Frequently Asked Questions

Green Vectors is patent-pending semantic transformation technology that eliminates redundant vector storage at ingestion. It produces vector representations that are up to 99.5% smaller and 25 to 59% more accurate than conventional vector databases.

Semantic Faceting is the first stage of the Green Vectors process. Before the system decides what to store, it identifies the meaning-bearing concepts inside the corpus and groups related semantic signal into facets. Traditional pipelines treat chunks as the basic unit of retrieval; Green Vectors treats meaning as the basic unit, so redundant fragments, boilerplate, duplicates, and weak signals never become retrieval clutter.

Continuous Vectorization is Morphos AI's patent-pending architecture for storing and updating semantic meaning in real time. It eliminates batch reindexing by incorporating new content into the existing vector representation incrementally, as documents are ingested.

Megachunking is Morphos AI's patent-pending method for representing documents as a hierarchy of semantically coherent chunks rather than fixed-size windows. It eliminates the chunk-size tradeoff in conventional RAG by letting retrieval surface the right level of context for each query.

Auto Weighting is Morphos AI's patent-pending method for scoring how strongly new content should update the existing semantic representation, at the moment of ingestion. It ensures that relevance, not recency or volume, drives what gets stored.

No. Green Vectors keeps your vector database. It replaces what surrounds it. Green Vectors runs alongside Pinecone, Qdrant, Weaviate, pgvector, and similar systems, transforming what gets stored and how retrieval works so the database holds up to 99.5% less data while delivering higher accuracy. The auxiliary stack (rerankers, BM25 sidecars, graph-like retrieval infrastructure, reindex pipelines) is what becomes optional.

No. Continuous Vectorization updates the semantic representation incrementally as new content is ingested. There are no batch reindex jobs and no scheduled rebuild windows. Periodic full rebuilds may still be desirable when changing embedding models, but day-to-day operation requires no reindex.

Yes for most use cases. Lexical and semantic signal are reconciled in a single retrieval pass, eliminating the need for a parallel BM25 pipeline.

First-pass accuracy is high enough that a cross-encoder reranker is unnecessary for most production workloads. Ultra-high-precision applications may still benefit from rerank.

No, and Green Vectors is not a knowledge graph replacement. It is a way to deliver graph-like retrieval value without graph infrastructure: relationship-aware retrieval without entity extraction, ontology design, or a separate graph database to maintain.

No. Compression trades accuracy for smaller storage. Green Vectors discovers and stores only the semantic concepts that carry signal, which produces both smaller storage and higher accuracy.

No. RAG is our initial market focus because the operational leverage is largest there. The same patent-pending architecture extends to edge AI, real-time streaming, recommendations, anomaly detection, multimodal fusion, and continual-learning systems. Morphos AI is selecting design partners for these applications now.

Project Gutenberg (50,000 books, 260GB baseline vector storage reduced to 1.3GB), Elastic Better Binary Quantization (industry benchmark), and a patent-search corpus. Validated figures: 99.5% storage reduction, 25 to 59% accuracy lift, ~4x query latency improvement at 15M-vector scale.