Rob Dull › AI Portfolio
AI-assisted workflow · Working prototypes

A product workflow
in nine tools

I built nine browser tools that carry a product initiative from customer context to a draft delivery plan and a Jira import file. Each tool hands a structured record to the next, and code checks the prioritization math, the funding cutoff, requirement coverage, unconfirmed assumptions, batch load, and dependency order. A person starts and reviews every step.

Walk through the PetHealth example ↗ See it step by step ↗ Technical notes ↗
Status. A single-user prototype running on fictional PetHealth data. It hasn’t been used inside a customer organization, and live data connections, signed approvals, and Jira write-back are proposed, not built.

Scope. The tools support Product Management work; designing them was Product Operations work. Background: where this sits between AI assistance and agentic operations, the proposed architecture, how ideas would be sorted and funded, and how the tools were rebuilt.

How it runs. Everything runs in your browser. Tools that call Claude use your own API key, sent straight to Anthropic, and every step can be inspected without a key through a worked example. Records stay in your browser and can be downloaded as a run file. There are no accounts and no server-side records.

The nine steps

Inputs and outputs for each step ↗

Each card says what the tool drafts, what the code checks, and what a person has to review.

  1. 01 · Discover
    Persona Generator

    Shows how a persona card built around a Jobs to Be Done statement is structured, using four samples. No AI call; it doesn't build a persona from your own input yet.

    Review: whether the persona reflects evidence or a convenient composite.

  2. 02 · Map
    Journey Map

    Maps the customer journey stage by stage and passes opportunity seeds to intake. Optional AI fill; opens with a PetHealth example.

    Review: which parts were observed and which are the team’s assumptions.

  3. 03 · Intake
    Initiative Intake

    Four AI calls: weigh the initiative against competing ones, draft candidate epics, record their riskiest assumptions, and propose how to measure benefits.

    Review: invented context, generic epics, and every assumption and metric.

  4. 04 · Justify
    Business Cases

    Drafts a business case for each epic, with a rough effort estimate the prioritizer uses.

    Review: costs, benefits, effort, and strategic claims.

  5. 05 · Rank
    Epic Prioritizer

    Two AI calls propose WSJF and RICE inputs. The tool ranks the epics and draws a funding cutoff.

    Code checks: recalculates every score and ties job size to the business-case effort.

    Review: the inputs. Someone has to own the weighting decision.

  6. 06 · Schedule
    Roadmap & Milestones

    One AI call proposes a milestone plan over up to 18 months, funded epics first.

    Review: capacity, dependencies, commitments, and release assumptions.

  7. 07 · Spec
    Business Documents

    Two AI calls draft a business requirements document (BRD) for one funded epic and mark each requirement explicit, inferred, or assumed.

    Code checks: refuses epics below the funding cutoff.

    Review: non-functional thresholds, stakeholders, and financial claims without evidence.

  8. 08 · Plan
    Backlog Builder

    Five AI calls draft features, stories with acceptance criteria, a solution outline, security and user-acceptance test plans, and documentation.

    Code checks: computes requirement coverage, and won’t accept a BRD until a named person confirms or defers each assumed requirement.

    Review: feasibility, testability, and whether “covered” really means satisfied.

  9. 09 · Deliver
    Build Planner

    Three AI calls draft MVP scope, a batch plan, baseline and worst-case estimates, a RAID log (risks, assumptions, issues, dependencies), and a ready-to-use build prompt for each story in one batch, then export a CSV for Jira’s bulk import. A batch is a regular delivery cycle of 2 days to 2 weeks; the default is 2 weeks.

    Code checks: rechecks batch load and dependency order.

    Review: team capacity, and the CSV before you import it.

Two tools outside the chain

What this shows

How I design a product workflow: what each step hands to the next, where a person has to decide, and which checks belong in code instead of a model. In a production version, the funding decision, requirement sign-off, and the Jira import would stay with named people; and I would recommend full automation for the evidence gathering, formatting, and coverage check.

It doesn’t show customer adoption, production reliability, enterprise integration, security approval, or measured business outcomes.

Limited Evaluation Beta
Test the prototype with a fictional, public, or sanitized initiative.
I want to learn whether the drafts are useful, where context survives the handoffs, where unsupported claims get in, and which controls a real organization would need first.
Evaluation guide ↗
Side Projects
Not everything here is a PM tool
Browser Game
500 Card Night
Live

500 is the card game I've played with the same group of friends since college. It's simpler than bridge, more interesting than euchre. The existing online versions are buggy, clumsily monetized, and don't follow our house rules. I've wanted to build my own since I was working in the Java games division at Nokia. That was twenty years ago. I finally built it in two days with Claude.

No accounts, no server, no database. Share a room code; play from anywhere. Four players, peer-to-peer, entirely in the browser.

Try it solo (no friends required)
Open the game in four separate browser tabs. Each tab joins the same room and plays as a different seat. It's how I test every change: the full four-player game running on one machine, no network required.
TypeScript React / Vite WebRTC / PeerJS Three-repo architecture No backend
Play the game ↗
500 Card Night lobby
500 Card Night play view 500 Card Night end of hand 500 Card Night bidding 500 Card Night bidding round 2
Browser game · In beta testing
Mahjong Night
In beta testing
Work in progress
Mahjong Night is still being built and is in beta testing. Expect rough edges, and expect things to change.

Its main purpose is a tutorial mode for learners that walks through setting up the wall the way it’s done at a real table: rolling to choose East, counting around the table and into the wall to find the break, breaking the wall, and dealing the tiles out in turn. None of the digital mahjong games I’ve found teach wall setup, and it’s the biggest barrier to playing in person.

Hong Kong rules with faan scoring. You can also play a hand against bots; four-player online play is part of what’s in beta testing.

Start here
Open the game and choose Tutorial: deal a hand.
Tutorial mode Wall setup Hong Kong rules TypeScript React / Vite No backend
Try the beta ↗
Mahjong Night tutorial: the wall broken open, with East about to draw the first four tiles
What the tutorial walks through
  • Rolling to choose who sits East, the dealer
  • Counting around the table to pick the wall that breaks
  • Counting into that wall, two tiles at a time, to find the break
  • Breaking the wall and marking the dead wall
  • Dealing in fours for three rounds, then the final round
  • Why East starts with 14 tiles