High-quality training data for AI models, delivered through human-in-the-loop workflows and expert annotation teams
We help AI teams build high-quality training data through managed annotation workflows and experienced human-in-the-loop teams.
From labeling and QA to LLM alignment and sensitive data handling, we support both project-based delivery and long-term data operations.
Flexible AI Data Support
- Annotation Teams & Data Operations: Deploy dedicated annotation teams, QA leads, and ML specialists to manage labeling workflows, quality control, and dataset preparation. Teams operate with clear processes, tooling, and full visibility.
- Permanent AI Data Hiring: Build internal AI data capability by hiring annotators, QA leads, data operations managers, and AI specialists for long-term ownership.
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AI Training Data Services
Multilingual Data Annotation
Access native-language annotators across European and global markets.
Human-in-the-Loop Workflows
Combine automation with expert validation to ensure accuracy, consistency, and explainability.
Compliance & Secure Data Handling
Operate under GDPR-compliant processes with strict data residency and security controls.
Quality Assurance & Data Validation
Ensure high-quality outputs through structured QA processes and dedicated review layers.
Annotation & Labeling Teams
Deploy dedicated annotation teams to label and structure datasets across text, image, and multimodal data.
LLM Alignment & Fine-Tuning Support
Support model alignment, prompt evaluation, and dataset refinement for large language models.
Who We Support
– AI and machine learning teams
– Companies building LLMs and data platforms
– Regulated industries (healthcare, finance)
– Data-intensive organisations
How Our AI Data Support Works
1. Assess your data and model requirements
2. Define annotation workflows and quality standards
3. Deploy annotation teams and QA leads
4. Deliver and scale high-quality training data
Why Human-in-the-Loop Matters
Automated labeling can support early-stage AI development, but often lacks the accuracy and context required for production and regulated environments.
Human-in-the-loop workflows combine automation with expert review, ensuring training data is accurate, explainable, and compliant.
This is critical for healthcare, finance, cybersecurity, and LLM alignment.
Human-in-the-Loop vs Auto-Labeling
Auto-Labeling
- Fast for simple datasets
- Limited context awareness
- Higher error risk
Human-in-the-Loop
- Higher accuracy
- Context-aware decisions
- Strong auditability
Explore Our Other AI Services
AI Training Data Solutions FAQs
We support regulated AI use cases with managed, human-led data workflows, including labeling, annotation, and QA for sensitive datasets used in healthcare, finance, and LLM fine-tuning.
Trained annotators and dedicated QA leads validate data throughout the workflow, ensuring accuracy, consistency, and contextual relevance at scale.
Industries handling sensitive or regulated data, such as healthcare, finance, logistics, and cybersecurity, benefit most from our compliant, human-verified approach.
All projects operate under European data residency with strict access controls and privacy safeguards embedded into the annotation process.
Teams can start with pilot datasets and scale annotation capacity as needed, allowing controlled costs and a smooth transition from experimentation to production.