Backed by Research & Industry Best Practices

Lumina-AI is built on proven methodologies and research from leading institutions and industry experts.

Datamorphic Testing

A methodology specifically designed for testing AI applications, addressing unique challenges in AI system validation.

Zhu et al., 2019 - arxiv.org/abs/1912.04900

LeanAI Method

Structured approach for effectively planning AI implementations, delineating what AI should, can, and will solve.

Agrawal et al., 2023 - arxiv.org/abs/2306.16799

AI-Powered Test Automation

Systematic review and empirical evaluation of AI-based test automation tools and their effectiveness.

Garousi et al., 2024 - arxiv.org/abs/2409.00411

Search-Based Fairness Testing

Critical for identifying and mitigating biases in AI systems, promoting ethical and equitable outcomes.

Research Paper - arxiv.org/abs/2311.06175

Industry Insights: MIT research shows 95% of generative AI implementations fail to impact P&L due to flawed integration. Gartner reports 87% of AI projects never reach production. Lumina-AI addresses these challenges with Shift Left testing and comprehensive guardrails.