Relari.ai
Relari pairs video demonstrations with surface electromyography (EMG) to study how human biomechanics can unlock force-aware, dexterous robot manipulation.
Relari pairs video demonstrations with surface electromyography (EMG) to study how human biomechanics can unlock force-aware, dexterous robot manipulation.
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Focused on LLM evaluation
Relari specializes in testing and evaluating generative AI applications such as RAG pipelines and agents. It targets the problem of measuring output quality and reliability before and after deployment, and a focused product can go deeper than general-purpose MLOps tools.
Open-source evaluation library
Relari maintains an open-source toolkit, continuous-eval, with modular metrics for components like retrieval and generation. Developers can inspect, customize, and self-run it, which lowers the barrier to trying it and reduces lock-in.
Component-level metrics
It emphasizes evaluating each part of an LLM pipeline (retriever, reranker, generator) separately instead of only the final answer. This makes it easier to pinpoint where quality problems originate and to debug them.
Synthetic data generation
The platform supports creating synthetic test datasets and golden datasets tailored to your use case. This helps teams that lack labeled data build evaluation sets quickly and test edge cases.
Fits iterative development workflows
Its tooling is designed to support continuous testing and benchmarking as prompts, models, and architectures change. Teams can compare configurations and catch regressions, which suits fast-moving AI product development.
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