Guardrails AI
Executive Summary
The startup provides an open-source platform that implements AI guardrails to mitigate risks associated with generative AI outputs, ensuring compliance with industry standards and preventing sensitive data leaks. By offering real-time hallucination detection and customizable validation tools, it enhances the reliability and safety of AI applications across enterprise infrastructures.
Funding
Estimated Funding
$7.5M+
Major Investors
Zetta Venture Partners
Team (10+)
Diego Oppenheimer
Founder/CEO
Safeer Mohiuddin
Co-Founder/Investor
Company Description
Problem
Generative AI models can produce outputs that are toxic, leak sensitive data, or contain hallucinations, creating risks for enterprises deploying these models in production. Existing methods for mitigating these risks often lack real-time detection capabilities and are difficult to customize for specific use cases.
Solution
The startup offers an open-source platform that implements AI-powered guardrails to mitigate risks associated with generative AI outputs. The platform provides real-time hallucination detection, customizable validation tools, and sensitive data leak prevention, enhancing the reliability and safety of AI applications. It allows developers and AI platform engineers to deploy production-grade guardrails across their enterprise AI infrastructure, ensuring industry-leading accuracy with minimal latency impact. The platform serves as a drop-in replacement for any large language model (LLM), safeguarding against unsafe or unethical AI outputs through an extensive library of orchestrated, rigorously tested guardrails.
Features
Real-time hallucination detection to prevent inaccurate or misleading outputs.
Customizable validation tools to ensure compliance with industry standards and specific use cases.
Sensitive data leak prevention to protect against the exposure of personally identifiable information (PII).
Extensive library of pre-built guardrails for various risk categories, including toxicity, financial advice, and competitor mentions.
Support for deploying guardrails within a virtual private cloud (VPC) for enhanced security.
Observability and customization features for monitoring and tailoring guardrail performance.
Drop-in replacement compatibility with various LLMs.
AI-powered validation for ensuring neutral or positive tone in generated text.
Target Audience
The primary users are developers, machine learning engineers, and AI platform engineers across leading enterprises who are building and deploying generative AI applications.