GraphRAG — Where Knowledge Graphs Meet AI
Most AI tools answer questions from documents. We answer them from a knowledge graph — millions of structured entities and relationships that give every answer context, provenance, and depth. The result: intelligence that reasons across connections, not just keywords.
What is GraphRAG?
GraphRAG (Graph Retrieval-Augmented Generation) extends classical RAG with structured graph traversal. Instead of retrieving isolated text chunks, our AI navigates a Neo4j knowledge graph — following ownership chains, board interlocks, and event timelines to assemble context-aware answers grounded in real entity relationships.
When you ask “Who controls Bergtal Holding AG?”, classical RAG searches text. GraphRAG walks the actual ownership graph — through holding companies, beneficial owners, and cross-border structures — returning a verifiable answer with every hop linked to its primary source.
The result is reasoning that scales beyond keyword matching: multi-hop questions, network analysis, and historical research that classical RAG cannot answer accurately.
Classical RAG
Retrieves text chunks. Limited to keyword similarity. Cannot follow relationships.
Vector RAG
Semantic search via embeddings. Better than keywords but still flat — no structured reasoning.
GraphRAG (Ours)
Multi-hop traversal across structured entities. Reasons through ownership, board, and event networks.
Five Architectural Pillars
Every layer is engineered for accuracy, provenance, and scale. From OCR ingestion to graph traversal, each component is independently observable and replaceable.
GraphRAG Engine
Retrieval-Augmented Generation with graph traversal. Multi-hop reasoning across entity relationships, ownership chains, and temporal events.
Neo4j Knowledge Graph
Millions of nodes (companies, people, addresses, events) with hundreds of millions of typed relationships. Native graph database for sub-second traversal.
Proprietary OCR & NER
Custom-trained OCR processes 140+ years of multilingual gazette archives. LLM-based NER extracts entities across German, French, Italian, English.
Real-Time Ingestion
Continuous pipeline from official registries with sub-24-hour latency. Every new SHAB publication, formation, or mutation indexed automatically.
Multi-Source Enrichment
Composable enrichment layers — Creditreform credit data, internet search, Intelligence X OSINT. Plugged in per use case.
Specialised AI Agents
Industry-specific agents trained on workflow data. KYC, due diligence, market intelligence, equity research — each with its own retrieval strategy and prompt architecture.
Anatomy of a GraphRAG Query
When you ask Swiss Graph a question, here's what happens behind the scenes — every step traceable, every result auditable.
01
Intent Parsing
LLM classifies the query, extracts entities, and selects the appropriate AI agent.
02
Graph Retrieval
Cypher queries traverse Neo4j to find relevant entities and their N-hop neighbours.
03
Context Assembly
Structured graph data + unstructured gazette text + enrichment layers merged into prompt.
04
AI Reasoning
Specialised agent reasons over assembled context with chain-of-thought.
05
Provenance
Every claim linked to source nodes — original SHAB images, registry entries, or external feeds.
The Knowledge Graph in Action
A small slice of the Swiss Graph — companies, people, addresses, and the relationships between them. Drag, expand, and explore. The full graph contains millions of entities.
Data at Scale
Scale without sacrificing depth. Every entity in the graph carries its full historical and relational context.
750K+
Swiss Companies
Active + dissolved since 1883
8M+
Decision Makers
Board members, directors, owners
156K+
Global Securities
96 exchanges, real-time
140+
Years of History
SHAB archives back to 1883
50B+
Data Points
Updated continuously
<24h
Update Latency
New SHAB filings to graph
4
Languages
DE, FR, IT, EN multilingual NER
99.9%
OCR Accuracy
Proprietary engine on gazettes
Why GraphRAG Wins
Multi-Hop Reasoning
Answer questions that require following 2, 3, or more relationships. UBO chains, board interlocks, beneficial ownership across jurisdictions — all natively traversable.
Verifiable Provenance
Every fact links back to its primary source — original gazette page, registry extract, or data feed. No hallucinated facts. No untraceable claims.
Cross-Border Intelligence
Swiss Graph connects to World Graph — so ownership chains, financial flows, and corporate networks span every supported jurisdiction.
Specialised Agents
Industry-specific AI agents with their own retrieval strategies, prompt architectures, and validation rules. Banking ≠ journalism ≠ M&A.
Multilingual Native
NER, OCR, and reasoning across German, French, Italian, and English. No translation layer — agents reason directly in source languages.
Historical Depth
Track corporate evolution across 140+ years. Name changes, restructurings, ownership transfers — the full lifecycle of every Swiss entity.
Security & Deployment
Enterprise-grade infrastructure. Swiss data hosting, on-premise deployment options, and zero-data retention guarantees.
Zero-Data Retention
Your queries and uploaded documents are never stored or used for AI training. Processed in-memory and deleted.
SOC 2 Type II
Audited security controls across all infrastructure, with annual third-party penetration testing.
Swiss Data Hosting
All processing and storage in Swiss data centers under Swiss federal data protection law.
On-Premise Available
Full platform deployable inside your VPC or air-gapped environment for maximum data sovereignty.
API & SSO
RESTful API access with rate limits per plan. SAML/OIDC SSO integration. Webhook event delivery for real-time alerts.
Custom AI Models
Fine-tuned industry-specific LLMs running in isolated environments per enterprise client. No data leakage between tenants.
Ready to See GraphRAG in Action?
Try the platform on real Swiss company data, or talk to our team about custom deployments and data feeds.