Relari AI

What is Relari AI?

Relari AI is an advanced platform designed to enhance the reliability and robustness of Generative AI systems by enabling developers to define natural language contracts that specify expected agent behavior, generate extensive synthetic test datasets, and conduct comprehensive modular evaluations. It supports continuous evaluation and monitoring to identify and fix issues early in the AI development lifecycle, facilitating faster iteration and higher confidence in deploying AI agents in mission-critical environments such as finance, enterprise search, and compliance.

Key Features

• Contract-Based Development

  Use natural language contracts to collaboratively define and verify AI agent behavior across diverse scenarios, ensuring clarity and alignment on expected outcomes.

• Synthetic Data Generation

  Create large-scale, tailored synthetic datasets to simulate user behavior and stress test AI agents, covering corner cases often missing in real-world data.

• Modular Evaluation Framework

  Leverage an open-source framework with 30+ metrics for evaluating text generation, code generation, retrieval, classification, and agent performance.

• Comprehensive Trace Analysis

  Gain immediate insights into AI agent task execution through detailed trace analysis, enabling rapid identification and resolution of issues.

• Continuous Monitoring and Feedback Loop

  Integrate user feedback and production data to train custom evaluators aligned with human judgment, supporting ongoing improvement of AI systems.

Use Cases

• AI Agent Testing and Validation : Systematically test and certify AI agents’ behavior before deployment to ensure reliability in complex, real-world applications.
• Synthetic Dataset Creation : Generate diverse synthetic datasets to expand test coverage and simulate various user intents and interaction patterns.
• Root Cause Analysis : Pinpoint performance issues and parameter trade-offs in AI pipelines using modular evaluation and detailed metrics.
• Accelerated AI Development : Speed up iteration cycles by using synthetic data and automated evaluation to validate improvements rapidly.
• Stress Testing Generative AI Systems : Evaluate AI models under extreme and edge-case scenarios to ensure robustness before production release.

FAQs