Overview
You cannot improve what you cannot measure — and you cannot trust what you have not tested. Most AI systems are deployed with inadequate evaluation, leading to production failures, compliance breaches, and loss of user trust. Our LLM Evaluation & Testing service gives you a rigorous, systematic approach to measuring AI performance before and after deployment. We build evaluation frameworks, golden datasets, and automated testing pipelines that make it possible to know — with evidence — whether your AI system is accurate, consistent, safe, and fit for its intended purpose.
How It Works with a21
Evaluation Framework Design
Define the dimensions of performance that matter for your use case — accuracy, consistency, safety, format adherence, hallucination rate. Design the evaluation methodology and select or build the metrics.
Dataset Curation & Benchmark Build
Build the golden dataset of test cases that covers your use case space — including edge cases, adversarial inputs, and failure modes. Establish ground truth labels through expert review.
Automated Testing Pipeline
Implement automated evaluation pipelines that run against every model or prompt change — providing continuous measurement and regression detection in CI/CD.
What We Offer
Custom Evaluation Metrics
Design metrics specific to your use case — beyond generic benchmarks. Whether domain accuracy, regulatory compliance, or format adherence — we measure what actually matters.
Golden Dataset Construction
Build and maintain curated test datasets with expert-labelled ground truth, covering normal inputs, edge cases, and adversarial examples.
RAG-Specific Evaluation (RAGAS)
Evaluate RAG pipelines on faithfulness, answer relevance, context precision, and context recall — with quantified scores against your retrieval and generation layers separately.
Hallucination Detection
Implement automated hallucination detection using reference-based and reference-free approaches — measuring the rate and severity of factual errors.
Safety & Red-Teaming
Stress-test AI systems against adversarial prompts, jailbreak attempts, and harmful input patterns — identifying safety vulnerabilities before production.
CI/CD Evaluation Integration
Embed evaluation pipelines into your development workflow — automated testing on every model update, prompt change, or RAG configuration modification.
Why Choose a21
Evaluation Before Everything
We do not ship AI systems without evaluation evidence. Our delivery process requires defined metrics and test results before production deployment.
Domain-Specific Benchmarks
We build evaluation datasets grounded in your domain — not generic academic benchmarks that do not reflect your actual use case.
Automated and Continuous
Our evaluation pipelines run automatically — catching regressions the moment they are introduced, not weeks later when users complain.
Compliance Evidence
Our evaluation frameworks produce the documentation that regulated industries require — evidence of performance testing suitable for model risk validation and audit.
Success Stories
Problem
A lender was deploying AI credit decisioning but had no systematic way to measure accuracy, fairness, or consistency — creating model risk management and regulatory exposure.
Solution
Designed a comprehensive evaluation framework covering accuracy, demographic fairness, consistency, and regulatory compliance. Built a 5,000-case golden dataset and automated evaluation pipeline in CI/CD.
Problem
A healthtech company needed to validate NLP models used in clinical documentation against FDA and EMA standards for software as a medical device (SaMD).
Solution
Designed a clinical evaluation framework, curated a 3,000-case annotated test set with clinical expert review, and produced structured validation documentation aligned with IEC 62304.
Tech Stack & Tools
RAGAS
DeepEval
LangSmith
Promptfoo
Giskard
Pytest
W&B
Argilla
Get Started
Know your AI works before your users find out it does not. Talk to a21 about AI evaluation.
