Pilot for Free/Verify Governance/Standalone Flexibility
Graphixa.ai is available within the AIMLUX.ai/EQUITUS.ai ecosystem. By offering it as an Open Source or Free Trial service-based solution, Equitus provides a low-barrier entry point for organizations to stabilize their data foundation before moving into more advanced AI/ML territories.
"Service-First" view of Graphixa.ai:
Graphixa.ai: The Governance & Migration Service
Unlike the heavy-duty KGNN platform, Graphixa is the Operational Gateway. It is designed to be the "first responder" in a migration, ensuring that the movement of data is governed, traceable, and concept-aware.
|
Feature |
Graphixa.ai (Standalone Service) |
Standard Industry Tools |
|
Delivery
Model |
Open
Source / Free Trial / Service-Based |
Proprietary
/ Licensed / Enterprise Only |
|
Logic |
Semantic
ETL: Rules based on business meaning (Ontology). |
Technical
ETL: Rules based on table structures (Metadata). |
|
Traceability |
Built-in Provenance
& Lineage for every record. |
Often
a separate, expensive add-on. |
|
Scope |
Operational
data movement & migration governance. |
General
purpose data movement. |
The 3 Pillars of a Modern Migration
To successfully migrate from legacy to cloud, you need all three components working in their specific lanes:
Pillar 1: Schema Conversion Tools (The Mechanical Shell)
Role: The "Construction Crew."
Action: Translates DDL (Data Definition Language) and SQL syntax from one dialect to another.
Limitation: It is a "dumb" move. It creates the tables but has no idea why the data exists or how it relates to business goals.
Pillar 2: Graphixa.ai (The Semantic Orchestrator)
Role: The "Site Supervisor & Quality Control."
Action: Maps the actual data into the shell using semantic rules. It ensures that "Field X" in the legacy system correctly populates the "Customer ID" concept in the cloud.
Governance: It provides the lineage (where it came from) and provenance (who touched it) that schema tools lack.
Pillar 3: Human Experts (The Strategic Architects)
Role: The "Designers & Engineers."
Action: Since Graphixa is rule-based and not "magic," humans are required to redesign complex logic (like moving old PL/SQL triggers into modern SQLScript).
Decisions: Humans make the final call on performance tuning and when the system is stable enough for the final "Cutover."
Why the "Open Source / Free Trial" Model Matters
Graphixa.ai is available as an open-source or trial-based tool, it allows teams to:
Pilot for Free: Test the semantic mapping on a small dataset (like the checklist we discussed).
Verify Governance: Prove to auditors that they have a rule-based provenance trail before spending a dollar on licensing.
Standalone Flexibility: Use it as a dedicated migration tool even if they aren't ready for a full Knowledge Graph (KGNN) implementation.
Statement of Work (SOW) outline is designed to formally separate the "Mechanical," "Semantic," and "Strategic" workstreams. By defining these boundaries, you protect the project from scope creep and ensure that the Graphixa.ai deterministic rules are not confused with the manual redesign efforts of the human team.
SOW Outline: Operational Semantic Migration & Governance:
1. Project Overview & Objectives
Objective: Execute a governed migration of [Dataset Name] from [Source System] to [Target Cloud Platform].
Core Approach: Utilization of a three-tiered execution model consisting of mechanical conversion tools, the Graphixa.ai semantic orchestrator, and human subject matter expertise.
2. Scope of Work & Responsibilities
Tier A: Mechanical Infrastructure (Schema Conversion Tools)
Provider: [Internal IT / Cloud Vendor Tools]
Responsibilities:
Execution of automated DDL (Data Definition Language) translation.
Basic SQL syntax conversion (e.g., Oracle-to-Snowflake dialect).
Boundary: These tools are strictly for "shell" creation; they possess no business context, no lineage tracking, and no data validation capabilities.
Tier B: Semantic Orchestration (Graphixa.ai Service)
Provider: AIMLUX/EQUITUS (Open Source / Service Instance)
Responsibilities:
Semantic Typing: Establishing the Ontology Reference Model (Defining concepts like
customer_id,revenue_amount).Bidirectional Mapping: Mapping source fields and target columns to the central semantic layer.
Deterministic Transformation: Executing rule-based data type conversions and generating SQL Upserts/Bulk Loads.
Governance: Capturing automated Provenance & Lineage events for every record moved.
Operational Feedback: Routing failed records back into the workflow for human intervention.
Boundary: Graphixa is not an AI that guesses logic; it is a rule-based engine. It does not perform procedural code redesign (PL/SQL to SQLScript).
Tier C: Strategic Architecture (Human Experts)
Provider: [Your Consulting Team]
Responsibilities:
Logic Redesign: Manually rewriting complex procedural logic (PL/SQL, stored procedures, triggers) into modern cloud-native formats.
Performance Tuning: Optimizing the Graphixa-generated SQL for cost and speed in the target environment.
Functional Validation: Verifying that the migrated data meets business-level requirements.
Cutover Management: Making final risk-based decisions on data integrity and production readiness.
Boundary: Experts act as the "exception handlers" for Graphixa; they resolve what the deterministic rules cannot.
3. Deliverables: Tangible MIlestones for efficient Project Management
|
Deliverable |
Ownership |
|
Converted Database Schema (Shell) |
Schema Conversion Tool |
|
Enterprise
Business Ontology |
Human Experts (in
Graphixa) |
|
Semantic Mapping Documentation |
Graphixa.ai |
|
Migrated Data
& SQL Upsert Logs |
Graphixa.ai |
|
End-to-End Lineage & Audit Report |
Graphixa.ai |
|
Refactored
Procedural Code |
Human Experts |
|
Post-Migration Validation Report |
Human Experts |
4. Project Milestones
M1: Infrastructure Ready: Completion of mechanical schema conversion.
M2: Semantic Blueprint: Finalization of the Ontology in Graphixa.ai.
M3: Pilot Load: Completion of a 5% data subset load with verified lineage.
M4: Operational Loop: Full batch migration with 100% error resolution in the feedback loop.
M5: Governance Sign-off: Final audit of provenance reports.
5. Out of Scope: Keeps human experts in the design/deploy/decision loop:
Automated "Black Box" AI mapping (All mapping is deterministic and human-verified).
Automatic rewriting of application-level logic (External to the database).
Hardware procurement or cloud infrastructure provisioning.
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