ArcXA SQL Guardrails govern from the code

ArcXA directly addresses the Primary challenges in SQL migrations to reduce cost and risk. Start the On-Boarding process and estimate your savings.

ArcXA SQL Guardrails: Govern from the code to the prompt reducing "hallucinations" with triple store rdf tracing.

Equitus.ai’s ARCXA is an open-source mapping intelligence framework designed for data migration, lineage tracking, and enterprise semantics. In the context of Text-to-SQL generation and reducing large language model (LLM) hallucinations, ARCXA acts as a rigid semantic guardrail and deterministic source of truth.

LLMs generate SQL queries from natural language,  frequently hallucinating non-existent columns, misuse foreign keys, or generate structurally invalid queries. ARCXA mitigates these issues by providing structural grounding and deterministic validation.

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1. Semantic Grounding via Knowledge Graphs

Rather than feeding an LLM a raw, confusing database schema (DDL), ARCXA translates enterprise database structures into a clear semantic model.

• Ontology Mapping: ARCXA utilizes an RDF and SPARQL data plane (arcxa-shard) to store data relationships as a graph.

• Eliminating Guesswork: By mapping the relational database structure to a clear, semantic ontology, the LLM no longer has to "guess" how tables join or what an abbreviated column name means. This direct grounding limits the LLM's creative license, significantly dropping table and column hallucinations.

2. Policy-Driven Validation Layers

A primary cause of AI hallucinations in SQL generation is the lack of real-time constraint verification.

• Strict Guardrails: ARCXA applies policy-driven validation directly within its workflow orchestration plane.

• Pre-Execution Checks: When an LLM generates an intentional query log or script, ARCXA cross-checks the output against its active data governance policies. If the query attempts to call a restricted or non-existent schema element, the system flags the error immediately before execution.

3. Comprehensive Lineage and Traceability

ARCXA tracks complete transformation traceability: Halting hallucinations requires understanding why an AI model made an error. 

• Audit Logs: It logs exactly which ontology terms were applied and which database schemas were accessed.

• Deterministic Baseline: By generating SQL query logs alongside ARCXA's immutable lineage records, data engineers can easily pinpoint where an LLM shifted from a verifiable schema path into a hallucinated logic loop.

4. Model-Assisted Semantic Matching

Arcxa-model-service, platform integrates local model inference to handle semantic matching.

• Intent Alignment: When a user poses a question, ARCXA uses semantic matching to tie the natural language intent directly to verified, registered data sources and components.

• Contextual Injection: This allows the system to feed highly focused, correct contextual prompts to the text-to-SQL model, ensuring it only builds queries using safe, existing parameters.

Challenge in Text-to-SQL	ArcXA Capability	Impact on Hallucinations
Hallucinated columns/tables	Graph-based semantic ontology	Forces strict adherence to real schemas
Incorrect or broken table joins	Lineage and relationship mapping	Provides explicit join logic paths
Unsafe or destructive queries	Policy-driven validation plane	Flags and stops invalid SQL before execution