Building Intelligent
AI Decision Systems

Executive Summary: A marketing decision intelligence system designed to transform fragmented marketing data into structured, data-driven business decisions.

Problem: Disconnected datasets meant manual, reactive, and inefficient decision-making.

Solution: A Python pipeline combining data processing, feature engineering, Machine Learning and LLM-generated business recommendations to automate repeatable marketing analysis and decision support.

Impact: Replaced fragmented manual analysis with structured, repeatable workflows that prioritise high-value customer segments and campaigns using data-driven decision logic.

Executive Summary: An end-to-end SEO & GEO Decision Intelligence platform combining live search performance architecture, Machine Learning, multi-horizon forecasting, explainable AI, grounded RAG, Multi-LLM recommendations and human-governed Decision Memory.

Data Scale: Processes 16+ months of live Google Search Console data covering 12.9M+ impressions and 837K+ clicks.

ML & Forecasting: Uses XGBoost, LightGBM and Random Forest with chronological validation, model benchmarking, baseline/strategic guardrails and multi-horizon forecasting across 7, 14, 30, 90, 180 and 365 days.

AI & Decision Layer: Integrates SHAP explainability, Multi-LLM support, grounded RAG, deterministic fallbacks, opportunity intelligence and a human-in-the-loop Decision Memory lifecycle for traceable recommendations.

Reporting & Engineering: Includes Power BI semantic fact/dimension exports, Streamlit dashboards, Docker-based architecture, GitHub Actions CI/CD, automated QA and production-oriented scheduled execution support.

Validation: 273 full-system automated tests passed in the full development environment, with a separate 275-test public release suite.

Public Demo: The portfolio deployment runs in a sanitised, fail-closed environment; private production data, credentials and live integrations are not exposed.

Executive Summary: A production-oriented advertising Decision Intelligence platform combining Google Ads and GA4 architecture, Machine Learning, multi-horizon forecasting, scenario simulation, explainable AI, grounded RAG, Multi-LLM insights and human-governed decision workflows.

Data Scale: Processes 3+ years of live Google Ads data — 94K+ records across 37 campaigns and 65 ad groups, covering 21M+ impressions and 3.2M+ clicks.

ML & Forecasting: Benchmarks XGBoost, LightGBM and Random Forest with chronological, leakage-safe validation, baseline guardrails and target-specific Champion Routing across 7, 14, 30, 90, 180 and 365-day horizons.

Decision Intelligence: Combines forecast-vs-actual analysis, budget scenario simulation, portfolio allocation, risk/opportunity intelligence, recommendation logic and SHAP explainability.

AI & Governance: Supports Claude, OpenAI GPT and Gemini through a provider-independent Multi-LLM layer, grounded RAG with deterministic fallback and human-in-the-loop Decision Memory using the lifecycle PROPOSED → APPROVED/REJECTED → APPLIED → OUTCOME_MEASURED.

Reporting & Engineering: Includes PostgreSQL-ready architecture, Power BI semantic fact/dimension exports, Streamlit dashboards, Docker, GitHub Actions CI/CD, automated QA and CRON/scheduler-ready execution workflows.

Validation: 320 full-system automated tests passed in the full development environment, with a separate 62-test public release suite.

Public Demo: The public portfolio environment uses sanitised data and remains read-only/fail-closed; no private production data or credentials are exposed.

Executive Summary: A Canada-based housing coordination platform designed to support land registry, modular housing workflows, and data-driven infrastructure planning.

Problem: Housing coordination processes require structured land data, role-based access, legal documentation, and clear workflows between landowners, builders, eco-professionals, and home seekers.

Solution: Prepared Software Requirements Specification (SRS) documentation covering identity management, role-based access, land registration, capacity management, field specifications, use cases, and prototype screen documentation.

My Contribution: Business analysis, requirement engineering, use case design, workflow documentation, field validation logic, and prototype interpretation for Module 1 and Module 2.

Focus Areas: RBAC, land registry workflows, GIS-based data logic, modular design assignment, audit trail requirements, and user dashboard flows.

Executive Summary: A Canada-based community care and senior wellness ecosystem focused on healthcare coordination, accessibility management, senior wellness tracking, and community-centered support.

Problem: Senior wellness platforms require structured registration, role management, credential verification, wellness profiling, accessibility logic, emergency contacts, and compliance-aware workflows.

Solution: Prepared SRS documentation for Module 1 and Module 2, covering user registration, role management, professional credential verification, senior wellness profiles, mobility levels, program participation, and emergency contact flows.

My Contribution: Designed business requirements, use cases, field specifications, workflow logic, validation rules, and prototype documentation aligned with healthcare-oriented platform needs.

Focus Areas: PHIPA/PIPEDA-aware requirements, wellness profile structure, mobility and accessibility logic, credential review flow, attendance tracking, and senior support workflows.

Executive Summary: A Canada-based multi-sided SaaS platform connecting Candidates, Employers and Institutions through structured education, credential verification and employment workflows.

Business-Technical Contribution: Translated stakeholder and business needs into structured, implementation-ready requirements across Candidate, Employer, Institution, SuperAdmin and Data Analytics modules.

Requirements Engineering: Produced business analysis and user story specifications covering functional requirements, acceptance criteria, business rules, role-based workflows, non-functional requirements, revenue requirements and release acceptance criteria.

Functional QA: Performed structured manual black-box functional testing across five platform modules, covering 148 test cases with 128 passed, 16 failed and 4 blocked, and documented 20 defects across Critical, High, Medium and Low severity levels.

Release Readiness: Evaluated platform behaviour against defined acceptance criteria, documented release risks and outstanding defects, and supported the transition toward UAT and production readiness.

Deliverables: Business Analysis & User Story Specification · Functional QA Test Report · User Stories · Acceptance Criteria · Functional Requirements · Defect Analysis · Release Recommendation