Publications
Articles and insights on data engineering, team leadership, and analytical methodologies.
AI-Driven Requirements Engineering: Building the Dark Knight of Business Analysis (BATMAN_IA)
A deep-dive case study on Politik-IA —an AI-powered political fact-checking platform— and how we built a standalone collaborative Project Manager integrated with an autonomous, database-connected AI Agent to eliminate backlog bloat and generate bulletproof user stories.
Abstract
Software project failure rates remain notoriously high, with McKinsey and Standish Group frequently citing poor requirement definition, scope creep, and alignment failure as the leading culprits. When launching Politik-IA —an ambitious AI-powered political fact-checking platform— as a solo vibe coder, we wanted to deliver a fast and maintainable solution for my client. To succeed, we couldn't rely on generic, disconnected project management tools. We developed a custom, standalone Project Manager suite capable of mapping strategic vision directly to executable backlogs. More importantly, we engineered BATMAN_IA: an autonomous conversational AI Business Analyst. Tightly integrated with our PostgreSQL database via custom tool-calling, BATMAN_IA translates product discussions into precise User Stories with 10–20 structured Acceptance Criteria (Given/When/Then), while enforcing strict anti-duplication constraints to keep backlog hygiene pristine.
AI Agents in Enterprise: Promise, Reality & Practical Roadmap
An executive briefing on autonomous AI agents — backed by Gartner, McKinsey, PwC data and 10 enterprise case studies. From $800M Foxconn gains to 40% project abandonment rates.
Abstract
AI agents represent the most significant shift in enterprise automation since cloud computing. This briefing separates hype from evidence: Gartner predicts 40% of AI agent projects will be abandoned by 2027, yet proven deployments at Klarna ($40M/yr savings), ServiceNow ($325M value), and Foxconn ($800M unlocked) demonstrate transformative potential. The document provides a practical governance framework (7 principles), a decision matrix, a 12-month implementation roadmap, and 10 detailed case studies — giving executives the tools to deploy AI agents responsibly rather than recklessly.
Abacus.AI: The Ultimate AI Platform Guide
A comprehensive guide to Abacus.AI — access 40+ top AI models (GPT-5, Claude, Gemini, Llama) for $10-20/month with DeepAgent autonomous capabilities.
Abstract
Abacus.AI gives you access to 40+ market-leading LLMs (OpenAI, Anthropic, Google, Meta, DeepSeek, xAI) plus its own optimized models. For $10-20/month, you get 20,000-25,000 credits, unlimited access to several models, and DeepAgent — an autonomous AI agent that can build apps, conduct research, create documents, and automate complex workflows. This guide covers pricing, features, available models (text, image, video generation), and why it offers insane value compared to multiple separate subscriptions.
Rethinking COVID-19 Country Comparisons: Why Urban Density Matters
A data-driven analysis revealing the true impact of pandemic policies — and Sweden's collective immunity gamble.
Abstract
This study presents a comparative analysis of COVID-19 infection and mortality rates across countries, introducing a novel methodology that removes the confounding effect of urban density. Using IHME-corrected data (which more than doubled reported death counts), we indexed each country's performance against the United States as a 100% baseline. Our findings reveal that Sweden's 2020 collective immunity strategy resulted in 3.2x more infections than the USA when controlling for urban density — making it one of the worst-performing wealthy nations. This analysis also exposes significant disparities within US states, correlating with policy choices rather than geography alone.
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