The missing relationship layer
for enterprise data.
Data teams already have catalogs, warehouses, ETL, and BI. But most systems still do not know how tables truly connect across real business data.
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Manual joins do not scale
Data engineers spend too much time discovering join keys, validating mappings, and explaining relationships.
⚠️
LLMs guess relationships
Without trusted relationship context, AI tools can generate wrong SQL even when metadata looks correct.
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Data context is fragmented
Relationships across databases, tables, fields, and systems are rarely organized as reusable intelligence.
How Arisyn IntaLink discovers relationships.
Arisyn IntaLink analyzes real data values, metadata, statistics, and structural signals to build an explainable relationship graph for enterprise structured data.
Value overlap
Inclusion ratio
Field statistics
Metadata context
Cross-source mapping
1
Connect data sources
Register databases, warehouses, lakes, or structured data systems.
2
Extract metadata and statistics
Analyze tables, fields, data types, distinct counts, null rates, and value distributions.
3
Compare real data values
Detect inclusion, overlap, co-occurrence, and candidate keys across fields.
4
Build relationship graph
Generate explainable table and field relationships with confidence evidence.
5
Expose trusted context
Provide relationship paths through UI, API, MCP, SDK, or downstream integrations.
Core capabilities
Arisyn IntaLink turns hidden structural relationships into reusable, explainable, and machine-callable data intelligence.
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Relationship Discovery
Automatically discover table-level and field-level relationships from real data values.
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Join Path Generation
Find trusted paths between datasets, even when relationships span multiple intermediate tables.
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Cross-Source Mapping
Analyze relationships across heterogeneous sources, databases, and enterprise systems.
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Metadata Foundation
Build a relationship-aware metadata foundation for data governance and semantic systems.
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Relationship Discovery
Automatically discover table-level and field-level relationships from real data values.
🤖
Join Path Generation
Find trusted paths between datasets, even when relationships span multiple intermediate tables.
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Cross-Source Mapping
Analyze relationships across heterogeneous sources, databases, and enterprise systems.
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Metadata Foundation
Build a relationship-aware metadata foundation for data governance and semantic systems.
Different from ETL, catalogs, and ordinary lineage tools.
Arisyn IntaLink does not simply move data, list metadata, or visualize existing pipelines. It discovers relationships that are often undocumented.
Not ETL
✕ Not just data movement
✕ Not pipeline orchestration
✓ Relationship discovery before integration
Not a basic catalog
✕ Not just table descriptions
✕ Not only ownership and tags
✓ Relationship-aware metadata intelligence
Not schema guessing
✕ Not based only on field names
✕ Not manually maintained joins
✓ Evidence from real data values
GET /api/relationships?source=orders&target=customers
{
"join_path": ["orders.customer_id", "customers.customer_id"],
"confidence": 0.96,
"evidence": ["value_overlap", "inclusion_ratio"],
"explainable": true
}
Built to be data integrated.
Use Arisyn IntaLink as a relationship intelligence service inside AI applications, semantic layers, data platforms, BI tools, governance workflows, and enterprise automation.
REST API
MCP Server
SDK
BI Tools
AI Agents
Semantic Layers
Data Governance
NL2SQL
What teams build with Arisyn IntaLink
A relationship intelligence layer improves downstream systems that depend on correct joins, trusted context, and explainable data paths.
Trusted NL2SQL
Give LLMs verified relationship paths so generated SQL is less likely to hallucinate joins.
Trusted NL2SQL
Give LLMs verified relationship paths so generated SQL is less likely to hallucinate joins.
Lineage and impact analysis
Understand how upstream and downstream assets are connected through discovered relationships.
Master data and entity discovery
Identify shared keys, candidate entities, and cross-system relationship patterns.
Semantic layer enrichment
Strengthen semantic models with real data
relationship evidence rather than manual assumptions.
Master data and entity discovery
Let agents query, connect, and reason over structured data with relationship-aware context.
Arisyn IntaLink powers Arisyn Semora’s trusted semantic engine.
Arisyn turns business questions into governed answers. Arisyn IntaLink provides the data relationship context that helps Arisyn choose trusted data paths instead of guessing joins.
Arisyn understands business intent
Question, metric, dimension, time range, and context.
Intalink provides relationship paths
Verified table and field relationships from real data analysis.
Trusted answer is generated
SQL, result, lineage, and explanation are returned together.
Enterprise-ready relationship
intelligence.
Designed for controlled, scalable, and auditable data environments.
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Access control
Support role-aware data access, multi-tenant isolation, and governed relationship visibility.
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Task and monitoring
Run discovery jobs, monitor progress, inspect logs, and validate results at platform scale.
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Explainable evidence
Each relationship can be traced to data evidence, statistics, and confidence signals.
Build a trusted relationship
layer for enterprise data.
Discover relationships automatically. Expose trusted join paths. Power AI, BI, governance, and semantic applications.
Cloud Native Architecture
Built for AWS, Azure, and GPT
High Performance
Optimized for large-scale relational scans
Enterprise Security
Open Integration
SOC2, SSO, and encrypted metadata
SDK & API for embedding Arisyn-Intaink capabilities
Seamless integrations with
modern data tools
Arisyn Intalink connects directly to your existing data sources and analytics tools — no code, no friction.