An AI team for a shop of one

Task

I designed and built an AI team of 14 roles for a small online business, and the dashboard she runs her shop from.

  • Strategy

    A team of AI roles, one chat each, led by a CEO role

  • Design

    A dashboard with 5 tabs and 12 written design rules

  • Role

    Freelance Product and Brand Designer

A freelance project since July, for a small online business run by one person. I set up her AI team in July, rebuilt it in August and grew it to 14 roles in September, and I designed and built the dashboard it works in. She writes to each role in her own words; the dashboard shows what’s done and what’s waiting on her.

  • 14 roleseach in its own chat
  • 5 tabsin one dashboard
  • Every Mondayfresh shop stats
The client dashboard on desktop, My tasks tab, with sample data: a board of tasks with tags, due dates and priorities
The same dashboard on a phone, Products tab: one checklist per product
Dashboard for the client, with sample data.
The results

The owner says work that used to take her weeks now takes days.

August and September sales were 2.3 times the same two months last year

Same two months, a year apart, so the season is the same.

August and September sales against the shop’s January to June average.
Monthly sales in 2026 against the January to June average January to June averaged 1.0. August was 2.6 and September 2.9 times that average. January to June average I started mid July Jan 2026: 0.9× the January to June averageJanFeb 2026: 0.7× the January to June averageFebMar 2026: 0.3× the January to June averageMarApr 2026: 1.2× the January to June averageAprMay 2026: 1.9× the January to June averageMayJun 2026: 1.0× the January to June averageJunJul 2026: 1.5× the January to June averageJulAug 2026: 2.6× the January to June averageAugSep 2026: 2.9× the January to June averageSep2.6×2.9×
Monthly sales in 2026 against the January to June average
MonthTimes the average
Jan0.9
Feb0.7
Mar0.3
Apr1.2
May1.9
Jun1.0
Jul1.5
Aug2.6
Sep2.9
From the shop’s own sales stats, shown against its January to June average instead of dollar amounts. I started in mid July, so August is the first full month. October isn’t finished, so it isn’t shown.
2.3×August and September sales against the same two months last year, the shop’s two best months on record.
20252026
August and September sales, 2025 and 2026 August 2026 was up 332% on August 2025, September 2026 up 65% on September 2025; together 2.3 times. August 2025August 2026, +332% on 2025+332%AugustSeptember 2025September 2026, +65% on 2025+65%September
August and September, 2026 against 2025
MonthChange
August+332%
September+65%
From the shop’s own sales stats, shown without dollar amounts. September 2025 includes one unusually large single order, so September’s rise is understated.
What the AI built, what I changed
Version 1

A static status report: one page, no tabs, rebuilt by hand.

Version 16

The tool she runs the shop from: five tabs, live data, and it opens on her own tasks.

The AI never got it right in one go. I reviewed every version and sent it back until it worked, then wrote each fix down as a rule with a check, so it can’t come back.

  • What the AI builtWhat I changedThe rule
  • What the AI builtA static page read from one fileWhat I changedA live database, a dark layout that works like an app, and a Refresh buttonThe ruleRules 1, 2, 4
  • What the AI builtA tile that said “Cause not identified.” while the data said otherwiseWhat I changedStatus lines come from the data, never typed in by handThe ruleRules 5, 6
  • What the AI builtOne long page with the team’s items firstWhat I changedTabs, and her own tasks firstThe ruleRules 7, 8
  • What the AI builtPure white text and tight paragraphsWhat I changedSofter colours with measured contrast, and body text at 15pxThe ruleRule 9
  • What the AI builtA Products tab with filters, card grids and counters on every itemWhat I changedOne view at a time, counters only where something gets counted, and ticks you can undoThe ruleRule 10

Version 11. I gave the website project its own tab, 31 steps in six phases, so it never mixes with her shop tasks.

How a decision reaches her

Step 1, a role reports back. Every role reports back in the same few words, so she can see at a glance what is done, what is blocked and what needs her.Step 2, the CEO role ranks what needs her. Only decisions reach the owner. Each one says why it matters, what it costs her and what happens if it waits, ranked so the items holding up other roles come first.Step 3, she decides what joins her own list. Team items never land in her task list on their own. She taps Add to my tasks, and it joins her list with a link back to where it came from.
The team’s first month

What the team got done in its first month

  • 43live listings rewritten to meet the marketplace’s policy, every active one
  • 14new pieces priced in two days
  • 16cards written for custom orders
  • 192competitors tracked, 47 shops read in full

The team did the reading, writing and checking; she pasted every listing fix and sends every buyer reply herself.

My part

I designed the team: every role’s job, its rules and how work moves between them. I designed the dashboard and built it with Claude: I set the structure, the layout and the 12 rules, a Project Manager role I set up wrote the code, and I reviewed all 16 versions and sent each back until it worked.

Her part

The roles find, draft and flag; she makes the calls.

Built in

Claude: one shared project with a chat for each role, and a dashboard with its own database.

The problem

One person, every job

She makes, packs, lists and markets everything herself. Switching between jobs, not making, was the limit, and her shop stats were often weeks old.

BeforeThree apps and old numbers
  • Notes appIts free plan was too limited
  • Task appShe jumped back and forth
  • Stock sheetCounts kept in a spreadsheet
  • Shop statsTwo weeks old in mid September
AfterWhat replaced them
  • One dashboardHer tasks, products, website plan, studio and research, in five tabs
  • Stats every MondayRead from her shop’s own stats pages, on a schedule
The dashboard

A dashboard that opens on her tasks

I designed and built it. She uses it every day. I reviewed every version and turned each fix into a written rule with a check that every new version must pass.

  • 5 tabs
  • 12 design rules
  • 79product records, one checklist each
  • 16versions, each checked on desktop and phone

(Counts from the dashboard, October 2026.)

Dashboard for the client, with sample data in place of the shop’s figures. Every tab works; nothing you change is saved.

  • A · DecisionHer tasks come first

    The first tab is her own task list. Team items join it only when she taps “Add to my tasks”.

  • B · DecisionOne checklist per product

    Each product has four checks: what she still needs to decide, stock, packaging and the listing. Sold out items stay hidden until she adds them back.

  • C · DecisionCharts only from real figures

    One data point shows as one point, not a trend. Every status line comes from the data.

Rule 10One view at a time

Added after I found the Products tab overwhelming to use. Each tab shows one thing, and every field means one thing.

Rule 9: no pure white text and body text 15px or larger, with contrast of at least 5.4:1 against the 4.5:1 WCAG AA asks for.

How it works

Each role is a chat with one job

A CEO role plans the work and hands it out. Every other role does one job, keeps its own files and reports back with done, blocked or needs a decision.

  1. 01One writer per file

    A map names who writes every file. It came after three kinds of lost work, such as a stale copy saved over newer edits.

  2. 02One browser at a time

    Only the role assigned to it uses her logged in browser, and it notes when it starts and stops.

  3. 03Decisions stay decided

    Once she decides something, it’s written down, and the team doesn’t reopen it unless something new comes up.

A real question

Is the marketplace hiding the shop?

First, the marketplace removed her best selling listing for a policy issue. The team flagged restricted keywords on her other listings, then read all 43 live listings and rewrote each one; she pasted every fix, so the rest of the shop wouldn’t be hidden or removed too. A few days later, with sales still slow, she asked the team this question.

The answer

No penalty on the shop as a whole. The drop started before the removal.

Visits had started falling about 12 days before the removal, across almost every listing. The removal was a second hit on top. Saves per visit held, so shoppers were still interested; fewer of them bought.

How the team checked
  1. 1Checked for a penalty

    No warning, no new violation

  2. 2Cut the data into weeks

    Monday to Sunday, visits by source

  3. 3Compared every listing

    Two weeks against the two before

  4. 4Compared with last year

    Last year dipped in the same weeks

  5. 5Found where it started

    Across almost every listing, before the removal

She caught a gap

Her recent price rises weren’t in the answer. The team checked live prices against a dated snapshot: the raised listings kept selling, so prices didn’t explain the fall.

New rule “List every price change before explaining any change in sales.”
How it improves

Each mistake is written down as a rule

When something goes wrong, the team writes down what happened and a rule that stops it happening again. New roles are added the same way: only when a written condition is met.

2.5×

The keyword tool guessed about 2.5 times as many sales as the shop really made. The Researcher caught it, and the team stopped using those guesses.

Roles are added only when needed: Customer Service came in when buyer messages took more than an hour a week.

  • What went wrongRule written
  • A listing was reported blocked. It was live.Check the live listing before giving her a task
  • Two roles used the same browser at once.One browser holder at a time
  • Conversion was called “back in range” when it wasn’t.Monday to Sunday weeks only; compare like with like
  • A dashboard tile showed fixed text that contradicted the data.Status lines come from the data, never typed in by hand
  • A sales answer left out recent price changes.List every price change first
What changed

What changed for her

One place for her work, and answers she can check against the numbers.

  • Fresh stats every Monday

    Her shop stats now arrive every Monday on their own, and her task app, notes app and stock sheet moved into one dashboard.

  • Answers she can check

    When sales dipped, the team checked for a penalty, compared every listing and last year, and showed where the drop started.

  • She asks in her own words

    She writes to the role she needs, in her own words.

“When sales dipped, I used to have no idea why. Now it checks the numbers and every listing each week, shows me the problem and how to fix it, and I just tell it what I need instead of juggling three apps.”

The owner

Name withheld at the client’s request.

More on how I use AI in my design work

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