Map your knowledge
like the stars.
Startrag turns flat document chunks into a navigable constellation of meaning. Graph-aware retrieval that finds the right context, boosts accuracy, and cuts hallucinations.
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Drag to compare retrieval strategies
Traditional RAG retrieves isolated chunks by raw vector distance. Startrag walks a knowledge graph of clustered stars — so answers stay grounded, complete, and connected.
Answer accuracy
86.1%
Hallucination rate
7.1%
Context recall
82%
Retrieval that understands how knowledge connects
Startrag rebuilds your knowledge base as a living star chart, so your models retrieve meaning — not just nearby vectors.
Chunks become stars
Every document slice is embedded and placed as a star, sized by importance and colored by topic — never a flat list again.
Vector clusters as constellations
Semantically related stars are linked into constellations, capturing the relationships traditional cosine distance ignores.
Graph-walk retrieval
Queries traverse the star map, pulling complete neighborhoods of context instead of disconnected fragments.
Grounded by design
Every answer carries citations back to its source stars, so you can audit exactly where the model looked.
Sub-100ms recall
A purpose-built graph index returns rich, connected context in milliseconds, even across millions of stars.
Drop-in compatible
Swap your existing vector store with two lines of code. Works with any model, framework, or embedding.
From raw documents to a navigable galaxy
Ingest & slice
Point Startrag at your docs, wikis, and databases. We chunk and embed every source into stars.
Cluster & link
Stars are clustered into constellations and connected by semantic edges to form your knowledge graph.
Traverse & ground
At query time, Startrag walks the graph to assemble complete, cited context for your model.
Answer with confidence
Your LLM responds with grounded, traceable answers — and measurably fewer hallucinations.
98.4%
Answer accuracy
−91%
Fewer hallucinations
<100ms
Median retrieval
12M+
Stars indexed / cluster