Evaluate GenAI Quality with Confidence
RagMetrics helps GenAI teams validate agent responses, detect hallucinations, and accelerate deployment with AI-assisted QA and human-in-the-loop feedback.
.png)
Why AI Evaluations Matters
Hallucinations erode trust in AI
65% of business leaders say hallucinations undermine trust.
Manual evaluation process doesn’t scale
Automated review cuts QA costs by up to 98%.
Enterprises need proof before deploying GenAI agents
Over 45% of companies are stuck in pilot mode, waiting on validation.
Product teams need rapid iteration
Only 6% of lagging companies ship new AI features in under 3 months.
The Purpose-built Platform for AI evaluations
AI-assisted testing and scoring of LLM / agent output
Reduced hallucinations and accurate outputs.
Human-in-the-loop workflows
Scale your existing AI development team.
Failure detection and quality dashboards
Quickly address issues before they impact the customer.
Testing and Retrieval
Use data-driven insights to improve AI pipelines. Fine-tune the retrieval strategy and understand the changes in performance.
Flexible and Reliable
LLM Foundational Model Integrations
Integrates with all commercial LLM Foundational models, or it can be configured to work with your own.
200+ Testing Criteria
With over preconfigured criteria and flexibility to configure your own, you can measure what is relevant for you and your system.
AI Agentic Monitoring
Monitor and trace the behaviors of your agents. Detect if they start to hallucinate or drift from their mandate.
Deployment Cloud, SaaS, On-Prem
Choose the implementation model that fits your needs -– cloud, SaaS, on-prem. Stand Alone GUI or API model.
AI Agent Evaluation and Monitoring
Analyze each interaction to provide detailed ratings and monitor compliance and risk.
The RagMetrics AI Judge
Overview: Ragmetics connect to foundational LLM models in the Cloud, SaaS, and on-prem, allowing developers to evaluate new LLMs, agents, and copilots before they go to production.
What Client Say About Us
Hear what our clients have to say about their experience working with us. Real stories, real results, real impact.
I was thrilled to see this graph from Aubrey Kayla and Alon Bochman at RagMetrics yesterday. It demonstrates that our RAG methodology at Tellen employing techniques from semantic search to LLM-based summarization significantly outperforms GPT-4 and all other large language models. Excited to boost these numbers by both leveraging more sophisticated RAG—HyDE, reranking, etc. and other language models, for which we're already building private endpoints in Microsoft Azure. Seems Llama3 could be a good bet!
I have had the pleasure to work with RagMetric's founders Alon and Aubrey. They are very knowledgeable on the areas of AI, LLM, as well as business. They know that a successful product is more than just technology. The results provided by RagMetrics are helpful for any AI product development and the company is very open to feedback and customizations. I would recommend anyone with an AI application to look into what RagMetrics can do for their use case.
Frequently Asked Questions
Yes. RagMetrics was built for benchmarking large language models. You can run identical tasks across multiple LLMs, compare their outputs side by side, and score them for reasoning quality, hallucination risk, citation reliability, and output robustness.
Yes. RagMetrics provides a powerful API for programmatically scoring and comparing LLM outputs. Use it to integrate hallucination detection, prompt testing, and model benchmarking directly into your GenAI pipeline.
RagMetrics can be deployed in multiple ways, including as a fully managed SaaS solution, inside your private cloud environment (like AWS, Azure, or GCP), or on-premises for organizations that require maximum control and compliance.
Running an experiment is simple. You connect your LLM or retrieval-augmented generation (RAG) pipeline—such as Claude, GPT-4, Gemini, or your own model—define the task you're solving, upload a labeled dataset or test prompts, select your scoring criteria like hallucination rate or retrieval accuracy, and then run the experiment through the dashboard or API.
To run an evaluation, you’ll need access to your LLM’s API key, the endpoint URL or model pipeline, a dataset or labeled test inputs, a clear task description, and a definition of success for that task. You can also include your own scoring criteria or subject matter expertise.
RagMetrics is model-agnostic and supports any public, private, or open-source LLM. You can paste your custom endpoint, evaluate outputs from models like Mistral, Llama 3, or DeepSeek, and compare results to popular models like GPT-4, Claude, and Gemini using the same scoring framework.
Validate LLM Responses and Accelerate Deployment
RagMetrics enables GenAI teams to validate agent responses, detect hallucinations, and speed up deployment through AI-powered QA and human-in-the-loop review.
Get Started%201.svg)
