Cold Start Indexing

Cold start indexing is the operation of building a vector index from scratch — ingesting documents, generating embeddings, and constructing the search structure — typically performed when a new RAG deployment goes live or after a fundamental change to the embedding model or schema. Cold start time can be substantial: indexing a million chunks at typical embedding model throughput takes hours, and the index build (HNSW, IVF, DiskANN) adds more hours. Operators design around cold start through several techniques: dual indexes (build new index while serving from old), batch embedding generation (parallelize across many workers), and incremental ingestion (start serving from a small subset while continuing to ingest). AI governance teams document cold start procedures and rehearsal frequency in their AI compliance playbooks because cold start is also the recovery procedure after data loss, corruption, or major schema changes. The cost of cold start drives architecture decisions including model choice, dimension choice, and quantization strategy.

Cold start coordination through Centralpoint: Centralpoint orchestrates cold start indexing and migration across whatever vector backend you operate, with dual-index cutover to maintain chatbot availability. The model-agnostic platform meters tokens, keeps prompts local, and deploys chatbots through one line of JavaScript across portals with full audit logs.


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