Range Review Prep: what this AI agent does and how to use it

Try the Range Review Prep live demo →

What it does

Merchandisers need numbers before opinions. These agents read your catalog and sales, explain what is and isn't working, and suggest actions. The Range Review Prep agent is the specialist for this job: prepares a category range review: mix, sales, winners, losers and what to add or cut.

Who it's for

Your team. It runs in the console for your staff and through the REST API. It is a team agent, so it is never exposed to shoppers.

Questions it answers

Ask it in any of our languages, for example:

• "Prepare a range review for Women's dresses"
• "Range review for handbags"
• "Range review for Men's shirts"

The live demo shows these as sample prompts in your language, on a fictional store's data.

How it works

1. It understands the question in the language it was written in, even when your catalog is in another language.
2. It looks things up with tools, never from memory:

• Assortment and range analytics
• Sales analytics by category, channel or period
• Read-only sql over your retail data (sandboxed per workspace)

3. It answers from your data, in your brand voice and currency, and shows which tools it used.
4. Rules stay in code. Prices, eligibility and permissions come from your data and settings; the agent explains them and can't invent them. Why this matters.

The data it needs

Products, Orders, Order lines. As soon as these tables are loaded, the agent is ready to switch on. Files can use your own column names: they're mapped automatically. See Connect your data.

Works well with

• Merchandising Copilot: Answers assortment and performance questions with numbers, then recommends actions.
• Best & Worst Sellers: Ranks SKUs and categories by revenue or units for any period and department.
• Assortment Gap Finder: Finds under-represented colours, price points or categories versus demand.
• Product Copywriter: Writes SEO-friendly titles, descriptions and bullet highlights from product data.
• Size-curve Optimiser: Compares sizes sold with sizes bought to suggest a better size curve for the next buy.

Your team can use them side by side in the console, or call each one through the API.

Turn it on

1. Load the data it needs.
2. Install Range Review Prep from the Agent catalog and add your own instructions (guide).
3. Test it in the Playground, in every language you serve.
4. Invite your team or call it from your own systems.

New to ServiceBuddy? Start with how onboarding works.