Key features
- Hallucination detection: APAC faithfulness scoring vs retrieved context documents
- Red-teaming: APAC automated adversarial test case generation for safety evaluation
- Custom evaluators: APAC domain-specific quality and compliance scoring functions
- Batch evaluation: APAC test suites run against prompt version changes before deployment
- Regulatory compliance: APAC financial/healthcare/legal LLM output quality gates
- LLM-as-judge: APAC scalable evaluation using LLMs to score LLM outputs
Best for
- APAC regulated industry AI teams that need systematic safety evaluation of LLM outputs before production deployment — particularly APAC financial services, healthcare, and government organizations where hallucination and safety failures have direct legal and compliance consequences.
Limitations to know
- ! LLM-as-judge evaluators have their own error rates — not 100% accurate APAC quality gates
- ! APAC custom evaluator design requires AI safety expertise to avoid false positives
- ! Usage-based cost accumulates with comprehensive APAC evaluation coverage
About Patronus AI
Patronus AI is an LLM evaluation and safety testing platform providing APAC AI teams with automated red-teaming, hallucination detection, and regulatory compliance evaluation — covering the quality and safety assurance gap between prompt testing (did the LLM follow instructions?) and production monitoring (is the LLM safe and accurate at scale?). APAC financial services, healthcare, and government AI deployments use Patronus AI to evaluate LLM safety before releasing to end users.
Patronus AI's hallucination detection evaluators score LLM outputs for factual accuracy against provided context documents — for APAC RAG applications where the LLM should only reference retrieved documents, Patronus AI's faithfulness evaluator identifies claims in the output that are not supported by the retrieved context. APAC regulated APAC industries where factual accuracy failures create legal liability use hallucination detection as a pre-deployment quality gate.
Patronus AI's red-teaming module generates adversarial test cases for APAC LLM applications — probing for jailbreak vulnerabilities, harmful content generation, bias amplification, and regulatory non-compliance. APAC AI safety teams run red-team evaluations before each major model or prompt update, generating hundreds of adversarial scenarios and scoring the LLM's response safety systematically rather than relying on manual review.
Patronus AI's custom evaluators allow APAC teams to define domain-specific quality criteria — APAC financial services teams create evaluators that check whether LLM responses correctly disclaim investment advice, APAC healthcare teams create evaluators ensuring medical information includes appropriate caveats, and APAC legal teams evaluate whether AI assistant responses recommend professional legal consultation. These custom evaluators automate regulatory compliance checking at scale.
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