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What is RagMetrics?

RagMetrics is a comprehensive platform designed to evaluate and instill trust in conversational GenAI, specifically focusing on assessing the capabilities of AI chatbots, agents, and retrieval-augmented generation (RAG) systems before and after deployment. By providing continuous evaluations of AI-generated interactions, it emphasizes critical aspects such as precision, relevance, the frequency of hallucinations, the quality of reasoning, and the performance of tools used in genuine conversations. The system integrates effortlessly with existing AI frameworks, allowing for the monitoring of live dialogues while maintaining a seamless user experience. Equipped with features like automated scoring, customizable evaluation criteria, and thorough diagnostics, it elucidates the underlying causes of any shortcomings in AI responses and offers pathways for enhancement. Users can also perform offline assessments, conduct A/B testing, and engage in regression testing, all while tracking performance trends in real-time via detailed dashboards and alerts. RagMetrics is adaptable, functioning independently of specific models or deployment methods, which enables it to work with various language models, retrieval systems, and agent architectures. This flexibility guarantees that teams can depend on RagMetrics to improve the efficacy of their conversational AI applications in a multitude of settings, ultimately fostering greater trust and reliance on AI technologies. Furthermore, it empowers organizations to make informed decisions based on accurate data about their AI systems' performance.

What is LLM Council?

The LLM Council functions as an efficient coordination platform that enables users to interact with multiple large language models at once and amalgamate their responses into a single, more trustworthy answer. Instead of relying on a solitary AI, it dispatches a query to a consortium of models, each producing its own independent output, which are then anonymously assessed and ranked by the other models. After this evaluation, a selected "Chairman" model consolidates the most persuasive insights into a unified final response, similar to how experts reach a consensus in collaborative discussions. Generally, this system is accessed through a user-friendly local web interface that utilizes a Python backend and a React frontend, while seamlessly connecting to models from various providers such as OpenAI, Google, and Anthropic through aggregation services. This structured peer-review methodology seeks to identify possible blind spots, reduce instances of hallucinations, and improve the reliability of answers by integrating a range of perspectives and enabling cross-model assessments. By fostering collaboration, the LLM Council not only enhances the output's quality but also cultivates a deeper understanding of the inquiries made, ultimately providing users with richer and more informed answers. This approach encourages ongoing dialogue among the models, promoting continuous refinement and evolution of the responses generated.

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Integrations Supported

Integrations Supported

Claude Opus 3
DeepSeek
DeepSeek V3.1
GPT-5.2
GPT-5.3 Instant
Gemini 3 Pro
Google Slides
Grok 4
Llama
Llama 4 Scout
Microsoft Excel
Microsoft Word
Mistral AI
Python
Qwen
React

API Availability

API Availability

Pricing Information

$20/month
Free Trial Offered?

Pricing Information

$25 per month
Free Version

Supported Platforms

SaaS
On-Prem

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars

Training Options

Documentation Hub

Company Facts

Organization Name

RagMetrics

Date Founded

2024

Company Location

United States

Company Website

ragmetrics.ai/

Company Facts

Organization Name

LLM Council

Company Location

United States

Company Website

llmcouncil.ai/

Categories and Features

AI Observability

Not specified

LLM Evaluation

Not specified

Categories and Features

LLM Evaluation

Not specified

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