What is Predictionguard?

Predictionguard is a private AI platform designed to facilitate the secure development and deployment of artificial intelligence workflows. It emphasizes data privacy and risk management, ensuring that organizations can adopt AI technologies without compromising sensitive information. The platform provides tools for building AI models with a focus on security, making it particularly suitable for industries where data protection is paramount, such as healthcare and finance.

At its core, Predictionguard enables users to create AI applications that are not only powerful but also safe from vulnerabilities and data leaks. This is achieved through a combination of self-hosted models, security checks, and privacy filters, all of which are designed to protect user data and maintain compliance with industry regulations.

Features

Predictionguard offers a range of features that make it a robust solution for organizations looking to integrate AI into their operations. Some of the key features include:

1. Private and Safeguarded AI Functionality

• The platform ensures that AI models are developed and deployed in a secure environment, protecting sensitive data from unauthorized access and exploitation.

2. Self-Hosted Models

• Users can run popular AI model families such as Llama 3.1, Mistral, Neural Chat, and deepseek within their own infrastructure. This allows for greater control over data and model performance.

3. Security Checks

• Predictionguard includes mechanisms to protect against emerging vulnerabilities, including prompt injections and model supply chain vulnerabilities. This proactive approach to security helps organizations mitigate risks associated with AI deployment.

4. Essential Integrations

• The platform supports integration with leading AI tooling, such as LangChain and LlamaIndex, allowing developers to leverage the best tools available while keeping their data secure within their network.

5. Privacy Filters and Output Validations

• To prevent issues such as hallucinations (inaccurate outputs), toxic responses, and the leakage of personally identifiable information (PII), Predictionguard employs advanced privacy filters and validation mechanisms.

6. Deployment Options

• Predictionguard offers flexible deployment options to suit different organizational needs:
  • Managed Cloud: Fully hosted and managed by Predictionguard, this option allows for quick setup (under one day) and is HIPAA compliant.
  • Self-Hosted: Organizations can host models within their own infrastructure, optimizing for performance and cost-effectiveness.
  • Single-Tenant: Dedicated deployments for individual customers, providing secure and isolated environments without the burden of managing infrastructure.

7. Fast and Easy Onboarding

• The platform is designed for rapid deployment, enabling organizations to get started with AI transformation quickly and efficiently.

Use Cases

Predictionguard is versatile and can be applied across various industries and use cases. Some notable applications include:

1. Healthcare

• Secure Medic Copilot: In prehospital care, AI can assist field medics by providing reliable guidance. Predictionguard enables the development of a secure medic copilot that leverages validated LLM outputs, ensuring that medics can trust the AI's recommendations without hesitation.

2. Finance

• Fraud Detection: Financial institutions can utilize Predictionguard to develop AI models that analyze transaction patterns and detect fraudulent activities while ensuring that customer data remains confidential and secure.

3. Customer Support

• AI Chatbots: Businesses can create AI-driven chatbots that provide customer support, using Predictionguard to ensure that sensitive customer information is protected and that the bot's outputs are accurate and appropriate.

4. Data Analysis

• Predictive Analytics: Organizations can leverage AI for predictive analytics to forecast trends and make data-driven decisions, all while maintaining strict data privacy standards.

5. Research and Development

• AI-Assisted Research: Researchers can use Predictionguard to build AI models that assist in data analysis and hypothesis testing, ensuring that proprietary research data is kept secure throughout the process.

Pricing

While specific pricing details for Predictionguard are not disclosed, the platform offers various deployment options that may influence cost. Organizations interested in utilizing Predictionguard are encouraged to reach out for a demo and discuss their specific needs, which will help tailor a pricing plan that suits their requirements. Factors that may affect pricing include:

• Deployment Type: Managed Cloud, Self-Hosted, or Single-Tenant options may have different pricing structures.
• Model Usage: The type and number of models deployed can impact overall costs.
• Integration Needs: Additional costs may arise based on the integrations and tools required for specific use cases.

Comparison with Other Tools

When comparing Predictionguard to other AI platforms, several unique selling points stand out:

1. Focus on Security and Privacy

• Unlike many AI platforms that prioritize performance over security, Predictionguard is built from the ground up with a strong emphasis on safeguarding data and ensuring compliance with regulations like HIPAA.

2. Self-Hosting Capabilities

• Many AI platforms do not offer self-hosting options, which can be crucial for organizations that require complete control over their data. Predictionguard allows users to host models on their infrastructure, providing flexibility and security.

3. Comprehensive Security Features

• The inclusion of security checks to protect against vulnerabilities such as prompt injections sets Predictionguard apart from competitors that may not offer such robust security mechanisms.

4. Rapid Deployment

• With a focus on quick onboarding, Predictionguard enables organizations to start using AI solutions in less than a day, which is a significant advantage for businesses looking to innovate rapidly.

5. Integration with Leading Tools

• Predictionguard's compatibility with essential AI tooling ensures that developers can leverage the best resources available without compromising data security.

FAQ

1. What types of organizations can benefit from Predictionguard?

• Predictionguard is suitable for a wide range of organizations, particularly those in industries with strict data privacy requirements, such as healthcare, finance, and government.

2. Is Predictionguard compliant with data protection regulations?

• Yes, Predictionguard is designed to be HIPAA compliant and adheres to best practices for data protection, ensuring that sensitive information is safeguarded.

3. Can I integrate Predictionguard with my existing AI tools?

• Absolutely! Predictionguard supports integrations with popular AI tooling, allowing you to build on top of your current infrastructure while maintaining data security.

4. What deployment options does Predictionguard offer?

• Predictionguard provides several deployment options, including Managed Cloud, Self-Hosted, and Single-Tenant deployments, allowing organizations to choose the best fit for their needs.

5. How quickly can I get started with Predictionguard?

• Organizations can typically get started with Predictionguard in less than one day, thanks to its streamlined onboarding process.

6. What types of AI models can I use with Predictionguard?

• Predictionguard supports a variety of popular AI model families, including Llama 3.1, Mistral, Neural Chat, and deepseek, among others.

7. How does Predictionguard protect against AI vulnerabilities?

• The platform includes security checks designed to identify and mitigate risks associated with prompt injections and model supply chain vulnerabilities, ensuring a secure AI deployment.

By focusing on security, privacy, and rapid deployment, Predictionguard stands out as a leading choice for organizations looking to leverage AI technologies while maintaining strict data protection standards.