Product overview
What is Guardrails AI?
Guardrails AI is a reliability platform for building, governing, and scaling production generative AI across models and deployment environments. It covers three stages of the AI lifecycle: creating realistic synthetic data for training and optimization, generating evaluation datasets that expose risky edge cases, and deploying runtime controls that stop unsafe or noncompliant outputs.
The platform is designed to make failure modes measurable before an application reaches users and enforce policies after deployment. Runtime guardrails inspect model or agent behavior for policy violations, hallucinations, and sensitive-data leakage, then block or handle the problematic output according to the configured rule.
How to Use Guardrails AI
- Define the application's reliability, safety, privacy, and policy requirements.
- Generate synthetic users, scenarios, or examples when real training data is insufficient.
- Build dynamic evaluation datasets focused on edge cases and risky outcomes.
- Run the model or agent against those evaluations and quantify failure modes.
- Configure runtime guardrails for prohibited content, hallucination, and data leakage.
- Deploy controls with the production application and monitor violations over time.
Core Features
- Synthetic data: Produces realistic datasets for fine-tuning, distillation, and prompt optimization.
- Dynamic evaluations: Targets edge cases and risky behaviors instead of relying only on static tests.
- Failure measurement: Quantifies where an agent breaks before customers encounter the issue.
- Runtime guardrails: Inspects and blocks outputs that violate configured policies.
- Hallucination controls: Detects reliability issues before responses reach users.
- Data-leakage prevention: Identifies outputs that expose protected or sensitive information.
- Model and deployment flexibility: Applies reliability workflows across language models and environments.
Use Cases
- Agent red teaming: Generate adversarial or unusual user scenarios to find weak behavior.
- Pre-release evaluation: Measure safety and quality regressions before deployment.
- Policy enforcement: Block outputs that conflict with organizational or regulatory rules.
- Privacy protection: Detect sensitive information in generated responses.
- Training-data expansion: Create diverse examples for fine-tuning or distillation.
- Prompt optimization: Compare prompts against repeatable evaluation datasets.
Pricing
The homepage offers a try-now path and sales contact but does not display standard plan prices in the reviewed content. Current commercial and usage terms need to be confirmed with Guardrails AI.
Frequently Asked Questions
Does Guardrails AI only run after deployment?
No. It also generates synthetic data and evaluation datasets for training and pre-release testing.
What can runtime guardrails detect?
The homepage names policy violations, hallucinations, and data leakage.
Can it work with different models?
Yes. The platform is described as working across any language model and deployment environment.
