Langhui Technology's AI model evaluation and safety data services integrate the latest 2026 safety research methods to deliver an end-to-end solution spanning hallucination benchmarking, compliance audit, bias detection, red teaming, and continuous monitoring — ensuring every AI model passes rigorous safety checks before deployment and during operation, and delivering truly trustworthy AI.
Closely tracking the latest safety research from global frontier labs such as OpenAI, Anthropic, and Google DeepMind, as well as regulators in the EU and China, Langhui's expert team translates continuously evolving academic and regulatory advancements into scalable, production-ready model evaluation and safety data services.
The 2026 upgraded edition of OpenAI's Preparedness Framework. Systematically evaluates the capability thresholds of frontier models across four risk dimensions — cybersecurity, CBRN, persuasion, and autonomous action — establishing an end-to-end risk scoring system that spans R&D through deployment. Langhui builds matching risk evaluation datasets accordingly.
Anthropic's Responsible Scaling Policy (RSP). Defines tiered safety commitments by AI Safety Level (ASL-2/3/4), requiring models to pass rigorous red teaming and alignment verification before being upgraded to higher capability levels. Langhui provides ASL evaluation data production services.
Google DeepMind's Frontier Safety Framework. Defines Critical Capability Levels (CCL) that trigger evaluation and mitigation mechanisms as models approach the threshold of dangerous capabilities. Langhui builds adversarial test sets covering CCL thresholds to support precise identification of model capability boundaries.
The EU AI Act enters full enforcement in August 2026. High-risk AI systems must undergo conformity assessment, establish risk management systems, and ensure data governance and transparency. Langhui provides compliance audit data covering Article 10 (data governance) and Article 15 (accuracy and robustness).
The upgraded Interim Measures for the Management of Generative Artificial Intelligence Services and the GB/T 44688-2026 safety assessment specification. Covers four dimensions — value alignment, content safety, personal information protection, and algorithmic transparency. Langhui builds localized compliance evaluation data covering 32 granular indicators.
The evolution of Red Teaming 2.0. Upgrades from one-off manual attacks to a unified triad of automated red teaming, expert red teaming, and multi-agent red teaming. NIST AI Safety Institute released Red Teaming Test Standard v2.0. Langhui delivers standardized adversarial sample production covering 12 risk categories.
Four core capabilities cover the full model safety evaluation lifecycle — from factual hallucination to regulatory compliance, from algorithmic bias to operational monitoring — building a quantifiable, auditable, and traceable safety data pipeline.
A factual hallucination detection benchmark built on multi-source trusted knowledge bases, spanning encyclopedia, medical, legal, financial, and scientific domains. Through entity-level fact checking and citation tracing, it precisely identifies factual errors in model outputs.
Compliance audit data covering major domestic and international regulations including the EU AI Act, China's Generative AI Management Measures, GDPR, and HIPAA. Decomposes regulatory clauses into quantifiable evaluation indicators, helping model teams pinpoint compliance blind spots.
Bias detection datasets across gender, age, race, region, religion, occupation, and other dimensions. Uses dual Counterfactual and Prompt-Based methods to identify systematic bias in model decisions and outputs fairness diagnostic reports.
Continuous monitoring data services for behavioral drift and safety degradation after model deployment. Builds a closed-loop monitoring system through production log sampling, anomalous output identification, and user feedback routing — triggering evaluation workflows the moment risks are detected, with average response time under 30 minutes.
Six standard safety evaluation datasets, ready out of the box — covering hallucination, compliance, bias, boundary, multilingual, and industry dimensions. Delivered via both subscription and custom models, benchmarked against the top-tier dataset standards of Appen, Scale AI, and Labelbox.
Factuality and faithfulness hallucination samples across encyclopedia, medical, legal, and financial domains, with entity-level citation tracing and error type grading. Compatible with TruthfulQA, HaluEval, and HALLUEVAL evaluation paradigms.
Compliance test samples decomposed by clause from the EU AI Act, Generative AI Management Measures, GDPR, and other regulations — including violation identification, clause mapping, and risk-level annotation. Supports conformity assessment for high-risk systems.
Counterfactual test samples across 24 dimensions including gender, age, race, region, religion, and occupation. Identifies systematic bias on sensitive attributes and outputs fairness diagnostic reports.
An adversarial sample library covering 12 risk categories including jailbreaks, prompt injection, value drift, and harmful outputs — with successful attack cases and defense remediation recommendations. Benchmarked against the NIST AI Red Teaming 2.0 standard.
Safety evaluation samples across 200+ languages including Chinese, English, Japanese, Korean, French, German, Spanish, and Arabic. Cross-cultural risk identification and value alignment, with support for low-resource language safety evaluation.
Compliance evaluation data for healthcare, finance, legal, education, automotive, and government industries. Integrates industry regulatory requirements with best practices to support safety compliance review for vertical-domain models.
From general-purpose foundation models to vertical industries, from model R&D to product launch — Langhui's model evaluation and safety services fit the full spectrum of frontier model safety evaluation needs, building a robust safety perimeter for every category of AI application.
Pre-deployment safety evaluation for GPT-class, Claude-class, and Gemini-class general foundation models — covering hallucination, bias, jailbreaks, and harmful outputs across all dimensions. Ensures models meet the deployment safety baseline, benchmarked against frontier lab release standards.
Compliance audit data for large model applications in heavily regulated industries such as healthcare, finance, legal, and government. Audits each industry regulatory requirement clause by clause, outputting compliance diagnostic reports and remediation recommendations to reduce compliance risk.
End-to-end risk assessment for AI products (intelligent customer service, AI agents, AI search, Copilots). Identifies potential risks in real-world user interaction scenarios and deploys protective strategies in advance.
Continuous safety monitoring services for deployed AI models — real-time detection of behavioral drift, safety degradation, and emerging risks. Builds a closed-loop safety pipeline from alerting to response to iterative optimization.
Langhui has built a quality assurance system that runs through the entire model evaluation workflow — from hallucination detection coverage to compliance audit completeness, from bias identification accuracy to monitoring response speed. Every dimension has quantifiable assessment indicators, benchmarked against internationally authoritative AI safety standards.
Partner with Langhui's safety expert team to build a 2026-grade safety evaluation system for your models. Whether you lead a foundation model team or a vertical application team, we deliver end-to-end safety data solutions — from hallucination detection to compliance audit, from bias identification to continuous monitoring.