Grounding AI in Truth: Pioneering Evaluation for Language Model Application Anchored in Reality.
We train and release specialized language models tailored for evaluating key metrics like faithfulness, toxicity, hallucination, and relevance in AI outputs and inputs. Our models aim to outperform generalized approaches, offering cost-effective and customizable evaluation solutions.
Fostering transparency and collaboration is at the core of our values. We open-source our evaluation models on Hugging Face and develop an evaluator package on GitHub, allowing users to evaluate AI applications locally while contributing to the advancement of evaluation techniques.
Our team of experts offers consulting services to organizations seeking guidance on building advanced AI applications, leveraging techniques like retrieval-augmented generation, summarization, and AI tooling. We ensure your AI solutions perform as expected, even in production environments.
Through our commitment to responsible AI development, transparency, reproducibility, and accessibility, we aim to shape the future of generative AI, grounded in ethical principles and fueled by collaborative efforts with the global AI community.
We provide a comprehensive suite of evaluation metrics and methodologies to assess the performance of LLMs and RAG systems across various dimensions, ensuring data privacy, security, and trust.
We aim to provide a standardized and reproducible evaluation framework for consistent and reliable LLM assessment.