Yelp explains how it turned a two-week prototype into a scalable, production-ready AI assistant for business pages. They built near-real-time indices for reviews, photos, and structured data in an EAV schema, combined keyword-first retrieval with LLM prompts, and added query classification and trust-and-safety filters. The system streams answers with citations, logs metrics, and balances freshness, performance, and reliability.

• Yelp built its AI business-page assistant from two-week prototype to production in nine months by swapping Redis warehouse snapshots for streaming ingestion, keeping reviews, photos, and core business data fresh within 10 minutes (menus/community posts still update weekly).
• An EAV schema in Cassandra lets them add new structured fields on the fly without schema migrations.
• Combining keyword-first retrieval with LLM-based query expansion hit quality targets faster than pure embedding-based search.
• Batching only the slow-moving data sources (rather than everything) kept the real-time streaming pipeline manageable.