EvalMy.AI
Automated testing API for AI-generated answers — verify accuracy, completeness, and correctness against a source of truth.
| What is it | Automated testing API for AI-generated answers — verify accuracy, completeness, and correctness against a source of truth. |
|---|---|
| Pricing | Freemium |
| Free tier | Yes |
| Platform | Web Application |
| API | Yes |
| Best for | Automated testing of Retrieval-Augmented Generation (RAG) systems, Continuous integration/continuous deployment (CI/CD) for AI projects |
| Domain registered | 2024 |
Data updated Aug. 1, 2026
What does EvalMy.AI do?
EvalMy.AI is a specialized tool for developers and teams who need to automatically verify the accuracy of answers generated by AI models. At its core, it's an API service that compares an AI's output against a known correct answer, providing a detailed score. This is crucial for applications like customer support chatbots, knowledge base assistants, or any system where factual correctness is non-negotiable. You feed it a question, the AI's response, and the expected answer; it returns a nuanced evaluation, saving you from manual, tedious checking.
What sets EvalMy.AI apart is its proprietary C3-Score, a three-part metric that goes beyond simple keyword matching. It assesses Completeness (are all facts present?), Correctness (is there any hallucinated or extra information?), and Contradiction (is the answer logically consistent?). The tool is built for integration, offering both a straightforward REST API and a Python client library. This means you can plug it directly into your development workflow, LangChain projects, or CI/CD pipelines to run automated tests every time you update your model.
This tool is a major time-saver for AI studios, data science teams, and any developer building reliable, production-grade AI applications. If you're tired of manually spot-checking your chatbot's responses or need a systematic way to catch regressions in your model's performance, EvalMy.AI provides the automated, scalable validation layer. It's especially valuable for teams deploying RAG systems, where ensuring the retrieved and generated information is accurate is paramount to user trust and system success.
Key features
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