Multimodal and multilingual validation
Check consistency and quality across text, speech, image, video, and multilingual outputs to support reliable AI performance.
AI model evaluation
Human evaluation for reliable AI performance.
Assess model outputs across accuracy, relevance, reliability, bias, safety, guardrails and multilingual or multimodal behavior.
What we evaluate
Performance, reliability and risk across model behavior.
Evaluation programs are shaped around your product, users, modalities, policies and measurable success criteria.
Output accuracy and relevance
Evaluate AI generated outputs for factual accuracy, coherence, relevance, and alignment with user intent.
Product and document categorization
Classify products, documents, and content into meaningful categories to improve search, filtering, recommendation, and user experience.
Hallucination and reliability analysis
Assess whether model outputs are factually grounded, consistent, and reliable across different prompts, sessions, and use cases.
Ad and caption relevance
Review how well ads, captions, and generated text align with search queries, content context, and user expectations.
Bias and fairness testing
Assess model outputs for stereotypes, imbalance, unfair representation, or bias across demographics, regions, cultures, and languages.
Guardrail robustness testing
Test whether models follow safety rules, refuse harmful requests correctly, and resist jailbreak or prompt injection attempts.
Safety and compliance evaluation
Identify unsafe, harmful, sensitive, or non-compliant outputs based on project policies, safety guidelines, and platform requirements.
Built for real products
Evaluation around the behavior that matters to users.
We turn product goals and risk policies into structured scenarios, scoring rubrics and failure categories.
01LLMs & assistants
02Multimodal AI
03Safety & guardrails
04Search & recommendations
How we deliver
From evaluation rubric to prioritized model findings.
A repeatable workflow turns human judgment into measurable performance signals and clear failure examples.
Define the rubric
Align use cases, policies, scenarios, scoring scales and thresholds.
Execute scenarios
Run representative prompts, tasks and multimodal interactions.
Score & analyze
Review outputs, calibrate raters and classify failures.
Report & retest
Deliver metrics, examples, priorities and targeted follow-up evaluation.
Evaluate how your AI performs for Vietnamese users.
Share your model, use cases and success criteria. We’ll propose a focused evaluation plan.
Discuss a model evaluation pilot ↗