Building Intelligent
AI Decision Systems

Executive Summary: A substantial technical output from a US-based international project engagement in data analytics and marketing decision intelligence. I designed and built the pipeline end-to-end to transform fragmented marketing, customer and campaign data into structured, decision-ready analytical outputs.

Business Problem: Marketing data was fragmented across customers, campaigns and channels, making analysis dependent on repeated manual preparation and reactive reporting. The challenge was to create a repeatable workflow that could connect customer behaviour, campaign performance and business KPIs, then prioritise what should be reviewed next.

My Contribution: I worked across data acquisition and research, public-opinion and market analysis, cross-country market-viability analysis, B2B SaaS lead generation and validation, data cleaning, transformation and validation, KPI and operational analysis, Python-based analytical workflows, Power BI reporting and technical documentation.

End-to-End Solution: I designed the workflow from raw-data ingestion through cleaning, feature engineering, customer and campaign analysis, analytical/ML modelling, decision-support logic, recommendation generation and Power BI reporting.

Decision Intelligence: The system combines customer segmentation, campaign/channel performance analysis, conversion and customer-value indicators and structured prioritisation logic to identify higher-value segments, campaign signals and decision-ready recommendations.

Data Governance: Confidential company advertising data is not exposed in the public portfolio version. Public datasets were used with project-team approval to preserve the analytical methodology and system design while maintaining confidentiality.

Business Value: Replaced disconnected descriptive analysis with a reproducible pipeline that standardises data preparation, connects customer and campaign intelligence, improves performance visibility and produces structured recommendation outputs for business review.

Delivery Evidence: Produced reproducible Python/Jupyter workflows, cleaned and validated datasets, Power BI dashboards, reports and technical documentation. Project feedback also documented successful delivery, including an early submission described as matching the requested scope and completing more work in less time than expected.

Public Portfolio Boundary: The public repository demonstrates methodology and system design; it does not claim confidential company revenue, ROI or operational performance.

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.

Business Context — From SEO Execution to AI Decision Intelligence: Built from hands-on SEO workflows I have managed since 2023 across technical SEO, keyword and search visibility analysis, category and product content optimisation and organic growth strategy. These initiatives contributed to +81.1% growth in total ranking keywords, +180.1% in Top-3 keywords and +171.7% in Top-10 keywords.

As Search Console data and analysis requirements grew, I transformed these workflows into an AI-powered Decision Intelligence system to automate forecasting, opportunity detection, technical/content analysis and explainable SEO recommendations.

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.

Business Context — From Cross-Channel Ads Analysis to AI Decision Intelligence: I analysed and reported Google Ads and GA4 performance as part of cross-channel marketing, SEO and e-commerce analytics, using paid-media data to compare advertising efficiency with organic growth, traffic and commercial outcomes.

Role Clarification: I did not directly manage or execute paid-media campaigns. My role focused on performance analysis, KPI reporting, cross-channel comparison and decision-support analytics.

These analytical workflows later informed the development of the Ads Budget Intelligence AI Agent, transforming historical reporting into an AI-powered Decision Intelligence system for ML forecasting, scenario simulation, budget optimisation, risk/opportunity intelligence and explainable recommendations.

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