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01.1 // AI BUILDER · A PLAIN-ENGLISH TOUR

AI that does
real work.

Four systems, all running — most of them inside my own working week as a supply chain planner in NZ healthcare distribution: an AI purchasing copilot you can drive in your browser, a planner's judgment made callable, a personal fleet of agents, and a tennis app with real players on it. This is the plain-English version: what each one does, the honest numbers, and what it proves.

// HOUSE RULES: EVERYTHING SHOWN RUNS · SYNTHETIC DATA IS LABELLED SIM · AGENTS DRAFT, HUMAN DECIDES
01 // SUPPLY CHAIN · PURCHASING LIVE DEMO

Metis — an AI purchasing copilot you can drive in your browser

My role
Designed and built, solo
Data
Synthetic, labelled SIM — nothing leaves the tab
Runs on
Your browser: SQLite via sql.js, state in localStorage

A distributor raises hundreds of purchase-order lines a week, and every line has to clear three checkpoints: Was it ordered? Did the vendor confirm? Did the warehouse receive it? Most of that is routine chasing — reading confirmation emails, nudging silent vendors, matching paperwork. Metis does the routine part and hands a person only the genuine exceptions.

The 2.0 demo is a complete simulated company: 10 vendors, about 2,000 products and 200 PO lines a week, entirely synthetic. Step the clock forward, work the exception queue yourself, and watch the honest number appear — roughly 81% of lines handled end-to-end with zero human touches. The dashboard shows 100% before you have worked the queue. That is the mirage; 81% is the truth.

The demo also publishes its own failure. The first real supplier email we fed it (fully desensitised) returned nothing: the payload sat in PDF attachments, the date was day/month/year, the PO number was the wrong shape. That gap is on the page as a fix list, not hidden.

Why it matters

This is the realistic shape of AI in a back office — not replacing the team, but clearing the routine 80% so people only handle the calls that need judgment.

SIM · 10 VENDORS · ~2,000 SKUS · ~200 PO LINES/WK × 3 CHECKPOINTS · TOUCHLESS: 100% MIRAGE → ~81% HONEST
→ Play the 2.0 demo runs in your browser · synthetic data · nothing uploaded
02 // SUPPLY CHAIN · PLANNING LIVE

Replen — a planner’s judgment, made callable

My role
Author — the planning judgment is mine
Data
Yours, via CSV templates — no invented numbers
Built as
1 knowledge pack + 5 dependency-free Python calculators

Replen is a supply-chain planning skill that sits between a general AI and a real operating decision. It sizes safety stock and reorder points, diagnoses forecast error, handles intermittent demand, compares EOQ against MOQ and pack constraints, and segments a portfolio with ABC–XYZ. The more important part is the gate before the number: which service-level definition do you mean, is the variability measure valid, and what data is still missing?

I built it by turning repeated planning decisions into a written answer protocol, moving the arithmetic into five dependency-free Python calculators, adding reusable CSV inputs, and separating public planning knowledge from private work context with a red-line check. Its operating rule: numbers without assumptions are forbidden. It is a live pilot for decision support — no ERP write-back and no invented data.

Why it matters

It shows how experienced staff can turn the judgment in their heads into a reusable AI workflow — without handing control of the business process to the model.

PILOT · 1 KNOWLEDGE PACK · 5 CALCULATORS · 2 CSV TEMPLATES · P1/P2 SERVICE-LEVEL CHECK BUILT IN · 0 ERP WRITES
03 // HOW I WORK LIVE

The Personal AI Fleet — one person with a staff of agents

My role
Architect and operator
Data
My own ledgers — one source of truth per domain
Built as
Markdown brains + callable skills + ledgers + scheduled jobs

An org chart for a single life: a chief-of-staff agent on top and specialists underneath — investing research, personal brand, health, the day-job planning stack — plus a horizontal layer that digests everything I read and hear and routes the signal to the cell that should change because of it. Every agent is built the same four ways: a written-down brain, a callable skill, a ledger, a rhythm.

Every agent drafts; one human reviews and decides. No trade is ever executed, nothing is ever auto-posted, and a failed run says so instead of inventing a number. It is how this site, the research notes and a good share of my working week actually get produced — this page included.

Why it matters

For a small team, this is the cheapest honest picture of what an “AI hire” looks like — where agents genuinely carry load, and where a human must stay in the loop.

1 CHIEF OF STAFF · 5 SPECIALIST CELLS · AGENTS DRAFT, HUMAN DECIDES · 0 AUTO-PUBLISH · 0 TRADES EXECUTED
04 // SHIPPED PRODUCT LIVE

Tennis Buddy — what one person can ship now

My role
Product, design, frontend, backend, deploy, ops
Data
Real players and real matches — in production
Stack
React 19 · Supabase (Postgres, RLS, magic links) · Vercel · Resend

A match-logging and hitting-partner app for club tennis players: find someone at your level, set up a hit, chat before the match, submit the score set by set, watch the season build. Designed, built and shipped to production by one person — a supply chain planner, not a software engineer.

The method matters more than the stack. I drew every screen before any code existed — 34 frames and an acceptance spec — then directed AI to write the React and SQL while I made every product call. Five phases went from that design to v1.0 in eleven days, with friends signing up mid-build, and the spec’s fourteen acceptance criteria were walked at the end: thirteen pass, one deferred on purpose. The milestone that counts: a real invite, accepted, a Saturday morning on court, and the score recorded in the app — a loss, logged honestly.

Why it matters

Custom software has quietly become much cheaper. The internal tool on your wishlist may no longer need an agency quote — it needs someone who can design the product and direct the build.

SOLO BUILD · V1.0 IN 11 DAYS · 5/5 PHASES · 13/14 ACCEPTANCE · REACT 19 + SUPABASE + VERCEL · REAL PLAYERS, REAL SCORES
→ Open the live app browse open games without an account
OPEN TO // AI PILOTS · LIVE DEMOS · A LOOK AT YOUR BACK OFFICE

Curious what this could look like in your business?

I’m open to AI pilot collaborations with New Zealand businesses, and happy to demo any of the four systems above live — on a call or at a meetup. If it helps, bring a messy back-office process; those are my favourite.

→ panjunbao@gmail.com