How to Actually Use AI When You Run a Square Store
AI can change how you work as an independent retailer — but only if you use it the right way. Here are three concrete levels, from easiest to most impactful.
ChatGPT, Claude, Gemini — everyone's talking about them. But for a retailer running their store with a Square POS every day, how do you go from impressive demos to genuine time savings? We've observed which use cases actually work, and which ones aren't worth the effort.
Here are three concrete ways to bring AI into your daily workflow, ranked from most accessible to most powerful.
1. Keep a fixed prompt for a consistent visual identity
The first mistake most retailers make with AI: starting from scratch every time. A vague prompt, a mediocre result, try again differently — and after a few weeks, your product visuals are all over the place.
The fix is simple: define your visual direction once as a prompt, then reuse it systematically.
Example visual direction prompt:
Save this in a notes app or text file. Paste it at the start of every new image generation session, then just add a description of the product you're shooting.
The result: all your Square product listings have immediate visual coherence. A customer browsing your online catalog recognizes your aesthetic instantly — even if photos were generated across several months.
"This is exactly what major brands do with their brand style guides. AI lets you achieve that same level of consistency without an agency or recurring photography budget."
- Field observation, Nearby
2. Turn supplier invoices into a product catalog in minutes
This is the use case that surprises retailers the most — and the one that saves the most time in practice.
Every delivery comes with an invoice. That invoice already contains everything you need: product references, names, quantities, unit prices. But that data is locked inside a PDF or a paper sheet, and has to be manually re-entered into Square.
AI can do that work for you. Here's how:
Photograph or export your invoice as a PDF
A clear phone photo is enough. Modern AI reads documents well, even handwritten or slightly blurry ones.
Ask for a structured table
Drop the photo or PDF into ChatGPT, Claude or Gemini, and ask: "Extract all products from this invoice and create a CSV table with columns: product name, reference, quantity, unit price (excl. tax)."
Import into Square
Square accepts CSV imports for your product catalog. A few clicks, and your new items are created — no manual data entry.
2 – 4 hrs
saved per week for a store with 2 to 3 supplier deliveries, just by eliminating manual invoice entry.
This method works well for occasional use. But it has its limits: you still need to manually reformat the CSV to match Square's expected columns, review every row, and repeat the process for each new invoice. For a store with multiple suppliers and frequent deliveries, it stays tedious.
3. The core limitation of general AI: it doesn't know your store
The two use cases above are real and useful. But they also reveal the main limitation of general-purpose AI tools like ChatGPT: they have no knowledge of your store's context.
Every time you generate an image, the AI starts from zero. It doesn't know your store has a bohemian aesthetic, that your customers are mainly 25–40 year-olds, or that you mostly carry sizes S and M. You have to explain it all again.
Same with product listings: AI can extract invoice data, but it has no idea how you name your Square categories, what your internal SKU format looks like, or what margin you typically apply.
This is also where a specialized copilot makes a real difference for the catalog itself: categorizing products, fixing SKUs and catching inconsistencies before they propagate through your Square catalog.
What you have to redo manually every time with a general AI:
- →Re-explain your visual direction and brand aesthetic
- →Reformat the CSV to match Square's column structure
- →Correct category names that don't match your catalog
- →Adjust prices to match your pricing policy
- →Rewrite descriptions to match your store's tone
That's exactly why a specialized service like Nearby changes the equation: your store's context is retained across sessions. Your visual direction, your categories, your description style, your Square import format — all memorized. Every new invoice, every new product, every new image starts from a base that already knows your business.
"The real promise of AI for retailers is never having to explain the same thing twice. A service that knows your store — that's where the actual time savings happen."
What we built at Nearby
Where to start
If you're on Square and want to start getting value from AI, pick up the first two points this week: define a visual direction prompt, and test extraction on your next supplier invoice. You'll see the time savings immediately.
When you're ready to go further — and stop starting from zero every single time — that's where Nearby comes in.
If your current challenge isn't the invoice but the quality of your catalog, start with the AI product copilot page.
Nearby knows your Square store
Invoice extraction, product image generation, complete listings — with your store's context baked into every operation. No re-entry, no starting over.