A RAG system — Retrieval-Augmented Generation — connects an AI model to your own knowledge. It retrieves the right facts from your documents first, then answers grounded in them — with sources. DonRAG adds a knowledge-graph layer, so it grasps how everything connects, not just keyword similarity.
Grounded & cited — every answer traces back to a real source. No hallucinations.
Graph-aware — understands how documents, people and decisions relate.
Always fresh — re-indexes the instant your knowledge changes.
Less busywork — fewer support tickets, faster onboarding, instant answers.
Support copilots · Internal search · Docs Q&A · Research
NODES: —EDGES: —CLUSTERS: 14STATUS: SYNCED
[ LIVE MODEL ]
Your knowledge base, building in real time.
Every button press ingests a real source. Watch nodes, edges and clusters grow until your RAG knows everything your team knows — then ask it anything and get answers with citations, not guesses.
Press a button to start →
Click any suggestion below, or tap a node in the graph.
› Your RAG is live — drag to explore · tap any node
// Database compiled
Your RAG is live
40 sources indexed · explore the graph
SALES OPS
Question
System answer
[ 01 ]INGEST
Team's corporate memory
Drop in onboarding, regs, notes, transcripts — then ask "how do we do X here?" and get an answer that links back to the exact pages.
[ 04 ]TUNE
Tune it to yourself
From Settings, reshape answer tone and format (Reader prompt), page style (Writer prompt) and the models — no redeploy, no developer.
// What we've built構築済み
YourStart.app
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