Women’s Health Foundation – Semantic Classification POC

The Challenge A humanitarian foundation focusing on women’s health faced a complex problem: determining the highest impact decision points in contraception adoption for specific markets and demographics. Two strategic objectives drove the initiative—first, understanding the multifaceted factors (from product attributes to social influences) that guide women’s contraceptive choices, and second, identifying actionable insights from disparate data sources. The key challenge was integrating internal survey response data with internal investment documents to answer nuanced competency questions such as, “What are the most frequently cited factors when considering a contraceptive method?” and “Which factors most strongly influence adoption or rejection?” This required a system that could not only ingest and organize heterogeneous data but also enable executives to visualize and act upon insights derived from complex cross-document analyses.   The Solution To address these challenges, the project team developed a proof-of-concept (POC) that leveraged advanced graph technology combined with AI-augmented classification techniques.  The solution was implemented across several workstreams: Defining System FunctionalityThe initial phase involved clearly articulating the use case.