Rancho BioSciences

Bad data breaks good models. Rancho’s AI-ready datasets are 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞-𝐫𝐞𝐚𝐝𝐲 𝐢𝐧 𝐂𝐒𝐕, 𝐉𝐒𝐎𝐍, 𝐡5𝐚𝐝, 𝐒𝐞𝐮𝐫𝐚𝐭, 𝐚𝐧𝐝 𝐒𝐜𝐚𝐧𝐩𝐲—cutting 6–9 𝐦𝐨𝐧𝐭𝐡𝐬 of engineering rework. Feed your ML pipelines with data built for scale.

Scientists spend up to 80% of their AI project time cleaning data. Rancho changes that. Access 100M+ expertly curated single cells spanning oncology, neuro, and immunology—already powering biomarker discovery and scGPT model training.

Metadata chaos costs months. Rancho’s AI-ready datasets are 𝐨𝐧𝐭𝐨𝐥𝐨𝐠𝐲-𝐚𝐥𝐢𝐠𝐧𝐞𝐝 𝐭𝐨 𝐔𝐁𝐄𝐑𝐎𝐍, 𝐂𝐞𝐥𝐥 𝐎𝐧𝐭𝐨𝐥𝐨𝐠𝐲, 𝐚𝐧𝐝 𝐃𝐢𝐬𝐞𝐚𝐬𝐞 𝐎𝐧𝐭𝐨𝐥𝐨𝐠𝐲—cutting prep time by up to 80%. Free your team to focus on insights, not repetitive cleanup.

You would never call a variant at 2x coverage. Language models give you no coverage at all. Julie Segueni, PhD, on what actually reduces hallucination risk.

The larger model often guesses more, not less. Across 26 models on one 6,000-question factuality benchmark, hallucination rates run from 22 to 94 percent. Scale is not fixing this. Waiting for better models is not a data strategy. Dr. Julie SEGUENI presents which data controls reduce risk in biomedical AI, and which ones do not. Worked examples from single-cell atlas construction and rare cell discovery in liver disease. Dr. Jane Lomax moderates the Q&A. Expect real datasets and named methods, and expect Julie to tell you where the curation effort wasn't worth it.

Curated, ontology-aligned data can cut hallucination rates in models like GPT-4 and Llama2 by ~40%. Get the data behind the claim.