Patronus AI

Evaluates LLM outputs for factual hallucinations using Patronus's proprietary 70B Lynx model, which claims to outperform GPT-4 on hallucination detection benchmarks. The model analyzes generated text against source documents or ground truth to assign hallucination probability scores, enabling automated quality gates in production pipelines. Scoring is delivered via REST API with configurable thresholds and explanation generation for failed evaluations.

Evaluates LLM outputs for toxic language, harmful content, and policy violations using Patronus's safety evaluation models. Integrates with the platform's experiment tracking to flag unsafe responses during development and production monitoring phases. Provides categorical scoring (toxicity level, harm type) and can be configured as a hard gate or soft warning in evaluation pipelines.

Evaluates LLM performance on tip-of-the-tongue (ToT) tasks using Patronus's BLUR model, which assesses the ability to retrieve or infer information when given partial clues or descriptions. BLUR evaluates whether LLMs can correctly identify entities, concepts, or information from vague or incomplete descriptions, measuring retrieval accuracy and reasoning under uncertainty.

Manages evaluation datasets with versioning, allowing teams to track changes to test sets and maintain reproducibility across evaluation runs. Datasets can be uploaded, versioned, and reused across multiple experiments. The platform provides unlimited dataset storage in paid tiers and enables sharing datasets across team members for collaborative evaluation.

Enables chaining multiple evaluators (hallucination, toxicity, PII, brand safety, reasoning quality) in a single evaluation run, with results aggregated and correlated in the experiment dashboard. Evaluators run in parallel or sequence based on configuration, and results are combined to provide holistic quality assessment. Supports custom aggregation logic and filtering based on multiple evaluation criteria.

Provides web-based dashboards for visualizing evaluation metrics, trends, and performance across experiments. Dashboards display hallucination rates, toxicity scores, PII detection results, and other metrics over time. Supports custom report generation for compliance and stakeholder communication. Analytics are available in Base tier and above, with unlimited comparisons across all tiers.

Scans LLM outputs for personally identifiable information (PII) including names, email addresses, phone numbers, SSNs, credit card numbers, and other sensitive data. Uses pattern matching and NER-based detection to identify PII in generated text and flag responses that violate data privacy policies. Integrates with Patronus evaluation experiments to prevent PII leakage in production systems.

Evaluates LLM outputs against brand guidelines and organizational policies to detect off-brand messaging, policy violations, or inappropriate tone. Uses configurable rule sets and semantic matching to identify responses that deviate from brand voice, violate content policies, or contradict organizational guidelines. Results are tracked in the Patronus platform for continuous compliance monitoring.

Generates adversarial test cases and attack prompts to probe LLM vulnerabilities, including jailbreak attempts, prompt injection, and edge case scenarios. The platform uses synthetic test generation to create diverse adversarial inputs and evaluates LLM responses against safety and quality criteria. Results are tracked in experiments for regression testing and continuous security monitoring.

Provides a structured experiment management system for tracking LLM evaluation runs, comparing results across model versions, and analyzing performance trends. Experiments capture prompts, model outputs, evaluation scores, and metadata in a queryable database. The platform enables side-by-side comparison of evaluation results and historical trend analysis to detect regressions or improvements.

Monitors production LLM deployments by continuously evaluating outputs against safety and quality criteria. Integrates with production systems to sample or stream LLM responses for real-time evaluation, tracking metrics over time and alerting on anomalies or threshold violations. Provides dashboards for monitoring hallucination rates, toxicity, PII leakage, and brand safety in live systems.

Enables systematic regression testing of LLM updates by comparing evaluation results against baseline metrics. Automatically runs evaluation suites on new model versions and flags regressions in hallucination, toxicity, PII, or brand safety scores. Integrates with CI/CD pipelines to gate model deployments based on regression thresholds.

Provides synthetic simulation environments for training and evaluating AI agents on realistic task workflows across multiple domains. Environments include research science (literature synthesis), software development (multi-tool workflows), customer service (support cases), product applications (UI navigation), and finance (M&A, trading). Agents interact with simulated tools and data to complete tasks, with evaluation metrics tracking task completion, reasoning quality, and safety.

Evaluates the quality and correctness of LLM reasoning chains using Patronus's GLIDER model, which assesses intermediate reasoning steps and logical flow. Analyzes chain-of-thought outputs to identify reasoning errors, logical inconsistencies, or unsupported conclusions. Provides scores for reasoning quality and can identify where reasoning chains break down or diverge from correct logic.