This startup offers an AI firewall API that allows developers to monitor AI outputs in real-time and flag unwanted responses based on customizable policies. The platform provides performance analytics to help users efficiently validate and improve their AI applications.
Funding
Funding not disclosed
Founders
Product
Problem
AI applications can generate inappropriate, unethical, or illegal content, creating risks for developers and end-users. Current methods for monitoring AI outputs often lack real-time capabilities and customizable policies, making it difficult to ensure AI safety and compliance.
Solution
Overseer AI provides an AI firewall API that enables developers to monitor AI-generated content in real-time and flag unwanted responses based on customizable policies. The platform offers a secure validation process for LLM outputs, allowing developers to focus on building powerful AI products without worrying about potential risks. By integrating the Overseer AI SDK, applications can validate AI responses and filter out unsafe content, ensuring compliance and protecting users from harmful outputs. The service also offers a safety analytics suite, providing timestamps of failures for secure investigation without exposing sensitive data.
Target Audience
The primary target audience includes AI developers and companies building LLM applications who need to ensure the safety and compliance of their AI systems.
Features
- Real-time monitoring of AI outputs using the Validate() method
- Customizable policies to flag content based on specific safety and compliance requirements
- Secure validation of LLM outputs to prevent the generation of illegal, unethical, or unwanted content
- Comprehensive safety analytics suite with timestamps of failures for secure investigation
- TypeScript and Node.js client libraries for easy integration into existing codebases
- Pre-trained brand safety classification models, including vision-1 and vision-1-mini, optimized for high accuracy and efficient memory usage
- BrandSafe-16k dataset for training brand safety classification models, featuring 16 distinct risk categories