Large language models are probabilistic systems.
They don’t “know” relationships — they estimate them.
When generating SQL or linking datasets, they rank possible JOIN paths based on learned patterns. That works surprisingly well in clean demo environments.
It breaks in real enterprise systems.
The Structural Gap AI Cannot Solve Alone
In production databases:
· Foreign keys are missing.
· Naming conventions drift.
· Cross-system dependencies are undocumented.
· Semantics live in the data, not metadata.
When an AI system generates:
SELECT *
FROM Orders o
JOIN Payments p ON o.id = p.order_id;
The query may look correct.
But is that relationship structurally valid?
A probabilistic model cannot prove containment, domain alignment, or referential integrity. It can only predict likelihood.
In analytics, finance, or compliance workflows, “likely correct” is not good enough.
Data Relationships Are Deterministic
At the structural level, relationships are binary:
· One column’s domain contains another’s — or it doesn’t.
· Two distributions are compatible — or they’re not.
· A multi-hop path is structurally valid — or it’s invalid.
Structural integrity cannot be approximated.
This is where Arisyn introduces a different architectural layer.
Arisyn as a Deterministic Constraint Engine
Arisyn does not predict relationships.
It verifies them using statistical field modeling:
· Distinct value cardinality
· Null distribution behavior
· Domain overlap scoring
· Cross-table compatibility checks
From this, it builds a validated relationship graph:
· Tables → nodes
· Verified column links → edges
· Multi-hop paths computed algorithmically
This graph becomes a constraint layer beneath AI systems.
Instead of guessing JOIN paths, AI operates within a structurally verified search space.
Why Constraints Improve AI
There’s a common misconception that constraints weaken AI systems.
In data infrastructure, the opposite is true.
By combining:
· Probabilistic semantic reasoning (AI)
· Deterministic structural verification (Arisyn)
You get:
· Elimination of JOIN hallucinations
· Explainable query generation
· Auditable structural reasoning
· Production-grade reliability
AI handles meaning.
Arisyn enforces structure.
Together, they form a stable architecture.
The Architectural Takeaway
AI will remain probabilistic.
Enterprise data systems cannot.
If you’re building AI-powered analytics, NL2SQL systems, or autonomous data agents, you need a deterministic constraint layer.
Arisyn provides that layer — turning structural truth into infrastructure.
And in high-stakes data environments, deterministic foundations aren’t optional.
They’re required.
Learn more: https://www.arisyn.com
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