Inference with lower latency.
EigenWeights simplifies transformer MLP layers to speed up inference while maintaining capacity.
EigenWeights
MLP layers represent a significant portion of compute in transformers. EigenWeights finds more efficient representations that accelerate inference without retraining.
30% Faster
Significantly reduces inference latency.
Plug & Play
Direct replacement compatible with standard transformer architectures.
No Retraining
Applicable to existing pre-trained models.
Technical demo
Try compression on demo embeddings or upload your own file.
Demo Note: This demo uses EigenDB vector compression technology. The results shown are specific to vector embedding compression. For model weight compression, the principles are similar but applied to different data structures.
Click to analyze 1,000 random embeddings and see compression results
Use Cases
High-frequency APIs
On-premise models
Real-time applications
Measured results
Internal benchmarks with documented methodology.
EigenWeights 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.”