Research and Differentiators

We believe that scalable AI oversight is the most critical challenge to the widespread adoption of AI when LLM-powered systems become increasingly agentic and autonomous.

The mission of Research at Patronus is to deepen the understanding of AI systems through applied research and develop practical methods and tools for their evaluation and optimization. The Patronus team has conducted industry-leading research in the field of evaluation, alignment, and AI Safety. Our research broadly covers several categories:

• Benchmark and datasets
  • Benchmark development for real-world applications
  • High-quality automated dataset generation for evaluation
• Alignment and AI safety
  • Guardrail and LLM-as-judge models
  • Red-teaming and adversarial attacks
• AI system evaluation
  • Evaluation suite that can work with any agentic system
  • Multiple-hop reasoning and long context beyond the token limit of LLMs
  • Human feedback and memory components to continously improve evaluation
• Application-based evaluation
  • Conversational AI, RAG, Deep Research and more

Apply evaluation research to your AI Systems

Automated, scalable evaluation of LLMs and agentic systems is an open field of research. When releasing new open and closed source models or an agent system, it is critical to understand the capabilities and gaps in performance to optimize and safeguard your system. The evaluation research at Patronus can provide:

• State-of-the-Art automated evaluators that outperform industry alternatives in each category (safety, capabilities, alignment)
• High-quality datasets off-the-shelf and in enterprise offerings
• Adversarial testing that achieves a high attack success rate for real-world use cases
• Recommendations for best practices with evaluator selection, dataset curation, and evaluation framework