Technical Artifacts

Welcome to my technical repository. This space serves as an evergreen archive of the systems, automated pipelines, and structural frameworks I engineer to bridge the gap between complex data analytics and scalable operations.

Every asset housed here—ranging from comprehensive standard operating procedures (SOPs) to raw Python scripts and spreadsheet automation frameworks—is built with a defensive, "zero-trust" mindset. The focus is simple: maximize computational efficiency and pipeline automation while maintaining absolute data integrity through deterministic code guardrails and strategic human-in-the-loop oversight.

Enterprise AI Architecture & Zero-Trust Verification Standard

Document ID: SOP-SEO-AI-001 Current Version: 0.9 (Pre-Production/Draft Stage)

Overview

This standard operating procedure (SOP) defines a strict operational blueprint for integrating Large Language Models (LLMs) into live data analysis and SEO workflows without risking data corruption or structural drift. Rejecting the model of unverified AI autonomy, this framework establishes a rigorous, five-step adversarial data pipeline that sanitizes, verifies, and isolates automated payloads before they can ever interact with a production environment.

Technical Highlights Covered

  • Adversarial Validation (Layers 1 & 2): Programmatic schema and token integrity gates designed to catch formatting anomalies instantly.
  • Automated Exception Routing: An isolated quarantine fallback structure that traps corrupted data streams to protect production environments.
  • Human-in-the-Loop (HITL) RACI Matrix: Explicit governance boundaries that keep ultimate accountability and strategic veto power in human hands.
  • Spreadsheet Isolation Protocols: Strict operational sandbox boundaries restricting automated API writes to staging preview buffers only.

Download & Repository Assets

📥 Download Master SOP (PDF) 💻 View Python Validation Code