AI Engine Utility

Vector Search.

MeltyBase Vector is the first AI-native database built on hardened PostgreSQL. Combine relational data with high-dimensional embeddings for complex RAG workflows and semantic search at scale.

<10ms

Search Latency

HNSW

Index Support

BYOK

Embedding Logic

01. HNSW Indexing

High-performance Hierarchical Navigable Small World (HNSW) indexes for sub-10ms similarity lookups. Built natively into the Sovereign Postgres engine via pgvector.

02. Auto-Embedding Pipelines

Automatically generate and store embeddings as data is inserted into your tables. MeltyBase handles the transformation using local or remote model providers.

03. Retrieval-Augmented Intelligence

Combine traditional relational queries with semantic search. Use SQL to find the "closest" data points to any given context, enabling long-term memory for AI agents.

                    SELECT title, content FROM documents

                    ORDER BY embedding <=> '[0.1, 0.2, ...]'

                    LIMIT 5;

04. Sovereign Speed

By keeping vectors in the same engine as your relational data, MeltyBase eliminates the "data silo" problem. No more syncing between a database and a standalone vector store.

Zero-Copy Retrieval

Retrieve raw relational data and semantic vectors in a single database transaction.

Metadata Filtering

Filter by any relational column (e.g., tenant_id) with absolute performance isolation.