Cosinia / AI Memory Engine
Your AI deserves better memory
Cosinia is a high-performance vector database built in Rust, engineered to care for embeddings from ingestion through query. It emphasizes proper vector preparation, structural consistency, and long-term protection—so similarity remains meaningful as systems scale. With Vector Laboratory™ and Vector Shield™, Cosinia provides the tools to normalize vectors correctly and defend them against drift, degradation, and misuse over time.
The problem
Every AI rests on a quiet but critical layer: its vectors—the compressed memory of what it has seen, read, and learned. If those vectors aren’t properly normalized, whitened, and structurally balanced, that memory degrades. Signals blur, noise accumulates, and retrieval becomes unreliable.
Most vector databases behave like file cabinets, not memory systems. They store vectors but provide no safeguards against drift, decay, or misuse. Over time, similarity weakens—not because your AI failed, but because its memory was never cared for.
The Cosinia approach
Cosinia treats vectors as a living system. Before ingestion, embeddings are refined and evaluated through the Vector Laboratory™. Once stored, their long-term integrity is protected by Vector Shield™, which focuses on defending similarity against drift, degradation, and adversarial abuse.
The result is a vector memory that remains balanced, consistent, and retrieval-ready—so your AI stays sharp as it scales.
Vector Laboratory™
Experiment before storage. Compare normalization presets, evaluate clustering strategies, tune whitening and dimensional conditioning, and surface noise and outliers with clear diagnostics. When you reach the highest Cosinia Score, generate a ready-to-use SDK recipe to ingest vectors into Cosinia’s Rust-built, low-latency database.
Normalization
Align vector scale & energy
Stable representations.
Clustering
Group semantic neighborhoods
(K-means, HDBSCAN)
Whitening
Remove statistical bias
Cleaner retrieval signals.
Outlier Scoring
Expose weak points
Stronger semantic memory.
Vector Shield™
Coming soonVector Shield focuses on long-term protection of vector memory—detecting drift, identifying poisoned or adversarial embeddings, and preventing degradation before it impacts retrieval quality.
We show the roadmap without pretending it’s finished. You get transparency, not theater.
Your model is only as strong as its memory. Let’s make that memory extraordinary.