engineering · 1 min read
Keeping RAG Fresh: Incremental Indexing and Change Capture
A stale RAG system is a worse-than-useless RAG system. Here is how Bhogar AI keeps your knowledge base fresh without rebuilding everything every night.
BABhogar AI TeamProduct & Engineering
A RAG system that returns last quarter's pricing or a deprecated policy is worse than one that says "I don't know". Freshness is a first-class quality metric, not a stretch goal.
Why it matters
Full re-indexing is expensive at scale. The right answer is change capture: detect what changed, re-embed only that, and atomically swap chunks in the vector store with no read downtime.
How Bhogar AI approaches it
Bhogar AI offers incremental indexing for every supported source: Google Drive, Notion, Confluence, SharePoint, S3, GitHub, Slack and more. Changes are processed within seconds; the vector store stays consistent under continuous read load.
- CDC-style sync with change detection per source
- Atomic chunk swap with no read downtime
- Configurable sync schedules and on-demand refresh
- Per-source freshness SLA monitoring
- Webhook-driven indexing for real-time sources
What you get
Customers using incremental indexing achieve sub-minute freshness on most sources at a fraction of the cost of nightly full rebuilds.