Data curation is the design, filtering, enrichment, and validation of datasets so AI models train on examples that are relevant, traceable, and high signal. InfoBay curates corpus slices for pre-training, supervised fine-tuning, RLHF, benchmarks, and enterprise-specific model behavior.
The strongest AI systems are shaped by what gets included, excluded, cleaned, and documented. InfoBay’s curation approach keeps provenance and model outcomes at the center of the pipeline.
Multilingual call-center and podcast audio for speech and voice AI.
DICOM, reports, and clinical records for medical AI.
ISBN-attributed educational data for reasoning and pre-training.
InfoBay designs datasets around the model behavior a team wants to improve, including reasoning, factuality, multilingual robustness, speech understanding, and domain accuracy.
Curated outputs include source, language, modality, domain, and quality metadata so enterprise teams can evaluate licensing, compliance, and retraining decisions.
Answers for buyers
Yes. InfoBay can clean, structure, enrich, and validate enterprise data into training-ready datasets with agreed privacy and provenance controls.
InfoBay can use augmentation where appropriate, but the core approach emphasizes traceable, expert-verified, and real-world data rather than synthetic-only pipelines.
InfoBay supports pre-training, supervised fine-tuning, RLHF, DPO, factuality evaluation, and benchmark construction.