Every team that has deployed a RAG-based AI assistant has hit the same wall: the answers start going stale. The product documentation was updated three weeks ago, but the AI still tells users the old pricing. The engineering runbook was migrated to a new Confluence space, but the support bot keeps citing the archived version. The problem is not retrieval — it is freshness. Your knowledge base is only as good as its last sync.
AACFlow's knowledge base connectors solve this with automated, incremental synchronization across the document sources your team already uses.
Why AI Answers Go Stale
When you build a RAG system, you ingest documents, chunk them, embed each chunk, and store the vectors. That process captures a snapshot in time. From that moment on, every edit to the source document creates a divergence between what the AI knows and what is true.
The naive fix — re-index everything on a schedule — is expensive and slow. A Confluence space with thousands of pages takes minutes to fully re-index. More importantly, it wastes compute: 95% of documents have not changed.
The right approach is incremental sync: detect which documents changed since the last run, re-embed only those, and update the vector store without touching unchanged chunks.
AACFlow Knowledge Base Connectors
AACFlow includes native connectors for the most common enterprise document sources:
Notion — syncs databases, pages, and subpages. Tracks to detect changes.
