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LLM Evaluation

Multilingual LLM Evaluation & Testing

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.

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Whitepaper

Whitepaper: AI Red Teaming & Safety Testing

A practical whitepaper on multilingual AI red teaming, adversarial safety testing, risk discovery, and remediation planning for enterprise AI systems.

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75+
Languages Evaluated
Native-speaker assessors for each
98.4%
Inter-Annotator Agreement
Across calibrated evaluation tasks
24hr
Turnaround on Pilots
For standard evaluation batches
6
Priority Languages
EN, ZH, HI, JA, KO, AR
Capabilities

What We Deliver

Response Quality Assessment

Structured evaluation of LLM outputs across accuracy, fluency, helpfulness, and instruction-following using calibrated scoring rubrics with inter-annotator agreement tracking.

Safety & Hallucination Detection

Systematic identification of harmful outputs, factual hallucinations, and policy violations across languages. Red-team testing with culturally-aware adversarial prompts.

Cross-Lingual Consistency Testing

Evaluate whether your model delivers equivalent quality across all target languages, identifying language-specific failure modes and performance gaps.

Retrieval & Grounding Evaluation

Assess RAG pipeline outputs for faithfulness to source documents, citation accuracy, and completeness across multilingual knowledge bases.

Domain-Specific Benchmarking

Custom evaluation suites for legal, medical, financial, and technical domains with subject-matter expert assessors who understand terminology and regulatory context.

Evaluation Dashboard & Reporting

Real-time scoring dashboards with drill-down by language, domain, and error type. Exportable reports with actionable recommendations for model improvement.

Our Process

How It Works

01
1

Scope & Rubric Design

Define evaluation dimensions, scoring criteria, and edge cases specific to your model's use case, domain, and target languages.

02
2

Assessor Selection & Calibration

Select native-speaker evaluators with relevant domain expertise. Run calibration rounds to ensure consistent scoring across the team.

03
3

Structured Evaluation

Assessors evaluate model outputs using your custom rubric. Multi-pass review with inter-annotator agreement checks for quality assurance.

04
4

Analysis & Reporting

Aggregate scores, identify systematic failure patterns, and deliver actionable reports with language-by-language breakdowns and improvement recommendations.

05
5

Iterative Improvement

Re-evaluate after model updates to measure improvement. Track quality trends over time with longitudinal dashboards and benchmark comparisons.

Case Study
AI / Technology

Multilingual Safety Evaluation for Global AI Platform

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.

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847 critical issues identified
Key Result
Common Questions

Frequently Asked Questions

What types of LLM outputs can Into23 evaluate?
Into23 evaluates any text-based LLM output including chat responses, summaries, translations, code generation, and RAG retrieval results. Our evaluation framework covers accuracy, fluency, helpfulness, safety, and cultural appropriateness. We assess outputs from all major model families — GPT, Claude, Gemini, Llama, Mistral — as well as custom fine-tuned models. Each evaluation is tailored to your specific use case and quality requirements.
How do you ensure evaluation consistency across languages?
Into23 maintains evaluation consistency through a rigorous calibration process. All assessors complete calibration rounds on pre-scored reference examples before starting live evaluation. We track inter-annotator agreement (currently averaging 98.4%) and flag outlier scores for review. Language-specific rubric adaptations account for linguistic differences while maintaining comparable scoring standards. Regular calibration refreshes prevent drift over time.
What is the minimum project size for LLM evaluation?
Into23 accepts LLM evaluation projects starting from 500 evaluation instances per language. A typical pilot evaluation covers 1,000-2,000 instances across 2-3 languages, which provides statistically meaningful results within 1-2 weeks. Enterprise programs typically run 10,000+ evaluations per month on a continuous basis. We offer flexible engagement models from one-time audits to ongoing evaluation partnerships.
How does Into23 handle safety and red-team testing?
Into23's safety testing combines automated adversarial prompt generation with human-led red teaming by culturally-aware native speakers. Our red team assessors craft prompts designed to elicit harmful, biased, or policy-violating outputs across each target language. We test for toxicity, bias, misinformation, PII leakage, and jailbreak vulnerabilities. Results are categorized by severity and language, with specific remediation recommendations for each finding.
Can Into23 evaluate multilingual RAG systems?
Into23 specializes in evaluating multilingual RAG (Retrieval-Augmented Generation) systems. Our assessors evaluate retrieval relevance, answer faithfulness to source documents, citation accuracy, and completeness across all target languages. We identify cases where the model hallucinates beyond source material or fails to retrieve relevant information in specific languages. This is particularly valuable for enterprises deploying knowledge bases across APAC markets.
What deliverables do clients receive from an evaluation project?
Each evaluation project delivers a comprehensive report including aggregate quality scores by language and dimension, error taxonomy with frequency analysis, specific examples of failure modes with annotations, and actionable improvement recommendations. Clients also receive access to a real-time dashboard for ongoing programs. Raw evaluation data is available in structured formats (JSON, CSV) for integration with your model development pipeline.

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