Portfolio
Case studies from my work at AT&T, Walmart, Lowe's, and more. Real results from real projects at enterprise scale.
Agentic AI Product Strategy for Fortune 100 Telecom
Product Managers were spending 40% of their time on repetitive documentation tasks — writing epics and features in Azure DevOps, creating user stories with acceptance criteria, and drafting process flows. This created PM bottlenecks, inconsistent quality across a 600+ person organization, and slow velocity from concept to dev-ready.
AI Agent Evaluation Framework (HHH+)
AI agents were being deployed without standardized evaluation, leading to ad-hoc assessments, safety concerns, and inconsistent quality. Needed a systematic framework to evaluate AI agents across safety, fairness, and reliability dimensions before deployment.
Digital Platform Transformation: 39% Revenue Increase, 300M Peak Hits
The client's digital platform had a legacy Websphere architecture that couldn't scale for peak traffic, price inconsistencies across web, mobile app, and stores causing cart abandonment, and a batch-based marketing system that couldn't do real-time personalization. $2B+ in projected annual digital revenue was at risk.
National Fiber Expansion: Product Strategy for 1.5M New Customers
The company wanted to expand fiber connectivity from 21 states to all 48 contiguous states through a joint venture partnership. This required new ordering systems, partner integration with a different technical stack, regulatory compliance across states, and a seamless customer experience regardless of infrastructure owner.
Claude Code AI System: Multi-Source Research Agent
Needed a system that could research comprehensively from multiple sources (not just Google), detect when sources disagree on facts, generate content in a consistent personal voice, and develop features autonomously — plan, implement, validate, and commit code.
Podcast-to-Pack: AI-Powered Podcast Analysis Platform (Award Winner)
Podcast listeners struggle to extract actionable insights from hours of content. Manual note-taking is slow, inconsistent, and misses connections. Needed a system that could analyze podcasts, extract key insights, identify themes, and generate structured deliverables — all while handling messy real-world audio and maintaining accuracy.
Second Brain: Personal AI Assistant with 3-Layer Security
Personal assistants need access to sensitive data (calendar, emails, vault notes) to be useful, but LLMs are vulnerable to prompt injection attacks. Needed a system that could integrate Google Calendar, Slack, and Obsidian vault memory while defending against adversarial prompts trying to leak secrets or manipulate outputs.
MakeYourApp.ai: Sketch + Voice to React App in 60 Seconds
Non-technical users have app ideas but can't prototype them. Wireframing tools require design skills, and no-code builders have steep learning curves. Needed a system where someone could sketch a UI on paper, describe it verbally, and get a working React app in under 60 seconds — no coding, no tutorials, just natural interaction.
Obsidian-Agent-Post: Voice-Consistent AI Content Pipeline
Creating consistent, high-quality content at scale requires maintaining voice, avoiding generic AI slop, and validating quality before publishing. Manual editing is slow; unchecked LLM output lacks personality. Needed a system with 31 voice checkpoints, 6-dimension quality scoring (CQS), and Karpathy-style validation loop — all while generating blog posts and LinkedIn content in a consistent personal voice.
AAMAD Framework: Multi-Agent Development Methodology (PyPI Published)
AI-assisted development with multiple agents (Product Manager, Backend Dev, Frontend Dev, QA, DevOps) lacks standardized contracts, leading to context drift, missed requirements, and inconsistent quality. Needed a framework with strict persona contracts, reproducibility, provenance tracking, and adapter abstraction for CrewAI, LangGraph, or prompt-based execution.