Source: https://daphnislabs.com/services/ai-evaluation-guardrails

# LLM evaluation and AI guardrails

Compare releases against real scenarios and define what happens when checks fail.

[Review My AI Tests](https://daphnislabs.com/contact?service=ai-evaluation-guardrails) [See Deliverables](https://daphnislabs.com/services/ai-evaluation-guardrails#deliverables)

**Test cases**

**Reference answers**

**Candidate system**

**Pass**

**Review**

**Block**

**Evaluation**

Cases

Run

Score

Compare

Decide

Monitor

## Relevant technologies

-   ![LangSmith](https://daphnislabs.com/images/ai-models/langsmith.svg) LangSmith
-   Python
-   Hugging Face
-   OpenAI
-   ![Claude](https://daphnislabs.com/images/ai-models/claude.svg) Claude
-   GitHub Actions

[All technologies](https://daphnislabs.com/#technologies-we-use)

## What Should We Test?

[Explore services](https://daphnislabs.com/services)

-   ### Release Regressions
    
-   ### Grounded Answers
    
-   ### Tool Selection
    
-   ### Sensitive Content
    
-   ### Output Formats
    
-   ### Failure Recovery
    

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## What You Actually Get

-   **Scenario Dataset**
-   **Scoring Rubrics**
-   **Evaluation Harness**
-   **Baseline Report**

-   **Policy Configuration**
-   **Release Gate**
-   **Failure Catalogue**
-   **Reviewer Playbook**

## An unsupported promise. Caught before release.

9:41 •••

**Answer review**

Can I return a mug after 45 days?

### A grounded response

The policy allows returns within 30 days. Contact support about an exception.

Candidate held for review

### Evaluation record

**Test**

Return-window question

**Source**

Returns policy · 30 days

**Failure**

Unsupported 60-day claim

**Release**

Blocked pending correction

Example record

Illustrative animation · Sample scenario

### Read this example

Can I return a mug after 45 days?

1.  **Candidate answer.** Yes, returns are always accepted within 60 days. _Checking against the policy._
2.  **Policy mismatch found.** The supplied policy says 30 days. The 60-day promise has no source. _Release check failed._
3.  **A grounded response.** The policy allows returns within 30 days. Contact support about an exception. _Candidate held for review._

A failed source check prevents the unsupported candidate from becoming the released answer.

Scripted illustration using sample information, not a client case study or a live system.

## What Does It Take to Build?

[Get a custom estimate](https://daphnislabs.com/contact?service=ai-evaluation-guardrails)

Pilot

Production

Enterprise

-   ### Pilot
    
    -   One AI feature
    -   A focused scenario set
    -   Baseline assessment
    
    [Get an Estimate](https://daphnislabs.com/contact?service=ai-evaluation-guardrails)
-   Recommended
    
    ### Production
    
    -   Release comparisons
    -   Broader failure coverage
    -   Team review workflow
    
    [Review My AI Tests](https://daphnislabs.com/contact?service=ai-evaluation-guardrails)
-   ### Enterprise
    
    -   Multiple products
    -   Domain-specific rubrics
    -   Custom policy requirements
    -   Continuous evaluation support
    
    [Talk to Us](https://daphnislabs.com/contact?service=ai-evaluation-guardrails)

02 / 03

**Founded in**

2013

**Projects delivered**

550+

**Client countries**

43+

**Global offices**

3

Engineering teams New Delhi · Dubai · Cyberjaya

Proof [Case studies](https://daphnislabs.com/case-studies)

Stack [Technologies we build with](https://daphnislabs.com/#technologies-we-use)

[AI Development](https://daphnislabs.com/services/ai-development) [Agentic AI Automation](https://daphnislabs.com/services/agentic-ai-automation) [LLM App Development](https://daphnislabs.com/services/llm-app-development) [AI for SaaS](https://daphnislabs.com/solutions/ai-for-saas)

## FAQs

### Can guardrails guarantee safe answers?

No. Tests and policies reduce specific risks but cannot cover every input or failure. We document known limitations and keep fallback and escalation behavior explicit.

### Where do test cases come from?

Representative user requests, known failures and deliberately difficult scenarios. Sensitive data should be removed or handled under the agreed data-access policy.

### Can we test a system built by another team?

Yes. We need an agreed way to run representative inputs, inspect the relevant outputs and compare results. Access to traces can make failures easier to diagnose.

### Do you use a model to grade another model?

Where useful, model-based grading can supplement deterministic checks and human review. The grading method itself needs calibration against examples your reviewers have assessed.

### How do thresholds become release gates?

We agree which checks are blocking, which need review and which are informational. The gate can then run in the existing release process with a clear override policy.

### What changes after launch?

New failure cases should join the evaluation set. Review criteria and monitoring can evolve as users, models and the surrounding application change.

## Which failure should your next release catch?

Bring sample requests, current outputs and the criteria your reviewers use.

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