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.
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.
Retrieves text chunks. Limited to keyword similarity. Cannot follow relationships.
Semantic search via embeddings. Better than keywords but still flat — no structured reasoning.
Multi-hop traversal across structured entities. Reasons through ownership, board, and event networks.
Every layer is engineered for accuracy, provenance, and scale. From OCR ingestion to graph traversal, each component is independently observable and replaceable.
Retrieval-Augmented Generation with graph traversal. Multi-hop reasoning across entity relationships, ownership chains, and temporal events.
Millions of nodes (companies, people, addresses, events) with hundreds of millions of typed relationships. Native graph database for sub-second traversal.
Custom-trained OCR processes 140+ years of multilingual gazette archives. LLM-based NER extracts entities across German, French, Italian, English.
Continuous pipeline from official registries with sub-24-hour latency. Every new SHAB publication, formation, or mutation indexed automatically.
Composable enrichment layers — Creditreform credit data, internet search, Intelligence X OSINT. Plugged in per use case.
Industry-specific agents trained on workflow data. KYC, due diligence, market intelligence, equity research — each with its own retrieval strategy and prompt architecture.
When you ask Swiss Graph a question, here's what happens behind the scenes — every step traceable, every result auditable.
01
LLM classifies the query, extracts entities, and selects the appropriate AI agent.
02
Cypher queries traverse Neo4j to find relevant entities and their N-hop neighbours.
03
Structured graph data + unstructured gazette text + enrichment layers merged into prompt.
04
Specialised agent reasons over assembled context with chain-of-thought.
05
Every claim linked to source nodes — original SHAB images, registry entries, or external feeds.
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.
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
Answer questions that require following 2, 3, or more relationships. UBO chains, board interlocks, beneficial ownership across jurisdictions — all natively traversable.
Every fact links back to its primary source — original gazette page, registry extract, or data feed. No hallucinated facts. No untraceable claims.
Swiss Graph connects to World Graph — so ownership chains, financial flows, and corporate networks span every supported jurisdiction.
Industry-specific AI agents with their own retrieval strategies, prompt architectures, and validation rules. Banking ≠ journalism ≠ M&A.
NER, OCR, and reasoning across German, French, Italian, and English. No translation layer — agents reason directly in source languages.
Track corporate evolution across 140+ years. Name changes, restructurings, ownership transfers — the full lifecycle of every Swiss entity.
Enterprise-grade infrastructure. Swiss data hosting, on-premise deployment options, and zero-data retention guarantees.
Your queries and uploaded documents are never stored or used for AI training. Processed in-memory and deleted.
Audited security controls across all infrastructure, with annual third-party penetration testing.
All processing and storage in Swiss data centers under Swiss federal data protection law.
Full platform deployable inside your VPC or air-gapped environment for maximum data sovereignty.
RESTful API access with rate limits per plan. SAML/OIDC SSO integration. Webhook event delivery for real-time alerts.
Fine-tuned industry-specific LLMs running in isolated environments per enterprise client. No data leakage between tenants.
Try the platform on real Swiss company data, or talk to our team about custom deployments and data feeds.