Open Source / Free Trial: Service-based Solutions (SOW)

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.

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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."

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Why the "Open Source / Free Trial" Model Matters

Graphixa.ai  is available as an open-source or trial-based tool, it allows teams to:

1. Pilot for Free: Test the semantic mapping on a small dataset (like the checklist we discussed).

2. Verify Governance: Prove to auditors that they have a rule-based provenance trail before spending a dollar on licensing.

3. 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.

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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.

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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

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4. Project Milestones

1. M1: Infrastructure Ready: Completion of mechanical schema conversion.

2. M2: Semantic Blueprint: Finalization of the Ontology in Graphixa.ai.

3. M3: Pilot Load: Completion of a 5% data subset load with verified lineage.

4. M4: Operational Loop: Full batch migration with 100% error resolution in the feedback loop.

5. M5: Governance Sign-off: Final audit of provenance reports.

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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.

Pilot Migration

A pilot migration is the "stress test" that proves your three-tiered strategy works before you commit the entire enterprise dataset. Using Graphixa.ai as the semantic orchestrator, alongside mechanical schema tools and human expertise, ensures that you aren't just moving data—you're moving meaning.

 AIMLUX.ai Pilot Migration - Structured checklist :

Phase 1: Preparation & Setup (The "Shell")

Goal: Create the technical destination and the semantic rules.

• [ ] Mechanical: Run the Schema Conversion Tool to generate DDL for a specific subset of tables (e.g., "Customer" and "Transactions" domains).

• [ ] Semantic: Define the Ontology in Graphixa.ai for this pilot scope (semantic types like customer_id, trans_date).

• [ ] Human: Review the converted schema. Does the DDL align with the cloud destination's best practices (clustering keys, partition logic)?

• [ ] Human: Finalize the "Source of Truth" definitions with business owners to ensure the Graphixa ontology is accurate.

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Phase 2: Orchestration & Mapping (The "Brain")

Goal: Link the source to the target without hard-coding.

• [ ] Semantic: Perform Bidirectional Mapping in Graphixa.ai. Map legacy CSV/DB headers to the ontology and the new cloud columns to the same ontology.

• [ ] Human: Manually validate "low-confidence" matches. If Graphixa isn't sure if C_UID is customer_id, an expert must confirm.

• [ ] Semantic: Select the Type-Aware Transformation rules (e.g., "Legacy Date to ISO 8601") for the pilot data.

• [ ] Human: Identify any complex procedural logic (old triggers/stored procs) that the rule-set cannot handle; mark these for manual redesign.

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Phase 3: Execution & Feedback (The "Heartbeat")

Goal: Run the data through the pipes and monitor for clogs.

• [ ] Semantic: Execute the Batch Load. Use Graphixa to generate and run the SQL Upserts for the pilot records.

• [ ] Mechanical: Monitor the cloud DB's ingestion performance. Is the bulk loader hitting any technical bottlenecks?

• [ ] Semantic: Review the Error Feedback Loop. Did Graphixa reject any rows? (e.g., a "text" value found in a "numeric" semantic field).

• [ ] Human: Perform "Root Cause Analysis" on rejected rows. Is the issue in the source data, the ontology definition, or the transformation rule?

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Phase 4: Validation & Lineage (The "Audit")

Goal: Prove that the data arrived correctly and is traceable.

• [ ] Semantic: Generate a Lineage Report in Graphixa.ai for a sample of migrated records. Can you trace Record #502 from the Cloud DB back to the original legacy row?

• [ ] Human: Conduct Functional Validation. Do the pilot reports in the new system match the numbers in the legacy system?

• [ ] Human: Perform Performance Tuning. Does the new SQLScript (redesigned by humans) run faster than the legacy code?

• [ ] Strategic: Make the Go/No-Go Decision for the full-scale migration based on the pilot's error rates and lineage accuracy.

The Standalone Role of Graphixa.ai

AIMLUX.ai solutions; Ai Governance ai governance System - Graphixa.ai shares the logical features of semantic concepts (ontology, lineage)   with the KGNN (Knowledge Graph Neural Network) platform, through Equitus.ai is offering a highly focused, "pluggable" solution for the operational side of data management.

Operational Semantic Orchestration tool:

The Standalone Role of Graphixa.ai

While it shares the same "DNA" as Equitus.ai’s larger ecosystem, Graphixa.ai is purpose-built to handle the execution of data movements using semantic logic, without requiring the full heavy lifting of a Knowledge Graph Neural Network.

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Step-by-Step Operational Workflow

1. Semantic Typing (The Standalone Ontology)

Even without KGNN, Graphixa.ai uses a dedicated ontology as its Reference Model.

• It defines what data is (e.g., customer_id) and its technical requirements (numeric, string).

• This provides a "governance-first" framework that exists independently of any specific database.

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2. Bidirectional Mapping (The "Middleman" Strategy)

Graphixa.ai acts as the orchestrator between disparate systems.

• Instead of creating fragile direct links, it maps Source → Semantic Type and Target → Semantic Type.

• This "Mapping, not Conversion" approach ensures that if you change your source system, you don't break your downstream integrations.

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3. Type-Aware Transformation (Deterministic Logic)

Because it doesn't rely on the predictive nature of a KGNN, Graphixa.ai stays strictly deterministic.

• It uses rule sets to convert data types (e.g., Oracle to SAP).

• It generates high-performance SQL Upserts and bulk loads.

• Key Point: It focuses on data transformation, avoiding the complexity of procedural logic conversion.

4. Orchestration and Error Feedback

As an operational tool, it manages the "heartbeat" of data ingestion.

• It processes data in batches and captures errors in real-time.

• Closed-Loop Feedback: Errors aren't just logged; they are fed back into the workflow, allowing for corrections and re-runs without manual database surgery.

5. Lineage and Traceability

It reuses the concept of semantic lineage to provide total transparency.

• Every movement creates a Lineage Event.

• This allows you to trace a specific field's journey to answer exactly "where did this data go wrong?"—a critical requirement for auditability and AI governance.

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Summary of Independence

Feature	Equitus.ai KGNN Platform	Graphixa.ai (Standalone)
Primary Goal	Discovery & Relationship Inference	Operational Orchestration & Ingestion
Logic Type	Predictive / Neural Network	Deterministic / Rule-Based
Integration	Deep Data Relationships	Semantic Field Mapping & SQL Generation
Dependency	Requires full graph infrastructure	Independent; can be deployed solo

Graphixa.ai = operational semantic orchestration and lineage tool

Graphixa.ai = operational semantic orchestration and lineage tool

Graphixa.ai, developed by Equitus.ai, is a sophisticated data unification and governance platform. Much like Informatica’s IDMC, it is designed to manage complex, fragmented data landscapes across hybrid and multi-cloud environments.

However, where traditional tools often rely on manual, one-to-one "field mapping" (connecting Column A to Column B), Graphixa uses a Semantic Layer—essentially a brain that understands the meaning of the data before moving it.

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Here is the step-by-step breakdown of how Graphixa governs and integrates data:

Step 1: Semantic Typing via Ontology

The foundation is a Reference Model (Ontology) that creates a universal language for your business.

• The Logic: Instead of just seeing technical names (e.g., CUST_ID, ClientNum), the ontology defines a Semantic Type called customer_id.

• Standardization: Each semantic type is anchored to a specific technical data type (e.g., order_date is always a datetime).

• The Value: This prevents "dirty data" from the start by ensuring every piece of information follows a strict, conceptual blueprint.

Step 2: Bidirectional Mapping

This is where Graphixa separates itself from basic ETL tools. It uses a "middleman" approach.

• Decoupled Integration: Source fields and target columns are both mapped to the Semantic Layer, not to each other.

• The "Reasoning" Engine: Because the source and target both speak the same "ontology language," the system reasons that they belong together.

• Flexibility: This makes your data architecture modular. If you swap your CRM from Salesforce to SAP, you only have to map the new SAP fields to the semantic layer once, rather than re-mapping them to every individual target.

Step 3: Type-Aware Transformation

Once the relationship is established, the platform handles the technical heavy lifting.

• Deterministic Conversion: It uses pre-defined rule sets to handle syntax differences (e.g., converting an Oracle-specific timestamp to an SQL Server format).

• No "AI Hallucinations": Crucially, these transformations are deterministic rules, not generative AI. This ensures 100% accuracy and compliance.

• Automated SQL: It generates Upserts (Update + Insert) and bulk loads, ensuring that existing records are updated and new ones are created without duplicates.

Step 4: Orchestration and Error Feedback

Graphixa manages the data "traffic" to ensure the system doesn't break under pressure.

• Batch Processing: Data is moved in chunks for stability.

• Closed-Loop Feedback: If a record fails (e.g., a "text" value is found in an "amount" field), the error isn't just lost in a log file. It is fed back into the workflow.

• Agility: This allows data engineers to see, correct, and re-run specific failed batches without starting the entire integration over.

Step 5: Lineage and Traceability

The final step is about total visibility—the "Audit Trail."

• Lineage Events: Every transformation creates a permanent record of what happened to the data.

• Root Cause Discovery: You can trace a specific record back to its source to answer, "Where did this data go wrong?" * Explainable Data: This provides the Traceability and Explainability required for modern AI governance, ensuring you can prove exactly where the data feeding your AI models came from.

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Informatica uses a "Metadata-Driven" approach (CLAIRE AI), which excels at cataloging and automating complex pipelines across thousands of existing systems. Graphixa.ai (by Equitus.ai) uses a "Semantic-Driven" approach (Ontology/KGNN), which focuses on teaching the system the meaning of the data to create a universal translator.

Comparison: Semantic vs. Metadata Approach

Feature	Graphixa.ai (Semantic/Ontology)	Informatica (Metadata/Active Fabric)
Core Philosophy	Meaning-First: Data is defined by its real-world concept (Ontology) before it is moved.	System-First: Data is cataloged based on where it lives (Metadata) and then integrated.
Mapping Logic	Bidirectional: Source and Target both map to a central Semantic Layer.	Linear/Point-to-Point: Usually maps Source directly to Target with metadata assist.
Data Interoperability	High: Systems "speak" the same language via the ontology, allowing for easy hot-swapping of sources.	Moderate: Relies on connectors and sophisticated mapping to bridge different systems.
AI Governance	Explainable Reasoning: Logic is based on business rules and relationships in a Knowledge Graph.	Augmented Automation: Uses ML (CLAIRE) to suggest mappings and automate repetitive tasks.
Handling Errors	Closed-Loop: Errors are fed back into the workflow for iterative correction and re-runs.	Log-Driven: Standard error logging and alerting within the orchestration pipeline.
Best Use Case	Intelligence & Context: Ideal for AI readiness, Knowledge Graphs, and "explainable" governance.	Scale & Efficiency: Ideal for massive-scale migration, cloud data warehousing, and MDM.
User Experience	Business-Centric: Users interact with concepts like "Customer" or "Revenue."	Technical-Centric: Users interact with schemas, tables, and technical metadata.