Systematic assessment of large language model outputs for accuracy, fluency, safety, and cultural appropriateness across 75+ languages.
AI companies building multilingual models need rigorous, human-led evaluation to identify failure modes before deployment. Into23's evaluation practice combines native-speaker assessors with structured scoring rubrics and our proprietary evaluation framework to deliver actionable quality insights across your target languages. We evaluate outputs from GPT, Claude, Gemini, Llama, Mistral, and custom models.
Structured evaluation of LLM outputs across accuracy, fluency, helpfulness, and instruction-following using calibrated scoring rubrics with inter-annotator agreement tracking.
Systematic identification of harmful outputs, factual hallucinations, and policy violations across languages. Red-team testing with culturally-aware adversarial prompts.
Evaluate whether your model delivers equivalent quality across all target languages, identifying language-specific failure modes and performance gaps.
Assess RAG pipeline outputs for faithfulness to source documents, citation accuracy, and completeness across multilingual knowledge bases.
Custom evaluation suites for legal, medical, financial, and technical domains with subject-matter expert assessors who understand terminology and regulatory context.
Real-time scoring dashboards with drill-down by language, domain, and error type. Exportable reports with actionable recommendations for model improvement.
Define evaluation dimensions, scoring criteria, and edge cases specific to your model's use case, domain, and target languages.
Select native-speaker evaluators with relevant domain expertise. Run calibration rounds to ensure consistent scoring across the team.
Assessors evaluate model outputs using your custom rubric. Multi-pass review with inter-annotator agreement checks for quality assurance.
Aggregate scores, identify systematic failure patterns, and deliver actionable reports with language-by-language breakdowns and improvement recommendations.
Re-evaluate after model updates to measure improvement. Track quality trends over time with longitudinal dashboards and benchmark comparisons.
Conducted comprehensive safety and quality evaluation across 6 languages for a major AI platform's chat model before APAC market launch. Native-speaker red team assessors identified 847 critical safety issues and 2,300+ quality gaps that were addressed before deployment.
We evaluate chat responses, instruction-following outputs, RAG pipeline results, code generation, summarization, translation quality, and any other LLM output type. We support GPT, Claude, Gemini, Llama, Mistral, and custom models.
We use calibration rounds before production evaluation begins, track inter-annotator agreement throughout, and apply consistent rubrics adapted for each language. Our 98.4% agreement rate reflects this process.
We can run pilot evaluations within 24 hours for standard batches. Minimum project sizes vary by evaluation type, so contact us for a scoping conversation.
Our safety evaluation combines systematic prompt testing with culturally-aware adversarial prompts designed by native-speaking red team assessors. We identify harmful outputs, policy violations, and language-specific safety gaps.
Yes. We assess RAG pipeline outputs for faithfulness to source documents, citation accuracy, and completeness across multilingual knowledge bases, identifying where retrieval or grounding fails in specific languages.
Clients receive scoring dashboards with drill-down by language and error type, detailed reports with failure pattern analysis, language-by-language breakdowns, and actionable improvement recommendations.
Get a custom quote for your LLM evaluation project. Our team typically responds within 24 hours.