From Pilot to Production: Rolling Out a Document Assistant
What changes when a document assistant leaves the pilot: corpus scope, refusal thresholds, permissions, hosting choice and the operations work after launch.
What changes when a document assistant leaves the pilot: corpus scope, refusal thresholds, permissions, hosting choice and the operations work after launch.
Break a RAG request into stages - embedding, search, reranking, generation - and see which one actually drives your latency and cost before you tune it.
Stale indexes give confident wrong answers. How to set reindexing frequency by document tier, and what breaks when a sync run falls behind.
A retrieval augmented AI assistant answers new joiners' questions from your own documents, with a visible source for every answer. How to build one.
How to ground support answers in your own help center content, design the first reply, and hand over to a human before retrieval fails the customer.
How to enforce permissions in a RAG document assistant: ingestion, query-time and post-retrieval filtering, and how to model ACLs as chunk metadata.
On-premise RAG keeps documents, index, embeddings and inference inside your boundary. What you lose in model choice, and what you gain in control.
How to keep a retrieval assistant's source documents fresh without hiring a full-time editor: spot rot, use usage data, and share the maintenance load.
Ragable indexes your files and answers from them, with citations. Start on SaaS or run it on your own infrastructure.
Start from $99/month