RagaAI Testing Platform

This page provides a high level introduction to the RagaAI Testing Platform.

Welcome to RagaAI, a comprehensive AI testing platform that automatically detects issues, identifies root cause and empowers users to fix them effectively. This leads to a 3x acceleration in AI development lifecycle while reducing AI risk exposure by 90% in production.

RagaAI is a multi-modal platform that supports testing for various AI applications, including

  • Computer Vision,

  • Generative AI Large Language Models (LLM) / NLP

  • Structured Data Applications and others

RagaAI Testing Platform - Public Sandbox

The RagaAI Sandbox is publicly available here. The Quickstart and the Sandbox Guide are also available to help you navigate the platform and see its capabilities firsthand.

Why RagaAI?

Ensuring AI quality is always a problem for data science teams around the world, weather it is dealing with experiments stuck in the lab or biased / non-robust AI in production.

RagaAI Testing Platform is designed to solve this exact problem by offering the following capabilities to data scientists -

  1. Comprehensive Testing: 300+ tests to identify any issues with data, model and AI deployment settings.

  2. Breakthrough RagaAI DNA Technology: Creating foundational model based embedding spaces to detect and fix issues related to labelling quality, drift detection, active learning, edge case detection and others.

  3. Multi Modal Platform: Enabling testing across Computer Vision, LLMs / NLP and Structred Data Applications individually or a part of a complex pipeline.

  4. Accessibility and Usability for Various Expertise Levels: The field of AI is diverse, with professionals ranging from seasoned experts to novices. However, many AI development platforms are either too complex for beginners or too simplistic for advanced users, creating a gap in usability and accessibility.

The RagaAI product is designed to help data science teams focus on building the best AI products and not get bogged down with crucial but massive infrastructure development projects.

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