Relari

Relari.ai is a comprehensive platform that enhances the reliability and performance of generative AI systems through synthetic data generation and a robust evaluation framework. It offers a suite of tools and services for developers, researchers, and AI teams to systematically test, fine-tune, and monitor their AI models.

Key features:

- Synthetic Data Generation: Produces large-scale synthetic datasets tailored to specific applications, enabling stress testing of models and applications at a reduced cost.
- Custom Dataset Creation: Allows users to generate datasets that closely mimic real-world data to improve model robustness and performance.
- Modular Evaluation: Enables programmatic definition of evaluation pipelines and metric selection for each module, covering various AI tasks such as text generation, code generation, retrieval, and classification.
- Open Source Metric Library: Provides access to over 30 open-source metrics and evaluators for free use.
- Close-to-Human Evaluators: Offers the ability to train custom ensemble evaluators using user feedback data, achieving over 90% alignment with human evaluation.
- Systematic Fine-Tuning: Utilizes synthetic data and comprehensive metrics to control the fine-tuning process of AI models.
- Performance Monitoring: Continuously monitors the performance of each module in the pipeline to identify problem root causes.
- CI/CD Integration: Integrates with continuous integration and continuous deployment pipelines for seamless updates and testing.
- Lifecycle Support: Provides support throughout the GenAI application development lifecycle, including prompt iteration, model selection, regression testing, benchmarking, functional testing, and both online and offline evaluation.

How it works:

1. Users define evaluation pipelines and select appropriate metrics for each module.
2. The platform generates large-scale synthetic datasets tailored to specific needs.
3. Users evaluate and fine-tune models using the generated data and metrics.
4. Continuous monitoring of model performance allows for iteration on design and implementation based on detailed metrics and feedback.

Integrations:

CI/CD Tools, AI Development Platforms (such as those used by Nvidia, Bank of America, and Raytheon Technologies)

Use of AI:

Relari.ai leverages generative artificial intelligence to create synthetic datasets and train evaluators. The platform supports various AI tasks, including text generation, code generation, retrieval, and classification.

AI foundation model:

The platform is built on open-source principles, providing a library of metrics and evaluators that can be customized and extended.

Target users:

- Individual Developers and Researchers
- AI Teams and Enterprises

How to access:

Relari.ai is available as a web app and can be integrated into development pipelines via API and SDK. A free community version is available for individual developers and researchers, while custom enterprise solutions are offered for AI teams and enterprises.