Our Services

We help AI teams systematically evaluate large language model outputs to identify and reduce:

• Hallucinations and factual errors
• Bias and unsafe responses
• Inconsistency across prompts or languages
• Quality degradation at scale

How we work

• Structured human review frameworks
• Multi-reviewer scoring for consistency
• Clear, actionable feedback for model teams

Best for

• Model validation before deployment
• Ongoing quality monitoring
• Safety and alignment testing

Our team supports multilingual annotation and review to ensure datasets reflect linguistic accuracy and cultural context, not just literal translation.

Supported work includes

• Text classification and labeling
• Prompt–response evaluation
• Translation review and consistency checks
• Multilingual dataset validation

Why it matters
Poor multilingual data leads to unreliable global AI systems. We help teams avoid that.

For AI systems used in sensitive or high-risk domains, human judgment is essential.

We support evaluation and annotation workflows for:

• Mental health and wellbeing applications
• Safety-sensitive conversational AI
• Content requiring careful ethical review

Our approach emphasizes:

• Reviewer training
• Clear guidelines
• Conservative quality thresholds

Each project is delivered using a team-based structure:

• One technical lead responsible for quality and communication
• Trained reviewers selected based on language and domain needs
• Scalable capacity depending on project size and timeline

Clients interact with one point of contact, while benefiting from team execution.

Most engagements begin with a short pilot or evaluation sprint to confirm quality and workflow fit.

Typical formats include:

• 2–4 week evaluation pilots
• Fixed-scope annotation sprints
• Ongoing monthly support

We adapt scope and capacity based on results.