The first vibe-training platform for evals and guardrails

You can use our models across a wide range of semantic tasks, including conversation evaluation, semantic similarity, grounding validation, policy compliance, and more. Explore our use case catalog to see what’s possible

Plurai uses a proprietary intent calibration process to deeply understand your task and generate a high-quality testing set and consistent evaluator. This enables production-grade evals and guardrails powered by optimized small language models (SLMs), which are far more cost-efficient and scalable than traditional LLM-as-judge approaches that are expensive and difficult to run at full production coverage.

Sure, Plurai can be deployed in your VPC for maximum security, data control, and even lower latency. Contact us to discuss your infrastructure and deployment requirements.

Plurai’s SLMs are purpose-built for your specific tasks through our intent calibration and synthetic data generation process. We don’t require prior labeled data. If you don’t have historical datasets, we generate high-fidelity synthetic data tailored to your use case.

By training and optimizing evaluators on highly targeted datasets instead of relying on general-purpose LLMs, we achieve high accuracy with far lower latency and cost. The result is production-grade coverage you can run continuously without the expense of traditional LLM-as-judge approaches.

In addition to purpose-built SLMs, we also offer optimized LLM-based evaluators for maximum accuracy at competitive cost. These are ideal for sampled data and offline evaluation workflows.

For large-scale testing or real-time guardrails, SLMs are typically the better choice due to their lower latency and cost efficiency.

You can use our models across a wide range of semantic tasks, including conversation evaluation, semantic similarity, grounding validation, policy compliance, and more. Explore our use case catalog to see what’s possible