intelligence made from first principles

A vector database that processes each vector independently, avoiding the costly memory-intensive network construction required by methods like HNSW. Built with proprietary hash indexing and native SQL semantics for millisecond recall.

• Significantly lower cost than traditional vector databases
• Per-vector indexing without expensive network build overhead
• Hash-native indexing with predictable latency

Omnilingual embedding models that collapse language barriers; if knowledge matches, the vectors align—regardless of how it is written or spoken.

• Unified latent space for 200+ languages
• Knowledge-first similarity, not surface tokens
• Drop-in adapters for existing inference stacks

Continuous explainability tooling to monitor, interrogate, and control models while they learn—before drift or anomalies ship.

• Training-time governance dashboards
• Real-time constraint enforcement and alerts
• Regulatory-grade audit exports by default