When we first built RAG at Twio, pgvector was the obvious pick. Our business data was already in PostgreSQL, and dropping embeddings into the same database was the fastest path to a working product.
For the first version, that was right. As we scaled, the problem stopped being "how do we store vectors?" and became "how do we reliably understand thousands of broker documents, emails, and attachments in production?" That changed the answer. Today, Vertex AI Search is our main retrieval layer.
RAG is Twio's memory layer, not a search feature
Twio is an AI SaaS for loan brokers. A single client case is a mess of fragmented information:
email threads
