Dive Deeper
A plain-language overview of our methodology, with focus on educational clarity and research discipline.
BioLensTracker uses AI-supported workflows to organize biotech event information into useful summaries. Language models help structure dense text into readable explanations, while machine-learning style pattern analysis highlights recurring historical behavior around catalysts.
Our educational framework anchors event interpretation to basic scientific context such as trial stages, endpoints, and topline readouts. This helps users understand why some catalysts matter more than others.
We present information with a research-first approach. Users are encouraged to compare multiple sources, question assumptions, and treat every signal as a learning input rather than a guaranteed outcome.