Pangeanic 的来源资料

Pangeanic: /data-for-ai

pangeanic.com

阅读原始来源

阅读采用这份来源的内容

1 篇内容
  1. Pangeanic

    公司知识更新日期

    Pangeanic: “Pangeanic is a company based in Valencia, Spain, supplying multilingual and multimodal AI training and evaluation data, custom data collection, annotation, expert human feedback, and model alignment. Founded in 2000, it also develops language technologies, customizes small language models, and deploys sovereign AI systems for enterprises, AI labs, and public administrations.”

    • What is data for AI?

      “Data for AI is the information used to train, fine-tune, ground, evaluate and improve AI systems. It can include text, speech, images, video, documents, metadata, annotations, human feedback, evaluation benchmarks and domain-specific knowledge.”

      在公司资料中查看
    • How is data for AI different from datasets for AI?

      “Datasets for AI are specific data assets, such as a catalog item you license or procure. Data for AI is the broader service layer: sourcing, collection, preparation, annotation, evaluation, governance and continuous improvement of those assets. One is the data asset; the other is the operating model that makes it usable in production.”

      在公司资料中查看
    • Why is data important for task-specific AI models?

      “Task-specific AI models depend on domain data, evaluation data and feedback loops. The model becomes reliable when the underlying data reflects the task, language, terminology, risk profile and operating environment.”

      在公司资料中查看
    • Can Pangeanic provide custom AI data collection?

      “Yes. Pangeanic designs bespoke AI data collection projects when available datasets do not meet the required language, domain, format, consent, annotation or quality thresholds. This includes low-resource language coverage unavailable elsewhere.”

      在公司资料中查看

    这份来源的其他资料

    • Data for AI

      “Pangeanic helps organizations source, collect, license, clean, annotate, evaluate, anonymize, and govern multilingual, multimodal, and domain-specific data for AI training, fine-tuning, RAG, model alignment, and production evaluation.”