One ingestion layer replaces three retrieval layers.
Green Vectors is patent-pending semantic transformation that encodes relevance, structure, and relationships directly into your vector space at ingestion. Hybrid search, rerankers, and graph-like retrieval infrastructure become optional, not architectural.
No parallel pipelines. No reranker latency budget. No ontology management. No reindex windows.
Modern retrieval is held together with duct tape.
The stack you assembled to solve retrieval has become the thing slowing it down.
Green Vectors is not compression. It's semantic transformation.
Conventional vector databases index every fragment of every document and let the query layer figure out what mattered. Green Vectors inverts that order. At ingestion, it discovers the concepts that actually carry signal in your corpus and writes the relevance, structure, and relationships between them directly into vector space.
The result is a vector representation that holds up to 99.5% less data, is more accurate, and faster to retrieve from. Not because we compressed it. Because we never stored what didn't matter in the first place.
The implication compounds at scale. Traditional vector indexes grow linearly with raw content volume. Green Vectors grows with semantic novelty, not document count. Adding more content that overlaps with concepts already in your index adds proportionally fewer new vectors. The more your data grows, the wider the storage gap.
The industry tried to make the warehouse smaller. We stopped storing the noise in the first place.
Cutting through noise · scroll to advance
Not compression. Not another vector database. A cleaner representation layer.
Compression shrinks bloated indexes after they already exist.
Reranking tries to fix noisy retrieval after search.
Green Vectors attack the problem earlier: at ingestion and update time.
| Traditional approach | What it does | Tradeoff |
|---|---|---|
| Quantization | Compresses vectors | Can trade precision for footprint |
| Reranking | Reorders retrieved results | Adds compute and latency |
| Hybrid search | Adds keyword matching | More search-time complexity |
| Green Vectors | Reduces redundant vector representation | Requires benchmark validation on your corpus |
Green Vectors keeps your vector database. It replaces what surrounds it.
Modern RAG runs three moving parts around a vector database, plus a reindex schedule to keep them synchronized. Green Vectors keeps the database and collapses the rest into one ingestion-time transformation.
Hybrid search pipelines
Lexical and semantic signal are reconciled in a single retrieval pass. No parallel BM25 sidecar. No merge logic. No relevance-fusion tuning.
Teams with very specialized lexical needs (e.g., regulatory citation matching) may still layer keyword search on top. Most do not need to.
Reranking
First-pass accuracy is high enough that a second-stage reranker stops being necessary for most production use cases. The latency and compute you spent on a cross-encoder pass is returned to your budget.
Ultra-high-precision applications (medical literature retrieval, legal eDiscovery) may still benefit from rerank. Everyone else gets it back.
Graph-like retrieval infrastructure
Conceptual relationships are inherent to the embedding space, so you get relationship-aware retrieval without entity extraction, schema design, or a separate graph database to maintain.
We are not a knowledge graph replacement. We are a way to deliver graph-like retrieval value without graph infrastructure.
Reindexing pipelines
Continuous Vectorization updates the semantic representation incrementally as new content arrives. No batch reindex jobs. No scheduled rebuild windows. No drift between your corpus and your index.
Periodic full rebuilds may still be desirable when changing embedding models. Day-to-day operation requires no reindex.
How Green Vectors compares.
| Conventional RAG stack | Green Vectors | |
|---|---|---|
| Vector database | Required | Required (drops in alongside yours) |
| Vector storage volume | Full | Up to 99.5% reduced |
| Lexical recall | Separate BM25 pipeline | Semantic + lexical signal unified in retrieval layer |
| Reranking | Cross-encoder pass | Optional |
| Graph-like retrieval | Separate graph database | Inherent to embedding space |
| Indexing on data growth | Scheduled reindex jobs | Live, incremental updates |
| Storage growth as corpus grows | Linear with content volume | Sub-linear, bound by semantic novelty |
| Query latency at 15M vectors | Baseline | ~4x faster |
| Accuracy (Project Gutenberg) | Baseline | 25 to 59% better |
Measured, not projected.
Vector storage reduction
Project Gutenberg corpus (260GB → 1.3GB)
Retrieval accuracy lift over baseline
Project Gutenberg corpus
Query latency improvement
15M-vector benchmark
Every figure on this page is a measured result from a live benchmark. Projected and modeled performance is labeled separately in our investor and partner materials.
One ingestion pass. Three patent-pending innovations.
Vector retrieval has needed so much scaffolding because the storage layer was never designed to carry meaning. Green Vectors fixes that at the source.
At ingestion, Continuous Vectorization identifies the meaning-bearing concepts in your corpus and organizes the representation around the semantic units that actually carry signal. Megachunking preserves meaning across multiple levels of granularity. Auto Weighting prioritizes signal as your corpus grows.
Together, they do at ingestion what traditional retrieval stacks try to patch at query time.
15M vectors → 76K · scroll to distill
Continuous Vectorization
Meaning first. Vectors second. Live updates. No reindexing.
Continuous Vectorization is the parent architecture of Green Vectors. Before the system decides what to store, it identifies the meaning-bearing concepts inside the corpus. Traditional pipelines treat chunks as the basic unit of retrieval. Green Vectors treats meaning as the basic unit, grouping related semantic signal together before redundant fragments, boilerplate, duplicates, and weak signals become retrieval clutter. As your corpus changes, new content updates the existing semantic representation incrementally. No separate indexing job. No scheduled reindex window. No drift between your corpus and your index.
What that buys you: a cleaner retrieval foundation from the beginning, plus data freshness with no operations overhead. Add documents at any rate. The representation reflects them immediately.
Megachunking
Hierarchical document representation
Megachunking breaks the chunk-size tradeoff. Instead of forcing every retrieval into a fixed-size window, Megachunking represents your documents as a hierarchy of semantically coherent chunks and preserves meaning across multiple levels: concept, section, and document.
What that buys you: less truncation, less hallucination from missing context, and no more guessing at chunk size before you know what your queries will look like.
Auto Weighting
Relevance-aware ingestion
Auto Weighting decides what matters at the point content enters the system. Highly relevant content strengthens the matching semantic representation. Repetitive, marginal, or low-value content has less influence.
What that buys you: signal sharpens as your corpus grows. No retraining cycles. No schema design. No manual tuning.
Together
Continuous Vectorization identifies the signal and keeps it current.
Megachunking preserves context across levels of meaning.
Auto Weighting keeps repetitive noise from taking over the retrieval path.
Together, they produce a retrieval system where relevance, structure, and semantic relationships are properties of the data layer itself, not extra scaffolding bolted onto the query pipeline.
Available today through Kitana.
Kitana is the Python SDK that brings Green Vectors to enterprise AI pipelines. It runs alongside your existing vector database, including Pinecone, Qdrant, Weaviate, and pgvector. Through Kitana, teams can evaluate Green Vectors against their current retrieval stack without replacing the database they already use. Powered by Continuous Vectorization, Megachunking, and Auto Weighting. Currently in closed beta.
How to evaluate Green Vectors against your current stack.
Bring your current retrieval baseline. We will help evaluate Green Vectors against the metrics that matter: vector count, storage footprint, latency, retrieval quality, and operational complexity.
Share your current retrieval architecture
Tell us what you use today: vector database, retrieval flow, rerankers, hybrid search, reindexing process, and target workload.
Select a representative dataset
Use a benchmark corpus, sample workload, or internal dataset that reflects the retrieval problems you actually care about.
Compare against your baseline
Evaluate storage, vector count, query latency, retrieval quality, and stack complexity.
Decide whether Kitana belongs in your pipeline
If Green Vectors improves the economics and quality of your retrieval layer, we move into Kitana access or a design-partner engagement.
Where Green Vectors goes next.
Green Vectors was built for retrieval first because that is where the immediate pain is loudest: vector bloat, latency, reranker complexity, and reindexing. But the underlying problem is broader. AI systems are creating, updating, retrieving, and reasoning over semantic data that keeps changing. When that semantic layer becomes redundant, stale, or bloated, every downstream system pays for it. Green Vectors is designed for systems where semantic representations need to stay compact, current, and useful as data changes. The list below is illustrative of where we are headed, not a feature checklist.
Edge AI
Storage reduction makes substantial knowledge bases fit on memory-constrained hardware: phones, IoT, robotics, embedded systems. Demonstrated at scale on commodity edge devices. Production deployments in progress with design partners.
Real-time streaming
Continuous Vectorization is not limited to documents. The architecture supports any data stream where meaningful state evolves over time: sensor feeds, transaction logs, telemetry, social signals. Active design-partner focus.
Recommendations
Personalized recommendations need continuously evolving representations of users, content, and engagement signals. Continuous Vectorization updates in real time as new events arrive, without retraining cycles. Active design-partner focus.
Anomaly detection
The mechanisms underneath Green Vectors discriminate between meaningful events and background noise in real time. Fraud detection, security monitoring, financial-market anomaly detection, and operational anomaly detection are areas of active development.
Multimodal fusion
The architecture is data-modality agnostic. One semantic representation can hold text, sensor data, and other signal types in a common space. Cross-modal applications under development with design partners.
Continual learning
Systems built on Continuous Vectorization adapt as data evolves, without retraining cycles or batch update windows. Applications that need models to reflect a changing world in real time are an emerging design-partner focus.
If your team is building in any of these spaces, we'd like to talk. We are selecting design partners now and are interested in use cases that stretch the architecture.
Become a Design PartnerFrequently asked questions.
New to these concepts? Browse the Morphos AI glossary.