Size Curve by Region: what this AI agent does and how to use it
Builds size curves per city so buys match local size demand. Who it's for, the questions it answers, the data it needs and how to switch it on.
Try the Size Curve by Region 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 Size Curve by Region agent is the specialist for this job: builds size curves per city so buys match local size demand.
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:
- "Size curve by city"
- "Which city buys more large sizes?"
- "Size split for our top 3 cities"
The live demo shows these as sample prompts in your language, on a fictional store's data.
How it works
- It understands the question in the language it was written in, even when your catalog is in another language.
- It looks things up with tools, never from memory:
- Read-only sql over your retail data (sandboxed per workspace)
- It answers from your data, in your brand voice and currency, and shows which tools it used.
- 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
Orders, Order lines, Customers. 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.
- Range Review Prep: Prepares a category range review: mix, sales, winners, losers and what to add or cut.
Your team can use them side by side in the console, or call each one through the API.
Turn it on
- Load the data it needs.
- Install Size Curve by Region from the Agent catalog and add your own instructions (guide).
- Test it in the Playground, in every language you serve.
- Invite your team or call it from your own systems.
New to ServiceBuddy? Start with how onboarding works.
Try it, and related AI Agent Buddies
Merchandising & assortment
Size Curve by RegionBuilds size curves per city so buys match local size demand.
Merchandising & assortment
Merchandising CopilotAnswers assortment and performance questions with numbers, then recommends actions.
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Best & Worst SellersRanks SKUs and categories by revenue or units for any period and department.
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Assortment Gap FinderFinds under-represented colours, price points or categories versus demand.
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Product CopywriterWrites SEO-friendly titles, descriptions and bullet highlights from product data.
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Range Review PrepPrepares a category range review: mix, sales, winners, losers and what to add or cut.
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Size-curve OptimiserCompares sizes sold with sizes bought to suggest a better size curve for the next buy.
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Trend ScoutSpots what is accelerating in your own sales: categories, colours and brands growing fastest.
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Range Rationalisation AdvisorIdentifies tail SKUs to delist using sales, stock and range overlap, with the revenue at stake.
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Festive Season Range PlannerPlans festive ranges (Ramadan, Diwali, Lunar New Year, Christmas) from category performance and catalogue depth.
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Accessory Attach PlannerFinds which accessories sell with each hero category and plans what to range beside it.
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Shade Range PlannerCompares shade (colour) sales with the shades stocked to plan the beauty range.
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Store Clustering Engine: what this AI agent does and how to use it
Groups stores by what sells where, so ranges and space can be tailored for each cluster. Who it's for, the questions it answers, the data it needs and how to switch it o…
Lookbook Builder: what this AI agent does and how to use it
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Finds which accessories sell with each hero category and plans what to range beside it. Who it's for, the questions it answers, the data it needs and how to switch it on.
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Compares shade (colour) sales with the shades stocked to plan the beauty range. Who it's for, the questions it answers, the data it needs and how to switch it on.
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Plans festive ranges (Ramadan, Diwali, Lunar New Year, Christmas) from category performance and catalogue depth. Who it's for, the questions it answers, the data it need…
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Identifies tail SKUs to delist using sales, stock and range overlap, with the revenue at stake. Who it's for, the questions it answers, the data it needs and how to swit…
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Prepares a category range review: mix, sales, winners, losers and what to add or cut. Who it's for, the questions it answers, the data it needs and how to switch it on.
Size-curve Optimiser: what this AI agent does and how to use it
Compares sizes sold with sizes bought to suggest a better size curve for the next buy. Who it's for, the questions it answers, the data it needs and how to switch it on.
Trend Scout: what this AI agent does and how to use it
Spots what is accelerating in your own sales: categories, colours and brands growing fastest. Who it's for, the questions it answers, the data it needs and how to switch…
Product Attribute Enricher: what this AI agent does and how to use it
Suggests missing attributes (colour, occasion, season, style) and better descriptions from product data. Who it's for, the questions it answers, the data it needs and ho…
Merchandising Copilot: what this AI agent does and how to use it
Answers assortment and performance questions with numbers, then recommends actions. Who it's for, the questions it answers, the data it needs and how to switch it on.
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