AI Evaluation & Monitoring
Lumenova AI enables continuous evaluation and monitoring of AI systems to help organizations detect issues early, ensure consistent performance, and uphold responsible AI standards. Our platform combines qualitative and quantitative testing with real-time monitoring across data, models, and frameworks, empowering teams to act quickly and maintain oversight throughout the AI lifecycle.
Key capabilities include:
- Library of configurable tests across fairness, robustness, and performance
- Real-time monitoring for data drift, model degradation, and compliance gaps
- Alerts and insights to support timely intervention and model improvement
Trustworthy AI: No Assumptions Allowed
AI Evaluation & Monitoring is a key component of any robust AI Governance Platform. It involves continuous observation of AI systems in real-world environments to assess their behavior over time. This way, organizations are empowered to know how their AI performs, instead of just assuming everything will be fine.
For enterprises deploying AI at scale, especially in highly regulated sectors, evaluating and monitoring AI isn’t optional. It’s a necessity to prevent drift, manage AI risk, and ensure transparency.
Comprehensive Model Observability
Performance
Measure precision, recall, F1 scores, latency, confidence intervals, and business-specific KPIs to keep models aligned with enterprise goals.
Bias & Fairness
Analyze model outcomes across demographic and protected groups to uncover disparities, enforce fairness thresholds, and meet regulatory standards.
Drift
Identify distribution shifts in data inputs and outputs to flag when models deviate from expected performance over time.
Hallucinations
Monitor generative AI systems for fabricated outputs, source inconsistencies, and factual reliability issues.
Explainability
Surface model decision pathways with built-in explainability modules, vital information for internal accountability and regulatory audits.
Robustness
Stress-test models against edge cases, adversarial inputs, and real-world variability to ensure stable performance.
Exhaustive AI Evaluation
Foundation & Fine-Tuned LLMs
Evaluate the behavior of both out-of-the-box LLMs and fine-tuned versions tailored to your enterprise tasks or domains.
Text Embedding Models
Measure the relevance, semantic accuracy, and drift of text embeddings used in similarity search, RAG pipelines, and classification models.
Retrieval Augmented Generation (RAG) Systems
Assess the reliability, factual consistency, and retrieval quality of RAG pipelines by evaluating both the retriever and generator components. Identify hallucinations, irrelevant context usage, and alignment with enterprise knowledge sources.
Internal & Third-Party Model Oversight
Monitor and compare the performance of both proprietary models and external vendor solutions to ensure ongoing alignment with your AI governance policies.
See the Full Picture of Your AI Models
Move from black-box AI to explainable, compliant, and trustworthy models.
With end-to-end AI Evaluation & Monitoring, your organization can:
- Detect risks early
- Reduce model failure in production
- Support regulatory reporting
- Align technical metrics with business outcomes
AI Evaluation and Monitoring Blogs
January 6, 2026
Avoiding Costly Mistakes: How External Validation of AI Models Minimizes AI Risk Exposure
Mitigate risk with external validation of AI models. See how independent audits prevent fines and ROI failure in finance & banking.
December 30, 2025
How GenAI Monitoring Safeguards Business Value in High-Stakes Industries
Avoid costly AI failures. Discover how Gen AI monitoring protects against hallucinations, bias & regulatory risks in high-stakes industries.
October 7, 2025
The Competitive Edge of Continuous AI Model Evaluation
Discover how continuous AI model evaluation prevents drift, ensures compliance, and turns responsible AI into a competitive advantage.