About
Prediction Guard is a secure and scalable AI platform designed to enhance data privacy and mitigate risks during AI adoption. It enables users to develop AI workflows while ensuring system-level security, from model server configurations to LLM outputs. The platform can run popular AI model families privately within a user's infrastructure, featuring robust security checks to shield against vulnerabilities. It integrates seamlessly with leading AI tools while enforcing privacy filters to prevent issues like hallucinations or data leaks. Users can choose deployment options such as a managed cloud, self-hosting, or single-tenant setups, each offering unique benefits tailored to enterprise needs.
Competitive Advantage
Emphasizes data security and privacy while providing flexible deployment options and robust integration capabilities.
Use Cases
Pros
- High data security focus
- Flexible deployment options
- Integration with popular AI tools
- Prevention of AI malfunctions
Cons
- Requires technical expertise
- Potential high cost for enterprise
- Limited to specific AI models
- Complexity in setup
Tags
Pricing
Who uses Prediction Guard?
Features and Benefits
Private Model Deployment
Allows users to run AI models privately within their infrastructure, enhancing data security.
Robust Security Measures
Guards against vulnerabilities like prompt injections and data leaks, ensuring safe AI operations.
Comprehensive Integrations
Seamlessly integrates with popular AI tools while maintaining data privacy.
Flexible Deployment Options
Offers managed cloud, self-hosted, and single-tenant setups for different enterprise needs.
Output Validation
Includes mechanisms to validate AI outputs to prevent incorrect or toxic information dissemination.
Integrations
Target Audience
Frequently Asked Questions
Managed cloud, self-hosted, and single-tenant.
Yes, it is HIPAA compliant.
Protection against prompt injections and model supply chain vulnerabilities.
LangChain, LlamaIndex, and various code assistants.
Yes, it uses privacy filters and output validation to prevent toxic outputs.
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