GraphRAG: context first, built on trust
GraphRAG is table stakes. Trusted context is the moat.
A knowledge graph makes retrieval smarter — but production AI also needs meaning, time, trust, and provenance, with every answer traceable to source. Arango delivers it on one engine — graph, vector, document, and search unified — not a graph database bolted to a vector store with glue code.
Value Drivers
What Arango brings to GraphRAG
01
More accurate, explainable answers
Graph-grounded retrieval connects concepts across documents and returns answers with traceable lineage — not nearest-chunk guesses.
Fewer hallucinations · cited answers
02
Build the graph automatically
AutoGraph Studio discovers your knowledge domains and builds the graph and ontology — no Cypher modeling, no hand-built extraction pipelines.
Reported 3–10× dev productivity
03
Cost-optimized at scale
The RAG Strategizer in AutoGraph Studio matches processing depth to content — full graph extraction only where it pays off — avoiding the entity-extraction cost cliff.
Lower indexing & LLM spend
04
One engine, no Frankenstack
Graph, vector, document, and search in a single engine — no bolt-on vector store, no sync layer, no two query languages.
Simpler architecture · lower TCO
3–10× developer-productivity figure is reported by Arango; indexing and accuracy gains vary by corpus and configuration.
Customer Outcomes
Proven GraphRAG in production
Teams building enterprise GraphRAG run it on Arango. NVIDIA — triaging millions of engineering issues — on GraphRAG for 50× faster ingest and triage. PSI’s GraphRAG knowledge engine cut clinical-trial site identification from weeks to minutes, with explainable, source-grounded recommendations.
50×
faster triage — NVIDIA NVBugs
Weeks → minutes
PSI site identification
Strong Performer
Forrester Wave™ MMDP, Q2 2026
~200
enterprises build on Arango
The takeaway: GraphRAG works best on a foundation that unifies graph, vector, and documents — and builds the knowledge graph for you. That’s the difference between a pilot and production.
Technology Differentiators
More than GraphRAG — an adaptive RAG engine
Adaptive RAG strategy
RAG Strategizer (AutoGraph Studio)
VectorRAG
GraphRAG
Picks the right method per domain automatically — and you can review and edit each assignment before the build — never over- or under-processing a corpus.
Two search modes
AutoGraph Studio
Instant
Deep Search
Each question is routed to the right domain partition. Instant Search answers in a single pass; Deep Search plans multi-hop retrieval. Every answer shows its citations.
Graph intelligence, built in
GraphML + Graph Analytics
Link prediction
Embeddings
Community detection
PageRank
Picks the right method per domain automatically — never over- or under-processing a corpus.
The Difference
Most GraphRAG tools give you one retrieval mode and a pile of glue. Arango gives you a retrieval-strategy engine on a single multimodel foundation.
How it Works
Documents in, trusted answers out — automatically
01
Ingest
The Importer turns documents (PDF, Office, text) into a knowledge graph — entity and relationship extraction built in.
02
Auto-build
AutoGraph Studio discovers knowledge domains, and its RAG Strategizer assigns GraphRAG or VectorRAG to each one. Review the plan, then build the context graph.
03
Retrieve
AutoGraph Studio answers with Instant Search (single pass: semantic, lexical and graph expansion) or Deep Search (multi-hop, LLM-planned), with citations back to the source.
04
Ground & serve
Source-cited answers to agents and copilots via Ada, AQLizer, and MCP — on one contextual data layer.
Relationships ≠ business context.
“Graphs are exceptional at modeling relationships — but relationships alone aren’t business context. Production AI needs shared meaning, time, trust, and state.” — Ravi Marwaha, COO & CTPO, Arango
The Matrix
From GraphRAG challenge to measurable value
| GraphRAG challenge | Arango Contextual Data Platform | Value · KPI / OKR |
|---|---|---|
| Vector-only RAG returns lookalike chunks; misses how concepts connect | GraphRAG adds cross-document relationship intelligence — beyond traditional RAG | Higher accuracy · fewer hallucinations |
| Graph-only DBs need a bolt-on vector store, a sync layer, and two query languages | ArangoDB unifies graph + vector + document + search in one engine (one query layer, AQL) | No Frankenstack · lower latency & TCO |
| Building the knowledge graph by hand is slow and skills-heavy | AutoGraph Studio auto-discovers domains and builds the graph + ontology | Faster prototype-to-production |
| Entity extraction on every chunk is expensive — the GraphRAG cost cliff | The RAG Strategizer in AutoGraph Studio assigns GraphRAG or VectorRAG per domain | Lower indexing & LLM cost |
| Answers you can’t trust or trace | Explainable outputs with traceable lineage; every answer shows citations, the graph path and, for Deep Search, a trace | Auditable, governed AI |
| Pilots that never reach production scale | Production-grade, horizontal scaling to millions of docs; reusable context layer, kept current with incremental graph updates | Compounding value, not rebuilds |
The Shift
Before and after Arango
Before
The DIY GraphRAG Stack
After
Arango GraphRAG
Outcome
More accurate answers
Lower cost
Weeks of modeling → automated
Pilots that reach production
Evaluating an Alternative?
Arango vs Neo4j — for enterprise GraphRAG
Arango replaces the graph DB + vector index + pipeline glue with one engine that builds and serves GraphRAG for you — and runs on-prem or air-gapped, not just in someone else’s cloud.
Accurate, explainable, production-ready.
Bring your document set and we’ll build a GraphRAG knowledge graph automatically — with answers grounded in real context and traceable to source.