"I found something. I couldn't leave it alone. So I built the thing that could use it."
— The origin of every system I've built
The Methodology
I don't deploy on faith. I validate, test, then scale.
click to explore each step
Signal
Prescribe
Experiment
Scale
Learn
Signal Detection
Step 1 of 5"Is there a pattern worth acting on?"
Statistical validation before anyone lifts a finger. Chi-squared tests, Cramér's V, entropy analysis. If the data is just noise, we stop here — no wasted resources chasing ghosts.
What This Enables
The methodology is portable. The variables change based on your data.
Minutes, Not Weeks
Reports that used to take weeks now run in minutes. Category analytics, lead generation, performance dashboards — automated, consistent, on demand.
Actionable Leads
Every customer classified into exactly one action: win-back, recover declining, convert, or acquire. Quantified in dollars and volume. Routed to the person who can close it.
Market Intelligence
Compare your performance to the rest of industry — but only when the comparison is valid. Product standardization ensures you're comparing apples to apples.
Dynamic Optimization
Basket-level margin optimization. Rewards that build with behavior, reset when behavior drops. What the best street sales reps do instinctively — codified and scaled.
Democratized Competence
Tools built for people who were told they couldn't have this. Drop files in a folder, get insights out. No pivot tables. No waiting for analysts. No gatekeepers.
Systems That Learn
Learning agents that improve as they accumulate knowledge. Every recommendation tracked, every outcome fed back. The model gets better whether you're watching or not.
What Would It Cost to Replicate This?
Estimate what your organization would spend to build these systems internally or through outsourcing
System Scope
Organization Scale
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Estimated Build Cost
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Typical Timeline
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Team Required
Based on McKinsey, Gartner, and Glassdoor benchmarks for enterprise ML/analytics projects
Want the Full Methodology?
Run this prompt yourself in any AI assistant. Select your company benchmark, then copy the prompt.
TLDR Request (Required FIRST Output): Provide a 3 to 5 sentence executive TLDR summary that explains: • what the analytics systems do, including any learning agents • the estimated cost for a Fortune 50-100 company to replicate • the overall enterprise value these systems generate Full Analysis Request: Work Summary: Production-grade internal analytics systems including: 1. Category Analysis Engine (~9,000 lines of Python) 2. Price Analysis & Optimization Engine (~6,300 lines) with learning agent These systems: • automate category intelligence • detect margin leakage • identify improvement opportunities • provide optimization logic • generate customer- and category-level insights • build predictive & prescriptive logic via learning agent • replace inconsistent manual workflows REQUEST (Use only public benchmarks): 1. Outsourcing Cost Estimate Estimate cost for Fortune 50-100 company to outsource development. Use Conservative, Typical, High scenarios. 2. Internal Staffing Requirements Determine roles, seniority, FTE count, and timeline. 3. Time-to-Build Benchmarks Estimate timelines for typical enterprise teams. 4. Deliverables A. ROI Breakdown Table B. Value-Created Chart C. Executive Summary D. Source Citations
Built and Validated
200K → 15K
Lines written → Lines that survived. Scientific method at scale.
~18 Months
From "I found a signal" to production systems. Self-taught.
Open Source
fishlib — product standardization library on PyPI
Give me your data. Tell me what variables you have. I'll find which combinations produce signal. I'll validate whether those variables are stable enough to act on. If they are — we prescribe, test, and scale. If they're not — we find different variables or fix the governance first.
This works on any sales data, any category, any organization. The methodology is the product. The only requirement is that you actually want to know the truth.
"Show me your weakness, and I'll make it irrelevant."