Offline & Real-time AI Evaluations
Lumenova AI enables both pre-deployment and ongoing evaluation 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 evaluations watch for data drift, model degradation, and compliance gaps
- Alerts and insights to support timely intervention and model improvement
Trustworthy AI: No Assumptions Allowed
AI Evaluations are a key component of any robust AI governance platform. Pre-production, offline evaluations benchmark AI systems on metrics like precision, recall, and hallucination rates. Then, once a system is in use in the “real world,” the Lumenova AI platform conducts ongoing tests to detect issues like toxicity, latency pikes, policy violations, concept drift, and more.
By utilizing cutting-edge techniques, teams can compare how new models affect actual business KPIs to ensure that an AI which “passed” its offline tests actually delivers value in the wild.
Measure What Matters Most with 200+ Metrics
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.
Agentic AI Governance
Gain full visibility into every step of AI agent execution, from tool calls and decision paths to chain-of-thought reasoning and message flows.
Catch Issues Earlier with Proactive Evaluations
Move from black-box AI to explainable, compliant, and trustworthy models.
With end-to-end AI evaluation, your organization can:
- Detect risks early
- Reduce model failure in production
- Support regulatory reporting
- Align technical metrics with business outcomes
