Rhesis AI provides a validation platform for Generative AI applications, utilizing automated test case generation and industry-specific validation sets to ensure compliance with standards like NIST and OWASP. The solution addresses the challenge of insufficient test coverage and evolving regulatory requirements, enabling businesses to deploy reliable and secure AI systems globally.
Funding
Funding not disclosed
Founders
Product
Problem
Businesses face challenges in ensuring the reliability, security, and compliance of Generative AI (GenAI) applications due to insufficient test coverage, overly general test scenarios, and the need for continuous updates to address evolving adversarial threats and regulatory requirements. Building and maintaining robust test frameworks from scratch can be time-consuming and resource-intensive.
Solution
Rhesis AI offers a validation platform for GenAI applications, providing automated test case generation and industry-specific validation sets to address these challenges. The platform supports compliance with standards like NIST and OWASP, enabling businesses to deploy reliable and secure AI systems globally. It offers in-depth validation sets, domain-specific testing, and adaptive test generation, ensuring thorough and consistent validation across multiple dimensions. The platform also includes tools like the uncensored QA LLM for adversarial test case generation and LLM-Judge for ethical evaluations, providing insights into weaknesses and ensuring AI trustworthiness.
Target Audience
The primary users are AI engineers, heads of AI teams, AI product leads, AI security architects, senior AI engineers, data scientists, automation engineers, product managers, chief technology officers, and AI solution architects involved in developing, owning, or auditing GenAI applications.
Features
- Automated test case generation to identify blind spots and ensure comprehensive validation.
- Global testing capabilities with built-in support for multilingual and multi-country testing.
- Flexible, framework-agnostic approach that integrates into existing workflows.
- In-depth validation sets directory tailored to specific industries, use cases, and compliance requirements.
- Adaptive test generation using custom documents and guidelines to adapt to application growth and emerging threats.
- Uncensored QA LLM for generating adversarial test cases.
- LLM-Judge for ethical, unbiased evaluations.
- Transparent test reports providing insights into GenAI performance and areas for improvement.