Citrusx provides an end-to-end platform for validating and monitoring AI models, ensuring accuracy, robustness, and compliance with regulatory standards. The platform identifies anomalies and vulnerabilities while offering real-time explanations of model predictions, enabling organizations to maintain trust in their AI systems.
Funding
$4.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Organizations deploying AI models face challenges in ensuring their accuracy, robustness, and adherence to regulatory standards. Identifying anomalies, vulnerabilities, and biases in these models, along with providing real-time explanations for predictions, can be complex and time-consuming. This complexity hinders the ability to maintain trust and transparency in AI systems.
Solution
Citrusx offers an end-to-end AI validation and risk management platform that enables organizations to validate, explain, and monitor AI models at scale. The platform identifies anomalies, vulnerabilities, and biases, providing real-time explanations of model predictions to ensure transparency. It supports compliance with various regulations by offering tools that provide essential capabilities for governance. The platform's capabilities extend to monitoring models in production to detect drifts and anomalies, preventing deterioration in model performance.
Target Audience
Citrusx is designed for data scientists, data science managers, chief data officers, risk officers, model risk managers, executives, and regulators involved in developing, deploying, and overseeing AI models.
Features
- End-to-end platform for AI model validation and monitoring, covering development, deployment, and ongoing assessment
- Anomaly detection to identify vulnerabilities and areas where models are easily manipulated
- Explainability tools providing insights into model decisions at the global, prediction, cluster, and group levels
- Comprehensive validation metrics to ensure model stability and accuracy
- Continuous monitoring to detect data drift, concept drift, and performance degradation in production
- Real-time reporting with customized reports for different stakeholders and scenarios
- Mitigation suggestions and alerts to reduce risks and vulnerabilities
- Support for various AI use cases, including predictive ML models and Generative AI (GenAI) systems
- Role-specific dashboards and monitoring views for data science, risk, compliance, and business teams
- Immutable audit trails with searchable views and export features for compliance