Being neurodivergent means my brain doesn't let go until it's done. I stopped fighting that and started using it. Hyperfocus isn't a superpower. It's just stubbornness with better aim.
- 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, Cramer'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 that make your weakness irrelevant. Drop files in a folder, get deliverables via email. Know where your categories stand in minutes - not weeks. Leads routed to whoever can close them.
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.
Built for seafood. Designed for any industry that tracks customers, products, and transactions.
Predictive AnalyticsWhat 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 ($30B+) company to replicate * the overall enterprise value these systems generate Full Analysis Request: Work Summary: Production-grade internal analytics systems (~20,300 lines of Python total): 1. Category Analysis Engine (~9,000 lines) Self-service platform on SharePoint with folder triggers. Users drop raw data, system runs automatically, deliverables arrive via email. Category intelligence, margin analysis, and actionable sales leads routed to whoever can close them. 2. Price Analysis & Optimization Engine (~6,300 lines) with custom learning agent Pricing optimization that learns from outcomes. Standard ML approaches didn't fit the category nuances - built a custom learning agent that tracks what was recommended, what happened, and adapts. 3. Revenue Management Diagnostic Engine (~5,000 lines) Combines the best of both engines. Validates pricing logic by matching internal positions against external market data. Statistical validation (Chi-squared, Cramer's V) ensures you only act on true signal. 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 * validate pricing logic against external market data Built on SharePoint because that's what was available - adaptability over ideal conditions. REQUEST (Use only public benchmarks): 1. Outsourcing Cost Estimate Estimate cost for Fortune 50-100 ($30B+) 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
500K → 145,000
3 production engines using Machine Learning. Category analysis across 13 seafood segments
Self-service, company-wide. Drop files in, get deliverables back - no analysts required. One system turned a two-week process into under 5 minutes.
~6 Months
6 months to production-ready code - 3 engines that provide your department with data-driven insights to make decisions that affect your bottom line.
Added learning agents so your insights evolve and adjust to your company's actual results - not idealized - course-correcting on a cadence you decide.
Open Source
salesengine - configurable sales intelligence engine for any industry. 22 functions, auto-detect columns, one config.
fishlib - domain-specific product standardization for seafood data 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 - and now it's open source. The only requirement is that you actually want to know the truth.
Show me your weakness,
and I'll make it irrelevant.