Robust Intelligence

Robust Intelligence is an enterprise-grade platform dedicated to securing AI systems end-to-end. It delivers real-time protection and continuous validation for AI models and their data pipelines, addressing the unique vulnerabilities that arise in machine learning and generative AI deployments. The core value proposition is enabling organizations to deploy AI with confidence by proactively identifying and mitigating security risks, compliance gaps, and performance issues before they impact production.

Key features: The platform offers a comprehensive suite of capabilities including an AI Firewall that monitors and blocks malicious prompts and data inputs in real-time for LLM applications. It performs automated model auditing to detect data drift, concept drift, and model degradation. For security, it tests against adversarial attacks, data poisoning, and model theft attempts. It also includes bias and fairness testing to ensure models operate ethically and comply with regulations like the EU AI Act. Continuous validation runs checks on model inputs, outputs, and internal behavior across the entire ML lifecycle.

What sets Robust Intelligence apart is its holistic, platform-based approach that integrates security directly into the MLOps workflow, rather than offering point solutions. It uses a technique called "fuzzing" to automatically generate test cases that uncover hidden model flaws. The platform is model-agnostic, supporting a wide range of frameworks from traditional scikit-learn models to large language models like GPT-4 and Claude. It offers deep integrations with popular cloud platforms (AWS, GCP, Azure), ML tools (MLflow, Kubeflow), and CI/CD pipelines for seamless adoption.

Ideal for large enterprises and regulated industries that rely heavily on AI for critical functions. Primary use cases include financial services institutions using AI for fraud detection and credit scoring, who need to ensure model fairness and regulatory compliance. Healthcare organizations deploying diagnostic models must validate accuracy and safety. Technology companies building customer-facing LLM applications require guardrails against prompt injection and data leakage. It is also crucial for any organization undergoing AI audits or needing to enforce internal AI governance policies.

Pricing is typically enterprise-oriented with custom quotes, but a limited free tier is available for evaluation. The platform operates on a subscription model, with costs scaling based on the number of models monitored, inference volume, and required feature modules such as advanced adversarial testing or compliance reporting.