strategy-material-engine

You can retrieve business case studies using vector search by importing raw data into the engine, structuring knowledge with tags, and querying through vector similarity to locate relevant marketing insights and source references.

Structuring raw marketing data involves organizing materials into structured, tagged, and linked formats. This process centralizes business cases and content sources to facilitate efficient retrieval and content derivation for AI analysis.

Yes, you need to install faiss, transformers, numpy, scikit-learn, and pyyaml. These dependencies power the vector similarity indexing and custom scripts required for the knowledge retrieval and content structuring processes.

Yes, you can derive atomic content from structured business cases. The system allows marketing and product teams to trace source references and extract atomic materials to build reports and analyze campaign stories.

Vector similarity search for business case retrieval works by indexing structured data using transformers and faiss. It matches query semantics against stored business cases, stories, and insights to return the most relevant strategy materials.

While scikit-learn and numpy support data processing, large-scale knowledge retrieval may face performance limits. The system relies heavily on faiss for optimized vector similarity search to handle structured data efficiently.