Relari offers a platform that lets teams create AI agents from natural‑language specifications without writing code. Its Nuvi builder generates reliable, testable agents that can be integrated into Software 3.0 workflows. The service includes tools such as Agent Contracts and Continuous Eval to ensure purposeful and maintainable AI behavior.
Funding
$500K 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
Developing and refining generative AI applications often involves guesswork when selecting parameters, evaluating performance, and optimizing prompts. Traditional methods lack systematic, data-driven approaches for assessing application behavior across various scenarios. This can lead to slower iteration cycles and uncertainty in transitioning prototypes to production-ready systems.
Solution
Relari offers a data-driven toolkit designed to help AI teams evaluate and enhance generative AI applications. The platform enables the creation of custom synthetic datasets and tailored evaluation metrics, providing a structured approach to AI development. By defining expected system behavior and measuring performance against custom metrics, Relari allows teams to understand application performance in different scenarios. The platform facilitates iterative improvement through automated evaluation and performance optimization, enabling faster and more reliable transitions from prototype to production.
Target Audience
Relari is designed for AI engineers, machine learning teams, and product developers working on generative AI applications, including those focused on RAG, enterprise search, and coding agents.
Features
- Custom synthetic dataset generation for stress-testing and evaluating LLM applications
- Tailored evaluation metrics to measure application performance against specific standards
- Automated prompt optimization to improve application responses
- Performance monitoring to track application behavior in real-time
- Integration with RAG systems for end-to-end optimization
- Support for Python and TypeScript