Vector compression for semantic search.
EigenDB uses structure in embedding spaces to reduce storage while preserving search quality.
EigenDB
Vector embeddings are central to RAG, semantic search, and knowledge bases. EigenDB uses redundancy in these spaces to reduce storage without retraining.
40× Compression
Reduce storage costs with low impact on search quality.
>95% Precision
Maintains semantic fidelity of your original embeddings.
Zero Training
Works on any existing embedding model without retraining.
Technical demo
Try compression on demo embeddings or upload your own file.
Click to analyze 1,000 random embeddings and see compression results
Use Cases
Vector databases at scale
RAG with millions of documents
Semantic search on edge devices
Measured results
Internal benchmarks with documented methodology.
EigenDB Performance
EigenDB compared with alternatives
Benchmarks on 384-dimensional embeddings (sentence-transformers)
| Metric | FAISSVerified | ChromaVerified | ElasticsearchVerified | WeaviateVerified | Pinecone | EigenDBVerified |
|---|---|---|---|---|---|---|
| Compression | 1x | 1x | 1x | 1x | 1x | 24xBest result |
| Recall@10 | 100% | 100% | 100% | 100% | 95%+ | 100% |
| Storage Cost | 100% | 100% | 100% | 100% | 100% | 4% |
| Search Latency | 1.39ms | 0.56ms | 5.86ms | 1.09ms | 26-60ms | 0.04ms |
| Index Build | 0.16ms | 40.5ms | 861ms | 1298ms | managed | 0.019ms |
Dataset: 500 embeddings, 384D (sentence-transformers/all-MiniLM-L12-v2). Benchmarks run on local hardware.
FAISS, Chroma, Elasticsearch, Weaviate: our benchmarks. Pinecone: official documentation data.
The main difference is compression.
In this benchmark, alternatives store all dimensions. EigenDB reduces dimensions and preserves measured recall.
Core Research
Alongside products, we maintain theoretical and experimental work on intelligent systems.
Neural Ontology
Evaluated with EEG, mouse-neuron, and cognitive-task data.
Experimental batteries
Results recorded in internal and external tests where appropriate.
KAIROS Framework
Conceptual framework for organizing hypotheses about intelligent systems.
“We combine practical tools with documented theoretical research.”