Distributional

Overview

Distributional is an AI reliability platform designed to help teams build, test, and maintain reliable AI applications. It provides tools and frameworks for evaluating the performance, safety, robustness, and consistency of AI models, with a particular focus on large language models and multimodal models.

The platform enables users to define structured test cases, run evaluations against different model versions or configurations, and track key metrics over time. This helps identify regressions, measure improvements, and ensure models meet specific criteria before deployment. Distributional aims to integrate into the AI development lifecycle, providing continuous testing and monitoring capabilities to catch issues early and improve the overall quality and trustworthiness of AI systems.

Key Features

• Structured AI test case creation and management
• Performance evaluation metrics for LLMs and AI models
• Safety and bias testing capabilities
• Robustness and consistency checks
• Comparison across different model versions and providers
• Continuous monitoring of AI system behavior
• Integration into development and CI/CD workflows
• Evaluation reporting and visualization

Supported Platforms

• Web Browser
• API Access

Integrations

• CI/CD pipelines
• Various AI Model APIs