One-page brief
A compact overview of the system thesis, core data objects, proposed architecture, and first MVP boundary.
About
Business operator, systems thinker, and AI workflow builder.
My background crosses systems engineering, computer science, architecture, B2B deal development, luxury retail, and high-touch e-commerce. That mix gives me a specific operating position: I understand the judgment happening inside real commercial workflows, and I can translate that judgment into product logic, automation strategy, and working internal systems.
I have 9+ years in commercial operations, including B2B deal development for major enterprise clients and 5 years generating $3M+ annually in high-touch sales at a data-driven luxury e-commerce platform. My work sits closest to problems where frontline expertise, customer behavior, workflow friction, data structure, and AI-assisted implementation meet.
Saw the gap
Identified a repeated workflow failure inside high-touch sales and scoped the replacement without an assigned roadmap, dedicated team, or budget.
Built the layer
Designed and built a Chrome extension-based internal workflow platform with AI-assisted development, permissions, logs, audit trails, and UTM attribution.
Shipped through governance
Took the tool through IT, Data, security, compliance, rollout, and adoption. It now supports 20 active users and replaced a $70K/year SaaS workflow.
The System Design
From one shipped internal tool to Commercial Judgment Infrastructure.
The Chrome extension began as a practical workflow replacement inside high-touch sales. The deeper design problem was larger: how to capture execution-linked commercial judgment, preserve the context around it, and return it to the organization as reusable operating intelligence.
The system design work extends that production tool into a broader architecture for Commercial Judgment Infrastructure: a capture layer, operating layer, shared execution spine, and Product / Client learning loops.
System design deck
A hiring-facing walkthrough of the capture layer, operating layer, shared execution spine, and Product / Client learning loops.
See the system in action
A redacted video walkthrough of the deployed Chrome extension workflow tool, plus full architecture whitepapers on the operating and learning layer architectures. Available on request.
The Thesis
AI is chasing the judgment happening in this exact second.
It has processed everything and inhabited nothing.
Human experts make directional judgments: they point toward a future that has not arrived yet. But most organizations have no structure for capturing those judgments as they happen.
Until now, expert judgment has been a personal asset the organization rents.
This thesis asks how expert judgment can be captured structurally, validated against what happens later, and turned into an enterprise point of view: a compounding asset, a learning system, and a moat.