Can the AI fix the product data as well as find the problems?

It can draft the corrections, and with write access it can apply them. We recommend starting read-only: review the fix list first, then decide what to automate.

How big a catalogue can this handle?

The AI works through the catalogue in batches, so size is rarely the limit. For very large catalogues, run the sweep per collection or per vendor.

AI Use Cases

Quick winBusinessSaves days of catalogue auditing per quarter

Shopify SKU mapping and product data checks

Let AI sweep your Shopify catalogue for missing or duplicate SKUs, inconsistent attributes and broken variants, then hand you a fix list.

The problem

Product catalogues drift: SKUs get duplicated or left blank, size and colour attributes are written three different ways, and variants point at the wrong options. Each error is small, but together they break reporting, stock syncs and ad feeds, and nobody has time to audit thousands of products by hand.

The workflow

1
Connect Shopify
Add the Shopify connector so the AI can read your full product catalogue including variants and attributes.
2
Define your catalogue rules
Tell the AI what good looks like: SKU format, required attributes, naming conventions for sizes and colours.
3
Run the sweep
Ask for every product that breaks a rule: missing SKUs, duplicates, inconsistent attributes, variants with missing options.
4
Review the fix list
Get the issues as a prioritised list, optionally written into a Google Sheet for the team to work through.
5
Re-run after fixes
Run the same sweep weekly to catch new drift before it spreads.

Start here

Once your connectors are set up, this prompt kicks the whole workflow off. Adjust the details to match your business.

Paste this into your AI tool

Audit my Shopify product catalogue. Check every product and variant for: missing or duplicate SKUs, inconsistent size and colour attribute naming, variants with missing options, and empty descriptions or images. Give me the issues as a table ordered by severity, with the product name, the problem and the suggested fix.

The outcome

A prioritised, specific fix list for your whole catalogue, produced in one session.

Time saved: days of catalogue auditing per quarter

What it needs

The connectors this workflow uses, and how it runs on each AI platform.

Connectors

Shopify Google Sheets optional

Works on

Last verified 18 October 2026

Copilot (free)	Not possible	The consumer app cannot add custom MCP servers.
Copilot Pro	Not possible	Drafted, to verify.
Microsoft 365 Copilot	Build required	Via declarative agents; MCP tool calls consume Copilot Credits.
Copilot Studio agents	Build required	Needs a Copilot Studio licence.
Claude Free	Works natively	Limited to 1 custom connector.
Claude Pro/Max	Works natively
Claude Team/Enterprise	Admin setup needed	Owners add custom connectors.
Claude Code	Works natively
Gemini Enterprise	Admin setup needed	Custom MCP blocked by default org policy; not on Business edition.

Who it's for

Retail & eCommerce Operations Marketing

The reusable skill

The reusable recipe: write your data quality rules down once, give AI read access to the records, and ask for violations rather than summaries. This works for any structured catalogue: products, CRM contacts, asset registers, course catalogues.