Vibe-train evals and guardrails tailored to your use case
Vibe training for AI agent reliability. Describe what your agent should and should not do — Plurai generates training data, validates it, and deploys a custom model in minutes. It feels like vibe coding, but for evaluation and guardrails. No labeled data. No annotation pipeline. No prompt engineering. Under the hood, small language models deliver sub 100ms latency, 8x lower cost than GPT as judge, and over 43% fewer failures. Always on, not sampled. Built on published research (BARRED).
Plurai's intent calibration and synthetic data generation process creates high-fidelity test sets without requiring prior labeled datasets, allowing teams to build production-grade evals for custom tasks immediately.
Plurai's SLMs cost 86.9% less than GPT-4 Mini and achieve 43% lower failure rates. Users pay $0.15/1M tokens instead of $0.3+ for LLM alternatives, enabling production-grade coverage at scale without expensive LLM-as-judge overhead.
Plurai supports VPC deployment with on-prem options on Business/Enterprise plans, providing maximum security, data control, and even lower latency for organizations with strict infrastructure requirements.
Plurai delivers <100ms inference latency for guardrails using optimized SLMs, enabling real-time safety checks on agent outputs without sacrificing accuracy or incurring high LLM costs.
Plurai combines fast inference (<100ms), high accuracy (43% failure reduction vs GPT), and low cost (8x cheaper), allowing teams to run continuous production-grade evals and guardrails without choosing between speed, safety, or budget.