If the team cannot run it without me in the room, it is not a system. It is a favor.
Case studies
The work, shown with its receipts.
What was the actual problem, what did I assume, what got built, and what would I change next time.
Filter by the evidence a reviewer needs fastest.
Showing all 3 proof entries.
Flagship casePublished MVP
Dynamic DCF Valuation Model
Model artifactInspectable assumptionsDecision support
A live, inspectable DCF on any public company (currently Apple). The assumptions, WACC, and
integrity checks are all out in the open, not buried three sheets deep.
Live example
AAPL base case: implied price, market bridge, and an audit trail you can follow.
In the workbook
DCF output, model inputs, WACC build, working capital, three statements, checks, source audit.
Why I built it
Most DCFs are black boxes. I wanted one a reviewer could pick apart without opening Excel.
GA4 BigQuery ecommerce analysis that finds the highest-leverage activation gap, prices the
revenue opportunity, designs the A/B test, and ends in a one-page impact memo.
The finding
New users are 89% of visitors but activate at 0.64%, versus 9.73% for returning users.
The opportunity
A +50% relative lift implies 769 incremental purchasers and $53,130 in revenue.
The action
Run Variant B first, with a one-page impact memo explaining the math and risks.
A framework for the operational audit every team needs: the slow leaks between teams, tools,
and decisions that never show up on a dashboard.
The question
Where is the team paying a tax it does not know it is remitting?
How I would answer it
Process mapping, cost-of-delay math, and an AI agent to track financial impact only where it earns its keep. Manual fixes last as long as someone cares. An agent never gets bored.
What changes
Faster handoffs, fewer "who owns this?" emails, and decisions that do not drift for a quarter.
The model, running in your browser.
Pick a scenario or push on any assumption. The implied share price updates live from a
5-year EBIT-based DCF on Apple.
Six skills I keep reinforcing, and what they look like in practice.
Requirements analysis
Asking the questions before anyone writes code, so the team is not rebuilding it in three months.
Business administration
Looking at initiatives the way a CFO would: margin, risk, growth, and the options it buys later.
Open the valuation case
Lean and Kaizen
Fixing workflows without a six-month consulting deck. The team owns the improvement after I leave.
Python and data tooling
Analysis scripts, automations, and lightweight dashboards that shorten the gap between question and answer.
Open the growth analytics case
Agentic AI
Triage, research, summarization, and the boring orchestration work. Not chatbots for their own sake.
Executive communication
Turning a tangle into one slide leadership can sign off on. Most decisions die in translation.
Read the impact memo
Where I think out loud, before the case study is ready.
Most of these are still cooking. I would rather show in-progress versions and sharpen them
than pretend to a back catalog I do not have.
Showing all project lab entries.
In development
Strategic Workflow Mapper
A tool that turns the "describe your process" interview into a real artifact: one that flags
handoff risk and points at automation candidates instead of being a pretty diagram.
EDI
Process design
Agentic AI
Concept
Value Capture Scorecard
A one-page scorecard for the "should we even do this project?" meeting. Value, complexity,
risk, and whether the team is ready, on a single page nobody can hide from.
Finance
Decision systems
Dashboards
Short takes
On strategy
The strategy deck and the actual work should be able to recognize each other.
When they cannot, it is usually the strategy that is wrong, not the team.
On AI
An AI agent is an intern who never sleeps. Manage it like one.
Clear scope. Check its work. Never let it touch anything you cannot undo.
On evidence
A great technical answer that does not change a decision is a hobby.
The question I keep asking: what is different on Monday morning because of this work?
MBA in Finance, sharpened by product analysis at startups and enterprises.
The MBA taught me how value gets made and lost. The product years taught me how work
actually happens. I bridge the two so the strategy and the system end up being the same thing.
Title
Finance MBA, Product Requirements Analyst
Toolkit
Kaizen, valuation, Java, ERP integration, orchestration and agentic AI
