Rhesis AI

About Rhesis AI

Rhesis AI key features

• Automated testing for LLM applications to ensure robustness, reliability, and compliance.
• Comprehensive test benches and customizable options for adversarial and industry-specific assessments.
• Continuous benchmarking engine for scheduled or ongoing quality assurance.
• Deep insights and recommendations based on evaluation results and error classification.
• Seamless integration into existing environments without the need for code changes.

Rhesis AI use cases

• Assessing the robustness of LLM applications to identify and mitigate potential adverse behaviors.
• Monitoring the consistency of LLM application behavior to ensure adherence to predefined standards and regulations.
• Evaluating compliance of LLM applications with various legal and industry regulations to reduce non-compliance risks.
• Providing automated benchmarking and quality assurance for LLM applications to enhance reliability and performance.
• Offering insights and recommendations based on evaluation results to improve the overall trustworthiness of LLM applications.

Useful for

• Enhances user experience by providing necessary functionalities for secure log-in and consent management.
• Facilitates compliance with privacy regulations by allowing users to customize their cookie preferences.
• Improves website performance through the use of performance cookies that analyze key performance metrics.
• Supports targeted advertising by utilizing advertisement cookies to deliver personalized content based on user behavior.
• Provides insights into user interactions and website traffic through analytical cookies, aiding in data-driven decision-making.

Price

• Rhesis AI offers an all-in-one AI testing platform with no code changes required for integration.
• The platform includes access to adversarial, industry-specific, and compliance test benches that can be customized.
• Automated benchmarking can be scheduled or continuous to identify gaps and unwanted behavior.
• The service provides deep insights and recommendations based on evaluation results and error classification.
• Pricing details for specific plans are not provided in the text.