AI DATA · MODEL EVALUATION · MULTILINGUAL AI · SOVEREIGN AI

AI solutions for industries where data, language and control are critical

Pangeanic works with AI labs, public institutions and enterprises that need dependable data, expert evaluation, multilingual intelligence and controlled AI deployment. Our work connects training and evaluation datasets, human feedback, model alignment, adaptive language technologies and sovereign AI infrastructure.

Different industries fail in different places. An AI lab may need expert rubrics and evaluation data. A government agency may need sovereign deployment and anonymization. A manufacturer may need terminology controlled multilingual workflows. We build around the operational constraint, rather than forcing every organization into the same AI stack.

Discuss your AI requirements Explore Data for AI Explore Sovereign AI

AI Labs Training data, evaluation, expert feedback and alignment

Public Sector Secure multilingual AI and sovereign deployment

26+ years Language data, machine translation and multilingual AI

Worldwide Multilingual and multimodal AI data operations

A Representative Vendor in the December 2024 "Emerging Tech: Conversational AI"

A Representative Vendor in the 2024 "Market Guide for Data Masking and Synthetic Data"

A Sample Vendor in the 2023, 2024 "Hype Cycle^TM for Natural Language Technologies"

INDUSTRY AI REQUIREMENTS

Different industries. Different AI constraints.

Reliable AI depends on more than model choice. Data availability, evaluation criteria, language coverage, privacy, deployment architecture and human expertise vary sharply between sectors. Pangeanic connects the data, evaluation, language and deployment layers required to move AI systems into production.

01 AI Labs & Technology

Build better models with better data, evaluation and expert judgment

AI labs and technology companies need more than annotation capacity. They need training and evaluation data with known provenance, expert judgment, explicit scoring criteria and repeatable methods for measuring model behaviour across languages, domains and tasks.

Data for AI AI Evaluation & QA Model Alignment & RLHF

Training & adaptation data Multilingual text, speech, image, video, document and multimodal datasets sourced or collected to specification.

Evaluation rubrics Task specific criteria, scoring dimensions, evaluation guidelines and adjudication processes for structured model assessment.

Expert evaluation Domain specialists and native language experts evaluating accuracy, reasoning, relevance, safety and task performance.

Alignment & preference data Human feedback, comparisons, rankings and policy aware supervision for model refinement and release validation.

02 Government & Public Sector

AI under institutional control

Public institutions need multilingual AI systems that protect sensitive information, preserve auditability and operate within defined infrastructure, governance and security constraints.

Sovereign and private AI deployment
Multilingual document intelligence
Data anonymization and masking
Secure translation and knowledge access
Human oversight and evaluation

Sovereign AI Data Masking

03 Defense & Law Enforcement

Multilingual AI where deployment boundaries are strict

Sensitive environments require local control of data, models and infrastructure, together with multilingual processing that can operate without exposing information to uncontrolled external services.

On premises and air gapped deployment
Secure multilingual processing
Speech and language data
Controlled model adaptation
Traceable human review

Controlled AI Deployment

04 Financial & Regulated Industries

Govern data before asking AI to use it

Financial services, legal operations and other regulated environments need AI architectures built around privacy, traceability, evaluation and controlled access to institutional knowledge.

PII detection and anonymization
Governed enterprise data
Private retrieval and knowledge systems
Model evaluation and quality assurance
Human validation workflows

AI Data Operations Evaluation & QA

05 Media & Global Content

Scale multilingual content without losing editorial control

Publishers, broadcasters and global content teams need fast multilingual production while preserving terminology, editorial style, quality thresholds and human control over what reaches an audience.

Deep Adaptive AI Translation
Machine Translation Quality Estimation
Terminology and style adaptation
Multilingual content workflows
Human review based on confidence

Deep Adaptive AI Translation MTQE

06 Automotive & Manufacturing

Turn specialist language and technical data into operational AI

Industrial organizations produce valuable technical content, terminology and multilingual assets that can support model adaptation, document automation and high quality multilingual production.

Domain specific datasets
Technical terminology assets
Adaptive multilingual workflows
Document intelligence
Quality estimation and human review

Adaptive Translation Data for AI

07 Healthcare & Life Sciences

High quality AI data where terminology and privacy are inseparable

Healthcare and life sciences workflows combine specialist terminology, sensitive information and demanding evaluation requirements. AI systems therefore depend on carefully governed data and expert human validation.

Domain specific language and document data
Expert annotation and evaluation
Multilingual medical terminology
Anonymization and privacy workflows
Controlled human review

Data for AI Expert Evaluation

DEPLOYED EXPERIENCE

AI capabilities proven in real operating environments

Pangeanic has worked across the AI lifecycle: creating data for model development, organizing human feedback and evaluation, adapting language models and deploying multilingual AI inside enterprise and public sector infrastructure.

The common thread is operational AI. Data, models and human expertise are designed around the security, quality, language and governance constraints of the organization.

Explore Pangeanic use cases

01 // AI LABS & RESEARCH

Barcelona Supercomputing Center

Data for AI, human feedback, RLHF and multilingual model development

Pangeanic has worked with the Barcelona Supercomputing Center on customized multilingual datasets, annotation, human feedback, model evaluation and language technology research for Spanish, Catalan and other languages.

Custom datasets · Annotation · Human feedback · RLHF · Model evaluation

View BSC use case →

02 // GOVERNMENT & PUBLIC SECTOR

Spanish Tax Agency · AEAT

Enterprise multilingual AI for a national public administration

Pangeanic provides the Spanish Tax Agency with an ECO based document translation environment serving approximately 25,000 geographically and functionally distributed public employees across multiple language pairs.

Public sector AI · Document processing · Multilingual workflows · Enterprise scale

View AEAT use case →

03 // DEFENSE & LAW ENFORCEMENT

Veritone · U.S. DoD Iron Bank

Private multilingual AI for security sensitive environments

Pangeanic delivered a customized translation engine for Veritone, containerized for deployment through the U.S. Department of Defense Iron Bank environment, with specialized language handling and processing inside controlled infrastructure.

Private AI · Secure deployment · Specialized models · Controlled infrastructure

View Iron Bank use case →

04 // MEDIA & PUBLISHING

Agencia EFE

Adaptive multilingual production integrated into the newsroom

EFE integrates Pangeanic technology into its editorial workflow for continuous multilingual news production, combining adaptive machine translation, APIs, terminology control and AI assisted review.

103M

words processed in 2024

Adaptive AI translation · APIs · Editorial workflows · Terminology control

View EFE use case →

05 // AUTOMOTIVE & MANUFACTURING

BYD Auto Japan

Domain adaptation for automotive terminology, style and content

BYD Auto Japan uses Pangeanic's Deep Adaptive AI Translation technology to adapt multilingual output to automotive terminology, technical specifications and company language across Chinese, Japanese and English content workflows.

Deep Adaptive AI Translation · Terminology adaptation · Domain data · Enterprise workflows

View BYD Auto Japan use case →

06 // MULTILINGUAL AI EXPERIENCE

From language data to production AI

Pangeanic's current AI capabilities build on more than two decades of multilingual data, machine translation, European research, privacy technology and production language systems.

That experience now supports training data, model evaluation, human feedback, model alignment and sovereign AI deployment.

Research & Publications

ONE OPERATIONAL AI LIFECYCLE

From the data a model learns from to the system an organization can trust

Most enterprise AI problems do not fit neatly into a single service. A model may need better training data, specialist evaluation, human feedback, domain adaptation, privacy controls and a deployment architecture that keeps sensitive information under organizational control.

Pangeanic connects those stages. We can enter at one point in the lifecycle or operate several of them as one program, with multilingual and multimodal data moving through preparation, evaluation, adaptation and production.

Training data Evaluation data Human feedback Model alignment SLM customization Sovereign AI Multilingual AI

PANGEANIC AI OPERATING CHAIN

01 · Source, license or collect Existing datasets, custom data collection and sourcing programs for text, speech, audio, image, video, documents and multimodal AI. Explore Data for AI

02 · Prepare, annotate and enrich Cleaning, segmentation, labeling, metadata engineering, annotation, human review, quality control and governed delivery. Explore AI Data Operations

03 · Evaluate against explicit criteria Benchmark design, task specific rubrics, scoring dimensions, expert evaluation, error analysis and regression testing. Explore AI Evaluation & QA

04 · Align with human judgment Preference data, rankings, comparisons, expert feedback and multilingual human supervision for model improvement. Explore Model Alignment

05 · Adapt models to the task Model selection, fine tuning, domain adaptation, terminology, retrieval grounding and task specific small language models. Explore SLM Customization

06 · Protect sensitive data Multilingual detection and masking of personal and confidential information before data enters downstream AI workflows. Explore Data Masking

07 · Deploy under your control Private cloud, on premises and controlled AI architectures for organizations that cannot hand their data, models or operational knowledge to public services. Explore Sovereign AI

08 · Operate multilingual AI at scale Adaptive translation, terminology control, automated quality estimation, APIs and human review for multilingual production environments. Explore Deep Adaptive AI Translation

START WITH THE REQUIREMENT

What do you need your AI program to achieve?

Buyers rarely arrive asking for an abstract technology category. They arrive with a data gap, an evaluation problem, a model that needs improvement, a privacy constraint or a production workflow that must perform reliably. The table below maps those requirements to the relevant Pangeanic capability.

Buyer requirement	Pangeanic capability	Typical output
We need usable AI data now.	Datasets for AI	Ready to license or project specific multilingual and multimodal datasets for training, adaptation, evaluation and testing.
We need data that does not already exist.	Data for AI	Custom sourcing and collection programs defined around modality, population, language, rights, format and acceptance criteria.
We have raw data but it is not model ready.	AI Data Operations	Preparation, annotation, enrichment, metadata, quality control, human validation and versioned delivery.
We need a controlled annotation and review workflow.	PECAT	Multilingual annotation, human feedback, review, evaluation, quality control and project delivery workflows.
We need to know whether our model is actually good enough.	Evaluation & AI QA	Evaluation design, rubrics, benchmarks, human scoring, model comparison, error analysis and release criteria.
We need better model behaviour, not just a larger dataset.	Model Alignment & RLHF	Preference data, expert rankings, human feedback and structured supervision for model refinement.
We need a specialist model for our own domain.	Small Language Model Customization	Base model selection, domain data preparation, fine tuning, retrieval grounding, evaluation and task specific adaptation.
Our information cannot leave our environment.	Sovereign AI	Private cloud, on premises and controlled deployment architectures with organizational control over data, models and access.
We need to use sensitive data safely.	Data Masking & Anonymization	Detection, masking, redaction and pseudonymization of personally identifiable and sensitive information.
We need multilingual production with domain specific quality.	Deep Adaptive AI Translation + MTQE	Adaptive translation using terminology, translation memories and style requirements, combined with reference free quality estimation and targeted human review.
We need one controlled environment for multilingual AI workflows.	ECO Intelligence Platform	Secure document processing, multilingual retrieval, adaptive translation, quality estimation, API integration and human review workflows.

WHY THE CAPABILITIES FIT TOGETHER

AI becomes operational when data, human judgment, models and infrastructure are treated as one system

Pangeanic sits at the intersection of four disciplines that are too often purchased separately. Bringing them together shortens the distance between acquiring data and running an AI system under real production constraints.

01 · DATA

Multilingual and multimodal Data for AI

Existing datasets and custom collection for model training, adaptation, grounding, testing and evaluation across languages and data modalities.

Text · Parallel corpora · Speech · Audio · Images · Video · Documents · Multimodal data

Explore Data for AI

02 · HUMAN JUDGMENT

Evaluation, rubrics and expert feedback

AI systems need explicit criteria for what good means. Pangeanic supports multilingual evaluation, domain expertise, preference data, scoring, adjudication and human feedback.

Rubric design · Expert evaluation · Rankings · Preference data · QA · Human review

Explore AI Evaluation

03 · MODEL ADAPTATION

Models adapted to tasks, domains and language

Generic models are not always the right production system. Pangeanic works with task specific model customization, domain data, retrieval grounding and adaptive language technologies.

SLMs · Fine tuning · RAG · Terminology · Domain adaptation · Multilingual AI

Explore Model Customization

04 · CONTROL

Sovereign deployment and governed operations

Sensitive AI workloads require more than good model output. Organizations need control over data location, model access, privacy, evaluation and human oversight.

Private cloud · On premises · Controlled infrastructure · Privacy · Governance · Human oversight

Explore Sovereign AI

HOW ORGANIZATIONS WORK WITH US

Start with the part of the problem you actually need solved

An engagement can begin with a dataset request, a collection specification, an evaluation problem, an existing model or a deployment constraint. The scope expands only when the use case requires it.

DATASET PROCUREMENT

License existing AI data

For teams that need a defined dataset or want to know whether suitable multilingual or multimodal assets already exist.

Useful information: modality, language, hours or volume, demographic requirements, usage rights and technical format.

Browse Datasets for AI

CUSTOM DATA PROGRAM

Collect or build data to specification

For AI teams that need participants, specialist domains, hard to source languages, bespoke modalities or defined acceptance criteria.

Scope may include recruitment, consent, collection, annotation, transcription, metadata, QA and delivery.

Discuss Custom Data

EVALUATION & ALIGNMENT

Measure and improve model behaviour

For model developers that need evaluation datasets, rubrics, domain experts, multilingual judges, preference data or release validation.

Scope can be defined around tasks, model families, languages, failure modes, scoring criteria and target thresholds.

Discuss Model Evaluation

PRIVATE AI SYSTEM

Adapt and deploy AI under your control

For organizations that already have proprietary knowledge, documents or workflows and need AI adapted to their environment without surrendering operational control.

Scope may combine SLM customization, RAG, anonymization, evaluation, multilingual processing and private deployment.

Discuss Sovereign AI

SCOPE A PROJECT FASTER

A useful first conversation starts with constraints, not buzzwords

You do not need to arrive with a finished technical specification. But the more clearly we understand the system you are building, the faster we can determine whether the requirement is principally a data, evaluation, adaptation or deployment problem.

Discuss your AI project

MODEL & TASK

What is the system expected to do?

Training, fine tuning, evaluation, speech recognition, multimodal understanding, retrieval, translation, classification or another defined task.

DATA

What evidence does the system need?

Modality, languages, domains, volumes, populations, formats, provenance requirements and usage rights.

QUALITY

How will success be judged?

Benchmarks, rubrics, acceptance thresholds, domain experts, error categories, human evaluation or production KPIs.

CONTROL

Where can the data and models operate?

Public cloud, private cloud, VPC, on premises, controlled infrastructure or environments with additional privacy and security restrictions.

INDUSTRY AI FAQ

Questions organizations ask before starting an AI data or model program

The right route depends on whether the bottleneck is data, model behaviour, language coverage, expert evaluation, privacy or deployment.

What does Pangeanic provide to AI labs and model developers?

Pangeanic provides multilingual and multimodal training and evaluation data, custom data collection, annotation, metadata, human feedback, preference data, model evaluation, rubric based assessment, model alignment and expert review. Projects can cover one stage of model development or several stages as one governed data operation.

Can Pangeanic create custom AI training and evaluation datasets?

Yes. Custom programs can be defined around the required modality, languages, participant profile, domain, volume, rights, technical format and acceptance criteria. Collection can be combined with transcription, annotation, metadata, human review and quality assurance.

Does Pangeanic support AI model evaluation and evaluation rubrics?

Yes. Evaluation programs can define the dimensions on which model behaviour should be judged, the scoring criteria for each dimension, evaluator instructions, benchmark data, expert or native language review, adjudication rules and acceptance thresholds. The resulting evidence can be used for model comparison, regression testing, alignment and release decisions.

What types of AI data can Pangeanic source or collect?

Pangeanic works across text, parallel corpora, speech, audio, images, video, documents and multimodal datasets. Requirements can include particular languages, speaker or participant profiles, specialist domains, recording conditions, metadata, consent requirements and quality thresholds.

What is the difference between Data for AI, Datasets for AI and AI Data Operations?

Data for AI covers the services used to source, license, collect, prepare and validate the data required for an AI task. Datasets for AI are the resulting data assets available for licensing or created specifically for a project. AI Data Operations is the continuous operating layer that manages data preparation, annotation, human feedback, evaluation, quality control, governance and production learning across the AI lifecycle.

Can Pangeanic work with sensitive or regulated data?

Pangeanic provides data masking and anonymization capabilities and can design workflows in which sensitive information is processed within controlled environments. For AI deployments that require stronger infrastructure control, Pangeanic also develops private, on premises and sovereign AI architectures.

Can Pangeanic customize small language models for a specific company or domain?

Yes. Pangeanic can select and customize task specific small language models using domain data, fine tuning, retrieval augmented generation, terminology adaptation and evaluation. The objective is to match the model and architecture to the actual task rather than automatically relying on a general purpose public LLM.

Does Pangeanic still provide machine translation technology?

Yes. Machine translation remains part of Pangeanic's language technology stack, but modern deployments can combine adaptive models, customer terminology and translation memories, Machine Translation Quality Estimation, APIs, human review and private deployment. Deep Adaptive AI Translation is designed for organizations that need multilingual production adapted to their own content and language.

Does an organization need to buy the complete Pangeanic AI stack?

No. An engagement can begin with a dataset purchase, a custom collection, an evaluation exercise, an alignment program, model customization, multilingual production or a private deployment requirement. Additional layers are included only when the use case requires them.

How should we start a conversation with Pangeanic?

Share the task you are trying to solve and, where available, the data modality, languages, expected volumes, model or system involved, quality criteria, usage rights and security constraints. Pangeanic can then determine the most appropriate data, evaluation, adaptation or deployment route.

YOUR INDUSTRY. YOUR DATA. YOUR CONSTRAINTS.

Tell us what your AI system needs to learn, prove or control

Whether the requirement begins with training data, evaluation rubrics, human feedback, a specialist model, multilingual production or sovereign deployment, we can start with the constraint that matters to your project.

Discuss your AI project Explore real use cases