Andrew Fajemisin

Growth Engineer & Full-Stack Builder

I don't just buy media — I build the infrastructure behind it. Below are live products I've designed, built and shipped solo: AI apps, subscription SaaS, pricing-experiment engines and analytics dashboards, on Cloudflare Workers / Pages with SQL (D1) backends, Stripe and the OpenAI API.

Selected builds

Products I've shipped

Each is a real, deployed system — front end, backend, database, payments, tracking and AI — built end-to-end by one person. Selected from a wider set of 20+ shipped projects.

SubToRaw

Learn Japanese from anime
Live app

A full consumer web app that turns anime into language lessons — a streaming-style feed with per-line furigana/romaji toggles, adjustable difficulty and gamified progression, backed by a large managed content corpus. Full-stack, built and operated solo.

Consumer appFull-stackContent pipelineSQL / D1
subtoraw.com

The NPC Antidote

AI reasoning coach (DebateMeter)
Live app

A subscription SaaS that trains people to spot manipulation, logical fallacies and weak arguments in real time — with an AI coach, daily training, streaks and mastery tracking, plus an admin dashboard. Stripe subscriptions and retention mechanics built in.

Subscription SaaSAI agentStripeRetention
app.debatemeter.com

Pricing Experimentation & Incrementality

Applied pricing science
Method

Pricing-experiment systems I've designed and run end-to-end: price-point A/B testing via geo-splits and new-cohort rollouts, willingness-to-pay research (conjoint, Van Westendorp, Gabor-Granger), and an ML model scoring 60+ demand signals — all measured against a randomized holdout control group, so the profit lift is proven, not inferred.

IncrementalityExperiment designEconometricsPricing
Live demos & walkthrough on request

BelowBookStocks

Deep-value equity screening
Live

A systematic below-book-value investing methodology — a 7-criteria screen, market-cycle map and thesis-grading journal — delivered as a product with its own acquisition funnel. Reflects a genuine interest in markets and quantitative investing.

FintechInvestingFunnel
belowbookstocks.com

Backtesting Engine

Python · systematic trading
Code

A point-in-time financial backtesting engine — market-data pipelines and signal modelling for testing systematic trading strategies without look-ahead bias. Written in Python; code walkthrough available on request.

PythonQuantData pipelinesSignal modelling
Code sample on request
How it's built

Stack

Cloudflare Workers & Pages D1 (SQL) Python JavaScript / TypeScript Stripe OpenAI API GA4 + Meta CAPI (server-side) A/B & holdout experimentation