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AI Data Services / Data as a Service

Multilingual AI Training Data Services

We build, enrich, and validate multilingual datasets for AI systems: speech data, parallel corpora and text corpora, annotation, transcription, metadata, and human review workflows for LLMs, ASR, machine translation, and enterprise NLP solutions.

Whether you need fresh data collected from scratch, existing assets improved, or evaluation-ready datasets for fine-tuning and benchmarking, Pangeanic delivers production-oriented data pipelines designed for measurable model performance.

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Multilingual by design European and global language expertise across speech, text, and domain-specific data workflows.
 
Built for production Training, fine-tuning, evaluation, and dataset improvement workflows aligned to enterprise use cases.
 
More than annotation Language technology, MT, NLP, QA, and human review expertise under one delivery model.

Why the human intelligence layer in AI alignment matters

AI success is no longer determined only by model size. It is determined by the quality of the data pipeline behind the model: what was collected, how it was structured, which human signals were captured, how outputs were evaluated, and whether deployment can withstand enterprise, legal, and multilingual realities.

Pangeanic brings together language technology expertise, large-scale multilingual data operations, enterprise-grade privacy awareness, and years of work in translation, adaptation, data preparation, and AI enablement.

  • Multilingual by design: language, locale, terminology, and domain sensitivity built into the workflow.
  • Enterprise-oriented: suitable for regulated, privacy-conscious, and quality-driven environments.
  • Model lifecycle support: from training data to alignment, evaluation, and post-deployment optimization.
Why Data Quality Wins

Better models start with better data pipelines

Model performance is not determined by architecture alone. In production, much of the difference comes from the quality, structure, diversity, and consistency of the training and evaluation data behind the system.

That is why enterprises are investing more in curated, task-specific datasets: multilingual speech corpora, annotated text, evaluation sets, de-identified records, domain terminology, and human feedback loops that help models perform more reliably in the real world.

Pangeanic combines language technology, machine translation, NLP, speech processing, and human validation into one integrated service layer. We do not simply annotate data. We prepare it for measurable downstream impact: better recognition, better terminology control, better semantic accuracy, and faster adaptation.

This makes us a strong partner for organizations building custom AI systems, refining proprietary datasets, or improving multilingual model behavior across high-value workflows.

 
Capabilities

Data services for modern AI systems

Speech Data Collection & Processing

Production-ready datasets for ASR, voice assistants, conversational AI, and speech analytics.

  • Multilingual and multi-dialect speech collection
  • Studio, telephony, field, and in-the-wild audio
  • Time-aligned transcription and segmentation
  • Speaker diarization and metadata capture

Text & NLP Annotation

Structured data for LLMs, RAG pipelines, search, classification, and domain adaptation.

  • Named Entity Recognition (NER)
  • Intent, sentiment, and semantic tagging
  • Parallel corpora and terminology alignment
  • Classification, redaction, and content labeling

Dataset Preparation & Enrichment

Transform raw or inconsistent assets into cleaner, trainable, better-documented datasets.

  • Data cleaning, filtering, and normalization
  • Metadata enrichment and taxonomy design
  • De-duplication and quality controls
  • Bring Your Own Data (BYOD) optimization

Evaluation & Human Feedback

Human-in-the-loop workflows to validate outputs, benchmark systems, and improve quality.

  • Human review and adjudication
  • MTQE and multilingual quality evaluation
  • Error analysis and benchmark design
  • Ranking, comparison, and feedback loops
Delivery Model

Collect new data, improve existing data, or combine both

Custom Collection

Commission new datasets for specific domains, languages, accents, channels, or task requirements when off-the-shelf material is not enough.

Existing Dataset Improvement

Bring your own data and we will clean, normalize, relabel, enrich, and validate it so it becomes more usable for training, fine-tuning, or evaluation.

Hybrid Data Pipelines

Combine proprietary assets with newly collected material, human review, metadata enrichment, and evaluation workflows to create higher-value datasets.

Female data annotator fully focused on multilingual , multimodal AI annotation
Expert Perspective

Research and engineering leadership behind the work

Manuel Herranz

Manuel Herranz2

CEO, Pangeanic

“Enterprises do not need more raw data. They need the right data, structured, validated, and aligned to the model behavior they want in production.”

José Miguel Herrera

Jose Miguel

PhD, Machine Learning

“Training data quality is not a support function. It is one of the main variables behind model accuracy, robustness, and adaptation to real-world workflows.”

Juan Luis García Mendoza

PhD, AI Research & Language Technologies

“For multilingual AI, dataset design matters as much as model choice. Evaluation sets, annotation consistency, and domain coverage often determine whether a system scales reliably or fails quietly.”
team of ML ENGINEERS verifying AI training data integrity; focused looking at monitors
Why Pangeanic

More than annotation. Data operations for multilingual AI.

Many providers can label or transcribe data. Fewer understand how multilingual data affects machine translation, ASR, retrieval, evaluation, and downstream enterprise workflows.

Pangeanic stands apart because our work is rooted in language technology. We bring experience from machine translation, NLP, speech processing, model evaluation, and multilingual AI deployments.

Our teams work across the full data lifecycle: collection, annotation, quality assurance, de-identification, evaluation, and continuous improvement. That makes us particularly effective when clients need more than volume — when they need precision, consistency, and relevance to real model behavior.

We have supported language technology initiatives linked to research infrastructures, global technology environments, multilingual evaluation, and large-scale data preparation workflows where quality cannot be improvised.

PECAT tool for multimodal, multilingual AI Training data

Featured in the Gartner® Hype Cycle™ for Natural Language Technologies (2023, 2024), Vendor in Conversational AI (December 2024), and Synthetic Data & Data Masking (July 2024)

Gartner’s analysis of risks and opportunities in language technology adoption highlights Pangeanic’s leadership in the field:

  • Sample Vendor Recognition: Pangeanic is recognized as a Sample Vendor for Neural Machine Translation (NMT) in the 2023 and 2024 Hype Cycle reports.
  • Advanced Customization: The report highlights our specialized capability to adapt and fine-tune linguistic models to the unique, high-precision needs of our clients, from Farsi machine translation for OSInt to Arabic to Russian machine translation, and to specific models with slang and drug cartel jargon.
  • Strategic Foundation for SLMs: Our government- and industry-validated expertise in Neural Machine Translation customization serves as the technical cornerstone for our larger specialized Small Language Model (SLM) development.
  • Representative Vendor in Gartner's Emerging Tech: Conversational AI. 

Read the full report

Pangeanic list of mentions in Gartner® Market Guides, Emerging Tech reports as representative vendors and Hype Cycles™
Outcomes

What better data improves

Higher Accuracy

Improve recognition, extraction, translation, and semantic consistency across multilingual workflows.

Faster Adaptation

Reduce the time needed to prepare domain-specific data for fine-tuning, benchmarking, and deployment.

Lower QA Overhead

Catch issues earlier in the data pipeline so production systems require less downstream correction.

More Reliable AI

Build datasets that support better multilingual coverage, better domain fit, and more dependable outputs. Your model is only as good as the data behind it…. And the model alignment behind it!

 

Let’s design the data pipeline your AI actually needs.
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Companies that trust Pangeanic

Spanish news agency EFE uses Pangeanic's machine translation to translate incoming news and pre-draft journalistic content Pangeanic has supplied training material to Amazon to create its Amazon Translate European Commission (EU R&D and projects that need machine translation support) microsoft has used Pangeanic datasets for Bing Translator IATA has been a client of Pangeanic services DeepL has used Pangeanic for testing of some of its models FIFA Medical is a long-standing client of Pangeanic-which optimizes processes with custom machine translation omron has been a client of Pangeanic document machine translation services SUBARU uses Pangeanic's machine translation technologies via associates Daitec world council of churches Healthcare company Zoll uses Pangeanic's machine translation and human translation services
FAQ

Frequently Asked Questions About Multilingual AI Training Data Services

What are multilingual AI training data services?

Multilingual AI training data services prepare the datasets used to train, fine-tune, and evaluate AI systems across languages. These services include speech data collection, transcription, text annotation, metadata enrichment, dataset cleaning, and benchmark creation for LLMs, ASR, machine translation, and enterprise NLP.

Can you improve our existing datasets instead of collecting new data?

Yes. Pangeanic can clean, normalize, enrich, relabel, validate, and restructure existing datasets so they become more useful for training, fine-tuning, or evaluation workflows.

What types of data do you handle?

Pangeanic works with speech, text, and selected multimodal datasets. Services include transcription, diarization, NER, sentiment and intent annotation, metadata enrichment, parallel corpora preparation, and benchmark dataset creation.

How do multilingual datasets improve AI systems?

Well-designed multilingual datasets improve language coverage, domain adaptation, terminology consistency, and model reliability across markets. They help reduce bias toward high-resource languages and support better real-world performance.

Do you also support evaluation and human review?

Yes. Pangeanic supports evaluation-ready datasets, gold sets, benchmark creation, multilingual quality review, human adjudication, and error analysis to help teams measure and improve model performance.

Professional data annotator working multimodal annotation services
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