What Is Ingestion-Time Optimization?

Ingestion-time optimization improves vector search efficiency at the point data enters the system, before it is stored, rather than relying only on query-time techniques like reranking. By optimizing what is written to the vector database, ingestion-time optimization reduces storage, improves latency, and improves accuracy at the source. Morphos AI applies ingestion-time optimization through patent-pending Green Vectors technology.

Ingestion-time vs query-time optimization

Most RAG optimization happens at query time. Reranking, hybrid search, and query expansion all try to improve results after the index is already built. These techniques add compute cost to every query and cannot fix a bloated or noisy index.

Ingestion-time optimization works earlier. By transforming and reducing vectors before they are stored, it produces a smaller, cleaner index. Improvements compound at every query because the underlying index is better, not because each query does more work.

Why ingestion-time optimization scales better

Query-time techniques add cost that scales with query volume. Ingestion-time optimization is applied once, as data enters, and benefits every subsequent query at no additional per-query cost. For high-query-volume applications, this is a structural advantage.

Where this fits

Ingestion-time optimization is the architectural principle behind Green Vectors and Kitana, which process data before it is written to existing vector databases.