between many tools that do not talk to each other. Protocols are written in free text, naming is inconsistent, raw data is scattered across drives, and inventory and cell banks are tracked separately. This makes it hard to reproduce experiments, compare results across projects, trace which cell material went into which batch, and satisfy regulatory expectations. Small and mid-sized labs often cannot afford enterprise platforms like Benchling or Dotmatics, so they manually glue everything together and rarely get to use advanced analytics or AI. BioPilot solves this by standardising the full early-to-in-vivo CTR pipeline in one product. Users can register and template in vitro experiments (differentiation, expansion, cryopreservation, device testing, etc.) and in vivo studies (animals, groups, dosing, housing, ethics documents) using fixed ontologies. This gives every experiment a structured schema and a precise schedule of readouts, barcodes and data types to collect. Raw data (FACS, scRNA-seq, imaging, counters, bioreactor logs, etc.) is then automatically linked back to the right experiment, condition and sample. On top of this, BioPilot offers: • Inventory and cell bank tracking that makes it trivial to trace which vial, edit or expansion batch was used where. • Live bioreactor monitoring with alarms (email / phone) to prevent losing expensive batches. • A growing library of free analytical tools, including automated FACS gating, scRNA-seq pipelines and image analysis that can feed results straight back into the experiment records. • A global ontology service for vendors, instruments, cell lines, species and process terms, with a roadmap for a public API. Because everything is structured and queryable, BioPilot makes it much easier to compare experiments, run Bayesian optimisation on process parameters, and let AI suggest next-step experiments. The pricing is kept aggressively low (free tier plus a simple paid tier based on storage), so smaller biotech and academic groups can access capabilities that are normally reserved for organisations with large software budgets. In short, BioPilot aims to become the operating system for cell therapy R&D: a single, affordable platform where experiments are planned, executed, monitored and analysed in a way that is standardised enough for automation and flexible enough for real-world biology.