"Why Build IT Yourself?"
Hybrid-AI Semantic Control Plane
Equitus Arcxa; Hybrid-AI semantic control plane (SCP) that turns enterprise SQL data complexity into governed, economically measurable AI outcomes.
Arcxa turns SQL complexity into reusable business meaning:
Large enterprises commonly carry years of accumulated SQL logic, undocumented transformations, duplicate definitions, and interdependent legacy systems. Arcxa’s SCP proposes to map schemas, transformations, policies, and dependencies into a semantic graph so that business concepts—such as “customer,” “net revenue,” or “regulated personal data”—are defined consistently across systems.
Arcxa's practical effect is that teams can move from asking, “Which script or table contains the answer?” to “Which governed definition and approved data product should this AI workflow use?” Its proposed platform capabilities include ontology-aware semantic mapping, multi-source normalization, and row-, column-, workflow-, and graph-level lineage.
Arcxa Assists: databases, ETL platforms, catalogs, security products, or AI models, by connecting them through a reusable layer of business meaning, lineage, policy, and decision controls.
Enterprise SQL Migration teams can enjoy compelling cost, speed and security benefits by using Arcxa Migration Engineering: Contact us to discuss how automate complex migrations
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ARCXA Migration Engineering (AME); is a consulting service designed for Enterprise SQL Migrations can be complex, expensive and tedious; Arcxa provides data unification and mapping intelligence by decoupling operational storage from enterprise business logic.
AME acts as an abstraction and governance layer positioned between legacy IT infrastructure, cloud endpoints, and LLM/agentic workloads by building a hybrid-AI semantic control plane, try Arcxa is open source and free to use on Github / docker.
https://github.com/equitusai/arcxa
1. Core Architecture: The Hybrid-AI Semantic Control Plane
Instead of forcing data migrations into monolithic pipelines or exposing raw schemas directly to AI models.
ARCXA constructs an ontology-driven graph layer by automating the mapping process with Subject-Predicate-Object (SPO) generating a Semantic Control Plane.
Hybrid-AI Engine: Combines deterministic knowledge graphs (R2RML mappings, SPARQL query planes, and RDF shards) with model-assisted inference (local embedding services, ONNX execution, and semantic matching).
Decoupled Control & Data Planes: ARCXA separates control mechanisms (
arcxa-coordinatorfor workflows, semantic governance, and approvals) from execution shards (arcxa-shardfor distributed graph storage).Ontology Alignment: Maps source-native fields directly to high-level enterprise concepts, enabling AI systems to operate on meaning rather than raw column names.
2. Enterprise Value Drivers
Accelerated & Risk-Reduced Data Migration
Data migrations typically suffer from lost business context, unmapped schema variations, and untracked downstream dependencies.
Automated Schema Mapping: Uses embedded semantic matching to align legacy relational schemas with target ontologies automatically.
Traceable Lineage & Auditability: Captures row-, column-, workflow-, and schema-evolution-level lineage.
Teams can audit what changed, which transformation touched it, and what policies applied. System-of-Systems (SoS) Validation: Models interface contracts and runs dry-run simulations before cutover to prevent pipeline failures.
Seamless Multi-System Integration
Integrating disparate data sources for LLMs and agentic RAG usually leads to fragile ETL pipelines.
Zero-Movement Unification: Connects structured, unstructured, and real-time streams without duplicating underlying datasets.
Federated & Model-Ready Querying: Translates high-level AI queries into governed, multi-source execution plans, surfacing vectorized, semantically structured context to LLMs.
Ontology-Driven Interoperability: Automatically resolves definition mismatches across different business units (unifying varying definitions of
AccountorRevenue).
Traditional AI runtime guardrails fail because they attempt to catch unsafe model behavior after data has been retrieved.
Compile-Time Policy Enforcement: Security policies bind to semantic objects rather than raw database tables.
Unauthorized Queries Fail (UQF) at compilation before any underlying query is dispatched. Contract-Aware Security: Roles, column masks, and row-level predicates are injected directly into compiled query plans.
Private / On-Prem Deployment: Runs self-contained (including graph shards and local model inference), enabling deployments in air-gapped or heavily regulated enterprise environments.
3. Summary of Enterprise Impact
Turns SQL complexity into reusable business meaning
Large enterprises commonly carry years of accumulated SQL logic, undocumented transformations, duplicate definitions, and interdependent legacy systems. Arcxa’s SCP proposes to map schemas, transformations, policies, and dependencies into a semantic graph so that business concepts—such as “customer,” “net revenue,” or “regulated personal data”—are defined consistently across systems.
AME proposed platform capabilities include ontology-aware semantic mapping, multi-source normalization, and row-, column-, workflow-, and graph-level lineage.
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