The data substrate that enables judgment over AI.
A continuously enriched, self-repairing model of corporate and trade networks comprising three layers – Source Documents, a Knowledge Graph, and an Ontology – all built for MCP-native interaction.
{ "query": "Meridian Trading LLC", "entities_resolved": 4, "jurisdictions": [ "US-DE", "CY", "VG" ], "ownership_depth": 3, "beneficial_owner": "PEP_MATCH", "trade_records": 847, "risk_signals": [ "sanctions_proximity", "ownership_opacity" ], "source_documents": 23, "confidence": 0.96 }
AI is only as real as what it knows.
Every AI system reasons over some representation of the world. Most are built on aggregated, pre-structured data – incomplete and stripped of context. Sayari’s AI reasons over high-value verifiable evidence: comprehensive original documents, resolved commercial structures, expert-assessed risk.
source records
unique companies
entity relationships
trade records
jurisdictions
global sources
Sourced. Structured. Assessed.
From raw records to resolved structures to assessed risk – every AI output anchored to evidence.
Source Documents
Petabytes of primary source material – court records, incorporation filings, patents, bankruptcy notices, beneficial ownership declarations, and bills of lading – in their original multilingual, unstructured form. A semantic embeddings engine vectorizes this corpus for retrieval, providing provenance and superanalyst-like retrieval and persistence of context and connections within and across document corpora.
Knowledge Graph
The model of global commerce: 1.5 billion or more entities across 250+ jurisdictions, with lineage back to 2015. The graph is built on two parallel paths – deterministic (anchored in gold-standard sources) and probabilistic (logic mined from the deterministic layer and continuously refined by self-repair agents). The pre-resolution substrate is preserved alongside the resolved graph, providing grounding for adversarial checks and independent verification of any resolved assertion.
Ontology
The Ontology encodes the commercial ontology – companies, key people, beneficial owners, shipments, trade flows, corporate control chains – and the judgment ontology, built from eleven years of real-world commercial intelligence tradecraft. It formalizes core tradecraft skills, documented failure modes (anchoring, confidence inflation, jurisdiction blindness), and lateral creativity markers that distinguish expert analysts from competent ones.
What the World Model sees that name-matching doesn’t.
Every relevant record type. Not just the easy ones.
Corporate registries
500M+ entities
Trade data
3.8B+ records
Sanctions & denial lists
40+ lists
Beneficial ownership
Ownership chains
Court, legal & adverse
Adverse signals
Procurement & contracting
Government contracts
Global coverage. Weighted toward where it matters.
Primary source data. Not aggregated. Not resold.
What makes the Commercial World Model fundamentally different from aggregated data providers.
Your AI now has access to the world model.
The Sayari World Model MCP exposes all three layers – source documents, resolved structures, and risk intelligence – as 41 callable tools any AI agent can use. Standards-compliant. No custom connectors. One integration.
Sayari Superconductor Evaluation Platform
Sayari Superconductor is zero-resistance judgment infrastructure: it orchestrates the World Model and Ontology across any AI-powered workflow, scoring every output against analyst expertise – not just accuracy. Superconductor is model-agnostic and deploys as an API call or a native sidecar with no rip-and-replace required.
Any (query, response, context) tuple from any model can be submitted for trap detection, tradecraft scoring, and recommended next steps. Every score returns rationale, source attribution, and recommended actions, rated GREEN / AMBER / RED.
SaaS multi-tenant, private cloud, and air-gapped sovereign deployment.
See the Commercial World Model in action.
Request a demo and we’ll show you exactly what the world model returns – on your entities.