“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.”
“Licensing of existing datasets and custom collection across multilingual text, parallel corpora, speech, audio, images, video, documents, and multimodal data, with project requirements defining provenance, usage rights, formats, and acceptance criteria.”
“Data sourcing, collection, annotation, metadata engineering, human review, evaluation, preference data, quality control and governed delivery workflows for AI systems.”
“Multilingual model evaluation, preference data, expert ranking, human feedback and RLHF workflows designed to align AI behavior with task, domain, policy and quality requirements.”
“Selection, customization, fine tuning, terminology adaptation, retrieval grounding, evaluation and controlled deployment of task specific language models.”
“Private cloud, on premises, controlled and air gapped multilingual AI deployment with organizational governance over data, models, evaluation, privacy and human oversight.”
“Prepare, structure, annotate, enrich and validate data for training, fine-tuning, grounding and evaluation workflows.”
“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.”
“Multilingual data masking and anonymization for personally identifiable information and sensitive enterprise data used in RAG, model training, fine-tuning, evaluation, analytics and controlled AI workflows.”
“AI Data Operations is the continuous system for sourcing, licensing, preparing, annotating, evaluating, governing and improving the data and human feedback used across the AI lifecycle.”
“Annotation is one workflow within the system. AI Data Operations also covers data discovery, rights, collection, preparation, human feedback, evaluation sets, privacy controls, provenance, versioning and production feedback.”
“No. A program can begin with an existing corpus or with a model objective and a documented data gap. Pangeanic can identify, license or collect the missing material before preparing it for training or evaluation.”
“The answer depends on the rights attached to each component. The applicable position may be ownership, a perpetual license, a limited license or contractual access. Pangeanic documents that position before contracting and does not present licensed third party material as transferable property.”
“They treat language, locale, dialect, terminology and cultural context as operating variables. Data specifications, native review and evaluation sets can therefore be designed separately for the markets and conditions in which the system will run.”
“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.”
“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.”
“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.”
“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.”
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