Key Benefits
Automated Testing
Comprehensive automated test suites for AI models, RAG pipelines, and generative applications — running thousands of test cases in minutes to catch issues that manual review would miss.
Performance Metrics
Measure accuracy, hallucination rate, faithfulness, relevance, latency, throughput, and cost — with quantified scores across all dimensions that matter for your use case, not just generic benchmarks.
Quality Assurance
Built-in QA processes that ensure AI systems meet your quality, reliability, and business standards before deployment — and continue to meet them after, with continuous production monitoring.
Bias & Risk Detection
Identify and quantify bias, fairness issues, safety vulnerabilities, and adversarial weaknesses in AI models and outputs — before they affect real users or attract regulatory attention.
How It Works
Define Your Test Strategy
Select the models, RAG pipelines, or generative applications to evaluate. Define the dimensions of quality that matter for your use case — accuracy, safety, format adherence, domain correctness. Build your golden test dataset with expert-curated ground truth, covering normal inputs, edge cases, and adversarial examples. Set the performance thresholds that must be met before production deployment.
Run Automated Test Suites
Execute comprehensive automated test suites across your AI systems — accuracy benchmarks, regression suites, safety red-teaming, bias assessments, and load tests. Integrate evaluation into your CI/CD pipeline so every model update, prompt change, or RAG configuration is automatically tested before it can reach production. Run evaluations across multiple LLM providers simultaneously for comparative benchmarking.
Analyse, Iterate & Ship with Confidence
Review structured evaluation reports showing performance across all dimensions — with drill-down into specific failure modes, example inputs that caused issues, and recommended fixes. Iterate on models, prompts, and retrieval configurations with each evaluation cycle quantifying improvement. Ship to production only when all thresholds are met — with evaluation evidence archived for compliance and audit purposes.
Features
Automated testing workflows for LLMs, RAG pipelines, generative applications, and classical ML models
Rich performance metrics: accuracy, hallucination rate, faithfulness, answer relevance, context precision, latency, and cost
Golden dataset management — curate, version, and maintain test datasets with expert-labelled ground truth
RAG-specific evaluation using RAGAS metrics: faithfulness, answer relevance, context precision, and context recall
Bias and fairness testing across demographic dimensions with quantified disparity metrics
Safety and red-teaming — adversarial input testing, jailbreak resistance, PII leakage detection
Regression testing to catch performance degradation introduced by model updates or prompt changes
CI/CD integration — automated evaluation gates that block deployment of underperforming changes
Multi-model comparative benchmarking across LLM providers — choose the right model for your use case
Continuous production monitoring — ongoing evaluation of live system outputs for drift and quality degradation
Structured evaluation reports with archived evidence suitable for model risk management and regulatory audit
Use Cases
Pre-Deployment Validation
Run comprehensive evaluation suites before deploying any AI model to production — confirming accuracy, safety, and performance targets are met. Produce evaluation evidence for model risk management sign-off.
RAG Pipeline Optimisation
Systematically evaluate and tune RAG pipeline components — chunking strategy, embedding model, retrieval configuration, and generation prompts — using RAGAS metrics to identify the highest-performing configuration.
Regulatory Compliance Testing
Ensure AI systems meet regulatory requirements before deployment — including fairness testing for credit decisioning models (SR 11-7, ECOA), safety validation for clinical AI (FDA SaMD), and explainability testing for regulated decisions.
Continuous Production Monitoring
Monitor deployed AI systems continuously for output quality degradation, hallucination rate increases, bias drift, and safety issues — with automated alerts when thresholds are breached.
Adversarial & Security Testing
Identify vulnerabilities in AI systems through systematic adversarial testing — prompt injection attacks, jailbreak attempts, PII extraction attempts, and data poisoning scenarios.
Model Selection & Benchmarking
Evaluate multiple LLM providers and model versions against your specific use case and data — making model selection decisions on evidence rather than marketing claims or generic benchmarks.
Why Choose a21.EVALS
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reduction in deployment issues caught post-launch
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faster testing cycles through automation
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test coverage across critical production paths
Evaluation-first delivery — we do not ship AI systems without evidence they meet the performance bar
Domain-specific test datasets built for your use case — not generic academic benchmarks
CI/CD integration that makes evaluation a continuous discipline, not a periodic checkpoint
Compliance-grade evaluation reports — designed to satisfy model risk management, audit, and regulatory requirements
Red-team expertise in regulated industries — we know how adversarial users in financial services and healthcare behave
Active improvement loop — evaluation findings feed directly into model, prompt, and retrieval improvements
Ready to Transform Your Business?
Deploy AI you can stake your reputation on. Discover how a21.EVALS gives you the evidence to ship with confidence — and the monitoring to stay confident after you do.
