AI Evaluation Services
Human-in-the-loop AI evaluation — RLHF, prompt evaluation, model testing, and safety review that makes AI systems smarter, safer, and more aligned.
Evaluation Services
Comprehensive human evaluation capabilities for every stage of AI model development.
Human Feedback (RLHF)
Reinforcement Learning from Human Feedback — structured preference data, ranking, and comparison tasks that align AI models with human values.
- Preference ranking
- Pairwise comparison
- Response rating
- Multilingual RLHF
- Domain-specific panels
Prompt Evaluation
Expert evaluation of LLM prompts and responses — assessing quality, accuracy, helpfulness, and safety across diverse use cases.
- Response quality scoring
- Factual accuracy review
- Helpfulness assessment
- Tone & style evaluation
- Multilingual evaluation
Model Testing
Structured human testing of AI model outputs — identifying failure modes, edge cases, and performance gaps across languages and domains.
- Edge case identification
- Failure mode analysis
- Cross-language testing
- Domain stress testing
- Regression testing
Dataset Validation
Expert review of training datasets for quality, bias, and accuracy — ensuring your data is clean, balanced, and model-ready.
- Quality auditing
- Bias detection
- Label consistency review
- Coverage analysis
- Remediation guidance
LLM Evaluation
Comprehensive evaluation of large language model outputs — reasoning quality, instruction following, multilingual capability, and safety.
- Reasoning assessment
- Instruction following
- Multilingual capability
- Hallucination detection
- Safety evaluation
Safety & Red-Teaming
Adversarial testing and safety evaluation — identifying harmful outputs, jailbreak vulnerabilities, and policy violations in AI systems.
- Adversarial prompting
- Harm detection
- Policy compliance
- Jailbreak testing
- Bias & fairness review
Evaluation Frameworks
We support multiple evaluation methodologies to match your model development workflow.
Preference Ranking
Annotators rank multiple AI responses from best to worst based on defined criteria.
Likert Scale Scoring
Structured 1–5 or 1–7 quality ratings across multiple dimensions per response.
Binary Classification
Pass/fail or acceptable/unacceptable judgments for safety and policy compliance.
Comparative Evaluation
Side-by-side comparison of model versions to measure improvement over time.
Improve Your AI Model Quality
Tell us about your model and evaluation needs — we'll design a human feedback pipeline that drives measurable improvement.
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