Plurai is an AI agent trust platform that helps teams evaluate, protect, and improve production AI agents. You describe what your agent should and should not do, and Plurai generates training data, validates it through a multi-agent debate process, and deploys a custom small language model in minutes. No labeled data, annotation pipelines, or prompt engineering needed. The platform delivers sub-100ms latency at 8x lower cost than GPT-as-judge approaches, making it practical to run on every interaction rather than just sampling.

• Vibe-Training for Evals: Describe your agent's expected behavior in plain language and get a custom evaluation model deployed in minutes, with no labeled data or annotation pipeline needed.
• Purpose-Built Small Language Models (SLMs): Custom-trained models that deliver sub-100ms latency and 8x lower cost compared to using GPT-as-judge, with over 43% fewer failures.
• Edge-Case Scenario Generation: Automatically generate thousands of realistic edge-case scenarios tailored to your specific agent using the IntellAgent framework.
• Real-Time Guardrails: Always-on protection that runs on every interaction, not just sampled data, covering conversation evaluation, semantic similarity, grounding validation, and policy compliance.
• Synthetic Data Generation: Generate high-fidelity synthetic data tailored to your use case when you don't have historical datasets available.
• VPC Deployment Option: Deploy within your own infrastructure for maximum security, data control, and lower latency.
• Multi-Agent Debate Validation: Training data is validated through a multi-agent debate process before model deployment, improving accuracy.
• CI/CD Integration: Continuous validation pipelines that integrate into your existing development workflow for ongoing agent testing.

• No data requirements to start: You can build custom evals without any labeled data, annotation pipelines, or prompt engineering, lowering the barrier to entry significantly.
• Cost-effective at scale:Run continuous AI evaluation at up to 8x lower cost than GPT-as-judge systems, powered by fast SLM inference ($0.15 per 1K tokens) and sub-100ms latency, making real-time evals practical in production.
• Fast deployment: Custom evaluation models can be trained and deployed in minutes rather than weeks of manual pipeline setup.
• Research-backed approach: Built on IntellAgent and related AI evaluation research with strong focus on agent reliability.
• Enterprise-ready infrastructure: Powered by NVIDIA Nemotron and NIM, with AICPA verification and on-premises deployment options.
• Generous free tier: 1 million free tokens lets you test the platform without commitment.

• Learning curve for concepts: Vibe-training, SLMs, and multi-agent debate validation are - novel concepts that may take time to understand and apply effectively.
• Growing Ecosystem: Third-party integrations and the broader ecosystem are still expanding and are currently less mature than those of more established AI tooling platforms.

• Customer Service Agent Quality Assurance: Run your support chatbot through thousands of simulated edge-case conversations to find failure points before customers do. Plurai generates realistic scenarios covering policy violations, ambiguous requests, and adversarial inputs, giving you a detailed report of where your agent breaks down.
• Policy Compliance Monitoring: Set up always-on guardrails that check every agent response against your company policies in real time. With sub-100ms latency, responses are validated before reaching the user, preventing brand-damaging outputs without adding noticeable delay.
• Pre-Deployment Agent Testing: Before pushing a new agent version to production, automatically stress-test it across diverse user personas and complex multi-turn conversations. Identify regressions and performance gaps with detailed analytics comparing current vs. previous versions.
• Grounding and Hallucination Detection: Validate every response in real time to ensure it is grounded in your knowledge base or context, using fast SLM-based evaluation and always-on guardrails that detect ungrounded outputs before they reach users.
• Conversational AI Development Iteration: Use the IntellAgent framework during development to continuously generate challenging scenarios, evaluate agent performance, and identify priority areas for improvement without waiting for real user data to accumulate.