Create evaluation protocols and benchmark testing for your AI systems. Ensure accuracy, safety, and performance with ongoing evaluation harnesses and quality monitoring.
AI, Engineering, and Quality leaders who need to ensure their AI systems are accurate, safe, and compliant. Essential for production AI deployments where errors have business impact.
40–60%
Fewer AI mistakes through systematic testing and optimization
15–30%
Better precision and recall through evaluation-driven optimization
Validated
Documented testing and evaluation for regulatory requirements
20–40% lower
Optimized prompts and models reduce API costs
Test datasets, evaluation metrics, and automated testing framework tailored to your use cases
Performance metrics, accuracy scores, bias detection, and quality trends over time
Systematic prompt engineering, A/B testing, and performance optimization
Continuous testing, performance tracking, and alerting for quality degradation
Evaluation reports, test results, and documentation for audits and regulators
Define evaluation criteria, create test datasets, and design evaluation metrics for accuracy, bias, safety, and compliance.
Develop evaluation harness, run baseline tests, optimize prompts and models, and create dashboards for monitoring.
Continuous evaluation, performance tracking, alerting for issues, and regular optimization based on results.
Ongoing
Initial setup: 2–4 weeks, then monthly retainer for continuous evaluation
4–8 hours/month
For reviews, feedback, and optimization discussions
$5,000–$15,000/month
Monthly retainer for ongoing evaluation and monitoring
Precision
Recall
Factuality
Cost per inference
Error rate
Bias detection score
Safety compliance %
Evaluation coverage
Model drift detection
Prompt effectiveness
Response quality score
Compliance readiness
Risk: Tests miss important failure modes, leading to production errors.
Safeguard: Comprehensive test coverage, edge case testing, red-team evaluation, and continuous test suite expansion based on production issues.
Risk: AI performance degrades over time without detection.
Safeguard: Continuous monitoring, automated alerting, regular re-evaluation, and performance trend tracking.
Challenge: Customer support chatbot making errors, no systematic way to test or improve accuracy.
Solution: Built evaluation harness with test cases, optimized prompts, and continuous monitoring. Reduced errors by 50% and improved customer satisfaction.
Impact: 50% error reduction, 30% improvement in accuracy, $50K annual savings from reduced support escalations.
Challenge: Clinical decision support AI needs validation for regulatory compliance and patient safety.
Solution: Comprehensive evaluation framework testing accuracy, bias, and safety. Ongoing monitoring and compliance documentation.
Impact: Regulatory approval, zero safety incidents, improved clinical outcomes, documented compliance for audits.
We work with your team to identify critical use cases, edge cases, and failure modes. We create test datasets based on real production data (anonymized), synthetic test cases, and adversarial examples. Test cases cover accuracy, bias, safety, and compliance requirements.
Yes. We test for demographic bias, fairness across protected classes, and representational bias. We use established frameworks (Fairlearn, Aequitas) and custom tests based on your specific requirements. We provide bias reports and recommendations for mitigation.
We run evaluations continuously (daily or weekly depending on your needs) and provide monthly reports. For critical systems, we can run evaluations on every deployment. We also trigger evaluations when performance metrics degrade.
Our evaluation harnesses are designed to adapt. We update test cases as your systems evolve, and we can integrate evaluation into your CI/CD pipeline for automated testing on every change. We also track performance trends over time.
Book a 20-minute fit call to discuss your AI evaluation needs and see if PromptOps is right for you.
Last updated: November 2025