Translating Executive Goals into
Engineering Reality.
I bridge the gap between business needs and technical execution. Currently transforming from expert Operations Lead to AI-Fluent Product Manager.
The Execution Strategy
Innovation without governance is noise. I treat my transition into Product Management as a live product launch, operating on a rigorous 3-week sprint cadence: Learn → Build → Polish.
This timeline documents my progression from Workflow Automation to full-stack AI implementation. My goal is to bridge the gap between executive strategy and engineering reality by building the tools myself.
Each block below follows a strict development lifecycle:
- Discovery: Sourcing architectural patterns and defining scope to solve specific business problems.
- Implementation: Hands-on coding with Next.js, Python, and LangChain to prove technical feasibility.
- Governance: Enforcing PM constraints on token budgets, risk assessment, and user acceptance.
This is the blueprint for how I build AI tools that create leverage, not novelty.
Live Execution Log
The Journey so Far
Foundations: API & Prompt Engineering
Mastered the fundamentals of LLM interaction, moving from 'Chat' to 'API'. Learned to control output structures via System Prompts.
Help Desk Copilot: RAG-based Q&A System
Built a conversational AI assistant that answers questions about support ticket data using complete RAG implementation (Retrieval-Augmented Generation). Goes beyond keyword search to synthesize answers, identify patterns, and provide insights across the entire corpus—demonstrating the same architecture powering ChatGPT and modern AI assistants.
Full-Stack Prompt Engineering & Governance
Evolved from basic scripts to a production-grade Web Application. Built a 'Prompt Playground' that operationalizes prompt templates and enforces ethical guardrails via UI logic.
Fine-Tuning & Local Inference
Pivoted to 'Small Language Models'. Fine-tuned Llama-3 to perform niche tasks cheaply, proving that bigger isn't always better.
Voice of Customer: Unsupervised Insight Engine
Built an AI-powered analytics platform that transforms unstructured support tickets into strategic insights using unsupervised learning. Automatically clusters thousands of tickets, generates executive summaries, and surfaces hidden patterns—eliminating hundreds of hours of manual categorization while maintaining strict data governance and cost controls.
Market Intelligence Agent: Hybrid RAG with Autonomous Routing
Built an autonomous AI agent that solves the 'stale data' problem by intelligently routing queries between internal knowledge (vector store) and live web search (Tavily API). Demonstrates advanced RAG architecture with self-routing capabilities—the agent autonomously decides which data source to query based on the question's intent, eliminating training data cutoff limitations.
AI ROI Calculator: Translating Technical Metrics into Business Value
Built a financial modeling tool that bridges the gap between technical execution and executive decision-making. Translates AI system costs into CFO-ready 3-year projections using real production telemetry from Block 6. Demonstrates the critical PM skill of business value communication—turning 'it works technically' into '$1.17M annual value' with cited, defensible assumptions.
Classification & Smart Reply Copilot
OBJECTIVE: Launch a local Copilot that uses multi-label classification and prompt chaining to draft fair, ethically-assessed CS responses.
Operational Rigor Meets Product Innovation
I built my career in high-stakes operations, not theory. As a veteran, I learned early on that mission success relies on clear communication and adaptable execution.
I applied that mindset at ZenBusiness (93% efficiency gain) and Southern Glazer's (7% revenue growth). I know what it takes to deliver results in complex, regulated environments.
Now, I bring that discipline to AI. I don't just manage backlogs; I use my technical roadmap to build prototypes, test feasibility, and validate business cases myself.
Prototyping & AI Fluency
I move beyond "buzzwords" to understand the mechanics of AI. I build lightweight prototypes using LangChain.js and OpenAI to test product concepts prior to engineering investment, reducing manual synthesis effort.
Product Strategy & Rigor
"Cool tech" doesn't equal "good product." I apply structured prioritization (RICE/ICE) to balance technical impact with business viability, translating raw data into clear User Stories and Acceptance Criteria.
Data Operations & Analytics
AI is only as good as the data it eats. My background involves heavy lifting in data mapping and schema validation. I use SQL and Power BI to identify category-level trends and workflow friction.
Governance & Value
Innovation requires guardrails. I track Token Budgets and ROI to ensure features are financially viable. I integrate risk management early, considering PII redaction and compliance for regulated environments.