Data services for AI systems that need to work in the real world

Data services for AI systems that need to work

Pangeanic turns raw multilingual, multimodal and domain-specific data into assets for AI training, fine-tuning, evaluation, alignment and governance.

We support collection, licensing, cleaning, annotation, human review, evaluation, anonymization and governance across languages, modalities and regulated domains.

What is data for AI?

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

What does a Data for AI provider do?

A Data for AI provider helps organizations source, collect, license, clean, annotate, evaluate, anonymize and govern the data required for AI training, fine-tuning, RAG, model alignment and production evaluation.

Built for production

Production AI needs data that reflects the actual use case, the domain, the language, the compliance environment and the quality thresholds of the organization. Generic volume collected at lowest cost rarely creates dependable systems.

Data for AI

The broad service layer: sourcing, licensing, collection, cleaning, annotation, evaluation, human feedback, anonymization and governance.

Datasets for AI

Specific data assets available for licensing or procurement, including text, speech, image, video, OCR, parallel corpora and domain-specific data.

AI Data Operations

The continuous operating model that keeps data, feedback, evaluation, governance and human review running across the AI lifecycle.

AI data sourcing

We identify, license, collect and structure the right data sources for model training, fine-tuning, grounding and evaluation across languages, domains and modalities.

Bespoke data collection

When existing datasets do not match the requirement, we design custom collection projects around language, domain, format, consent, annotation depth and compliance constraints.

Cleaning and preparation

We clean, normalize, deduplicate, validate, segment and enrich data so it becomes usable for AI workflows rather than merely accessible in storage.

Annotation and human review

Human-in-the-loop workflows for labeling, classification, preference ranking, multilingual review and expert validation, managed through auditable quality pipelines.

Evaluation data

Benchmark sets, gold-standard references, human evaluation protocols, MTQE workflows and quality gates for model comparison, regression testing and continuous alignment.

Privacy and governance

Data masking, multilingual anonymization, traceability, consent-aware workflows and secure handling for regulated environments, including workflows used by the Spanish Ministry of Justice and the European Commission’s DG Translation.

Data for language, vision, speech and knowledge systems

AI systems need the right data, in the right format, with the right metadata, provenance, language coverage and evaluation logic built in from the start.

Pangeanic works across text, speech, audio, image, video, OCR, parallel corpora, multilingual knowledge bases and human feedback data, with particular depth in European, co-official, low-resource and regulated-domain languages.

Common AI data categories

AI labs and model builders

Training data, instruction data, evaluation sets, human preference data and multilingual corpora for frontier and domain-specific models.

Enterprises deploying AI

Data preparation, knowledge grounding, evaluation workflows and domain-specific adaptation for production AI systems.

Governments and regulated sectors

Secure, auditable, privacy-aware and multilingual data workflows for sensitive environments where generic cloud pipelines are not an option.

Language and knowledge platforms

Translation data, transcription data, terminology, multilingual search, classification and content intelligence systems.

Language depth built through real projects

Multilingual and multicultural data workflows across European, co-official and low-resource languages, including data annotation, RLHF and training data work for BSC’s Salamandra and ALIA language models.

Human review at production scale

Expert labeling, evaluation, preference data, RLHF workflows and QA through managed, expert-reviewed pipelines with traceable quality gates.

Governed and sovereign by design

Multilingual anonymization, privacy-aware data processing, on-premise, private cloud and air-gapped deployment options for organizations where public cloud pipelines are not acceptable.

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.

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.

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.

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.

Does data for AI include human feedback and RLHF?

Yes. Human feedback, preference ranking, expert judgments, instruction data and RLHF workflows are important forms of data for AI, especially when models need to be aligned with task, policy, language or domain expectations. See AI Data Operations for the full alignment pipeline.

What makes AI data high quality?

High-quality AI data is relevant to the task, representative of real-world usage, legally usable, well-structured, properly annotated, traceable, validated by domain experts, and suitable for evaluation or production workflows. Volume alone does not create quality; governance does.

Can Pangeanic handle sensitive or regulated data?

Yes. Pangeanic supports privacy-aware multilingual data workflows, including anonymization, data masking, secure review and controlled deployment. Workflows can be deployed on-premise, in a private cloud or in air-gapped environments for organizations where public cloud pipelines are not permitted.