Evals & guardrails

Improve agent quality and prevent realtime glitches in up to 15x less the cost

Powered by auto-trained SLMs with unmatched accuracy and disruptive costs.
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Failure rate reduction
vs GPT 5.2
>43%
Read our paper
Cost reduction
vs GPT 5.2
>8x
See for yourself
Inference
latency
<100ms
Test our public
Choose product
Evals
Guardrails

Say goodbye to costly, inconsistent LLM-as-a-judge evaluations

Powered by auto-trained SLMs with unmatched accuracy and disruptive costs.
Build high-accuracy eval SLMs in minutes — from data samples or a simple prompt
Get a dedicated eval endpoint and synthetic training set, calibrated to your use case
Scale production agent evaluation at up to 15× lower cost

Real-time protection for unbreakable agents with our ultra fast guardrails

Control and manage high impact agentic failures effortlessly, with zero compromise on policy compliance, data security and brand integrity.
<100ms latency SLM Guardrails empower real-time intervention without impacting agent response time
Our high-accuracy guardrails are automatically trained and ready to use within minutes, given a simple prompt and a short optimization process
Our disruptive inference cost allows no-brainer scaling for high errors coverage and maximal quality

How it works

01

Describe in free language your eval/guardrail task

* You can also add data samples from your agent
02

Review the auto-generated test set, anditerate via AI chat if needed

03

Your eval or guardrail endpoint 
is ready to use!

* Full SLM background optimization completes in minutes

Beyond evals & guardrails: powering all semantic tasks

Explore our use case catalog to unlock the full potential of our SLMs.
Select use case
Agent response
Conversation evaluation
Grounding
Reference evaluation
User response classification
Sentiment Analysis
Semantic similarity
Policy compliance classification
Toxicity
User intent detection
Tool invocation validation
Agent action validation

Agent response

Evaluate whether the agent response complies with policy, avoids sensitive data exposure, follows brand tone, and meets required attributes.
Input
<Agent response>
Output
Classification on agent response (for example: helpful, not helpful / PII Detected, PII not Detected) & reasoning

Conversation evaluation

Evaluate multi-turn conversations for compliance, consistency, sentiment, and successful task resolution.
Input
<conversation between agent and user>
Output
Conversation evaluation (for example: resolved, unresolved / compliant, violation / consistent, inconsistent / positive sentiment, frustrated sentiment) and reasoning.

Grounding

Evaluate whether the agent response is fully supported by the provided context and does not introduce unsupported claims or contradictions. A response is grounded if all factual statements are supported by the context.
Input
Context:
<retrieved context or source material>

Agent response:
<agent response>
Output
Grounding classification (for example: grounded / partially grounded / not grounded)

Reference evaluation

Evaluate whether the agent response matches the expected reference answer in correctness and completeness. A response is correct if it aligns with the reference meaning and key facts. Partially correct if it captures some but not all required information or introduces minor inaccuracies. Incorrect if it contradicts or significantly deviates from the reference.
Input
Reference answer:
<golden answer>

Agent response:
<agent response>
Output
Reference evaluation classification  (for example: correct / partially correct / incorrect)

User response classification

Classify the user message according to its primary intent to enable correct routing or handling. Select the label that best represents the user’s goal or request.
Input
User message:
<user response>
Output
User intent classification
(for example: billing / technical_support / sales / account_management / general_query / other)

Sentiment Analysis

Determine the emotional tone expressed in the user message. Identify whether the sentiment is positive, neutral, or negative, and capture stronger signals such as frustration or satisfaction when present.
Input
User message:
<user response or multi turn conversation>
Output
Sentiment classification
(for example: positive / neutral / negative / frustrated / satisfied)

Semantic similarity

Evaluate the semantic similarity between two text samples based on meaning rather than exact wording.
Determine whether the two inputs express the same intent, a related idea, or different meanings.
Input
Sample A:
<text A>

Sample B:
<text B>
Output
Semantic similarity classification
(for example: same_meaning / related / different)

Policy compliance classification

Evaluate whether the agent response complies with safety, legal, and company policies. A response is a violation if it enables harmful, unsafe, illegal, or policy-restricted behavior, exposes sensitive data, or fails to appropriately refuse disallowed requests.
Input
Agent response:
<agent response>
Output
Policy compliance classification
(for example: compliant / violation)

Toxicity

Evaluate whether the message contains toxic, abusive, or harmful language. Toxic content includes insults, harassment, hate speech, threats, or condescending and demeaning tone. Non-toxic content may express frustration or criticism without abusive or harmful language.
Input
Message:
<agent or user text>
Output
Toxicity classification (for example: toxic / safe)

User intent detection

Identify the primary intent expressed in the user message. Determine what the user is trying to accomplish, such as asking a question, requesting an action, reporting an issue, or providing feedback.
Input
User message:
<user message>
Output
User intent classification
(for example: question / request_action / complaint / feedback / information_request / other)

Tool invocation validation

Evaluate whether the tool selected or invoked by the agent is appropriate for the user’s request and context. A correct tool invocation matches the user’s intent and is necessary to fulfill the task. An incorrect invocation uses the wrong tool, or a tool that does not address the request. An unnecessary invocation uses a tool when no tool was required.
Input
User request:
<user message>

Invoked tool:
<tool name or action>
Output
Tool invocation classification
(for example: correct_tool / wrong_tool / unnecessary_tool)

Agent action validation

Evaluate whether the agent’s action correctly follows the user’s request and system constraints. A correct action fulfills the user’s intent and is appropriate for the context. An incorrect action does not match the request or performs the wrong operation. An unnecessary action performs a step or operation that was not required to fulfill the user’s request.
Input
User request:
<user message>

Agent action:
<action taken by agent>
Output
Agent action classification
(for example: correct_action / incorrect_action / unnecessary_action)
Can't find what you're looking for?
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FAQ

How do I use the evals and guardrails on my agents?
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
How is that different from the evals I already have?
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.
Can you do this on prem?
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.
What makes your SLMs so accurate and cost-effective?
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.
Do you only have SLMs or other models as well?
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.
Is Plurai’s Proton product only 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