The science of learning, encoded.
We don't just build software; we engineer cognitive frameworks. Discover the peer-reviewed methodologies and proprietary machine learning architectures that power Lumi's predictive engine.
Featured Publications
Deep dives into the mathematical models driving our platform.
Predictive Validity using Item Response Theory (IRT)
Classical Test Theory (CTT) treats all questions equally. Discover how Lumi implements a 3-Parameter Logistic (3PL) model to calculate true latent ability and accurately forecast exam scores.
Modeling Memory Decay with FSRS
The human brain operates on a predictable forgetting curve. Read how our engine utilizes the Free Spaced Repetition Scheduler (FSRS) to calculate exact memory half-lives and radically increase study efficiency.
Our Methodology
We believe in transparent algorithms. Our engineering and academic teams publish their findings to ensure our platform is grounded in rigorous scientific reality, not just marketing buzzwords.
Every behavioral interaction—from hesitation milliseconds to answer changes—is sanitized, validated, and securely hashed before feeding into our latent trait models.
Our projected exam scores are continuously back-tested against actual student results (e.g., JEE Advanced, NEET) to iteratively calibrate our item difficulty and discrimination indices.
We utilize isolated vector embeddings for clustering (K-Means) without compromising PII. Insights are generated surgically, strictly maintaining user anonymity.
Bring empirical science to your campus.
Integrate our validated predictive models into your institution's curriculum via our Enterprise API.
View Enterprise Solutions