Discover Trusted Data Relationships Before AI or BI Guess the Wrong Join
Arisyn IntaLink automatically analyzes metadata, statistics, and real data patterns to discover explainable table and field relationships — helping AI agents, BI tools, semantic layers, and data engineers use trusted join paths instead of manual mapping or schema guessing.
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.
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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.
What IntaLink produces
Arisyn IntaLink turns hidden structural relationships into reusable, explainable, and machine-callable data intelligence.
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Relationship Graph
A table-level and field-level graph that shows how enterprise datasets are connected.
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Join Path Recommendations
Trusted paths between tables, including intermediate bridge tables when direct joins do not exist.
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Evidence and Confidence
Each relationship includes confidence scores and evidence such as value overlap, inclusion ratio, uniqueness, null rate, and metadata signals.
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Relationship APIs
Expose discovered relationships to AI agents, BI tools, semantic layers, data catalogs, and governance workflows through API, MCP, or SDK.
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",
"key_uniqueness"
],
"explainable": true,
"recommended_for": [
"nl2sql",
"semantic_layer",
"data_integration"
]
}
Built to be data integrated.
Use Arisyn IntaLink as a relationship context service for AI agents, semantic layers, BI tools, and data engineering workflows.
Connect IntaLink to Your Existing Data Stack
REST API
MCP Server
SDK
BI Tools
AI Agents
Semantic Layers
BigQuery
Looker
Tableau
Snowflake
Databricks
PostgreSQL
SQL Server
Oracle
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.
BI and Semantic Layer Enrichment
Strengthen metrics, dimensions, and semantic models with evidence-backed data relationships.
Data Catalog Enhancement
Add discovered relationships, candidate keys, and join paths to existing metadata catalogs.
Data Integration Planning
Identify how datasets can be joined before building pipelines or analytical datasets.
Master Data and Entity Discovery
Find shared identifiers, candidate entities, and cross-system relationship patterns.
Governance and Impact Analysis
Understand how tables, fields, and systems are connected through discovered relationship paths.
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.
Arisyn 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.
Built for governed enterprise data environments
Designed for controlled, scalable, and auditable data environments.
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Private deployment options
Run Arisyn IntaLink in controlled cloud or enterprise environments.
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Role-based relationship visibility
Control which users and systems can view discovered relationships.
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Auditable discovery jobs
Track profiling jobs, relationship changes, confidence updates, and validation actions.
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Explainable evidence
Every relationship can be traced back to statistical and data-pattern evidence.
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No black-box join guessing
Relationships are generated with confidence, evidence, and review workflows.
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 GCP
High Performance
Optimized for large-scale relational scans
Enterprise Security
SOC2, SSO, and encrypted metadata
