Can AI Update 87 Closet Accessory Prices in Minutes?

by John Gallagher, Co-Founder of Nesting Systems and Closet Institute of America Board Member

Executive summary

A closet company owner needed to replace 87 placeholder prices in TAG Hardware’s ClosetPro spreadsheet. Using the item numbers already in the file and Häfele USA’s publicly displayed suggested retail prices, we used an AI agent to look up, transfer, and validate every price—in 8 minutes! This saved a whole lot of repetitive copying and pasting.

Contour-CLOSETPRO-1.1.0-priced_public.xlsx

Download XLSX • 27KB

Last updated August 22, 2026. The workbook contains Häfele USA suggested retail prices displayed on the date of collection. Please verify current pricing before preparing customer quotes or making purchasing decisions.

If you only needed the spreadsheet, you’re all set. If you’re curious about how the process worked—and what it might demonstrate about practical AI use in a closet business—keep reading.

The question that started the experiment

A closet company owner recently posed a familiar problem in a Facebook group.

TAG had revamped its accessory line, and ClosetPro had added the new products to its catalog. However, the company owner still needed to establish pricing for each product.

ClosetPro made the catalog data available as a downloadable spreadsheet, which was certainly better than entering every product directly into the software. But the spreadsheet still contained placeholder prices.

That left someone with a decidedly unglamorous job:

1. Copy a Häfele item number from Excel.

2. Paste it into the Häfele USA website.

3. Find the suggested retail price.

4. Copy the price.

5. Return to Excel.

6. Paste the price into the correct row.

7. Repeat dozens—or potentially hundreds—of times.

   
   

Nothing about that process is particularly difficult. It is simply repetitive, time-consuming, and vulnerable to small human errors.

That made it an interesting candidate for AI.

First, we examined the spreadsheet

Before doing any pricing work, the AI agent inspected the workbook to determine whether the process could be completed reliably.

The file contained:

• 87 products requiring prices

• A unique TAG SKU for each product

• A corresponding Häfele item number

• Product descriptions and finishes

• A Price column containing 9999.99 placeholders

• Empty Multiplier and Total columns

  
  

The Häfele item numbers were the critical piece. They provided stable identifiers that could be matched against an authoritative pricing source.

This meant the job did not require the AI to “guess” which products belonged together. It could search for an exact item number, confirm the product, collect the displayed price, and return the result to the corresponding spreadsheet row.

Establishing the pricing rule

Before processing the entire file, we tested three products manually through the AI-controlled browser.

The instruction was specific:

Use the “Suggested Retail Price” publicly displayed on the Häfele USA website.

That distinction matters. A supplier may display several different numbers, including suggested retail price, account-specific cost, promotional pricing, or dealer pricing. Without a clearly defined rule, even a technically correct lookup could produce the wrong business result.

The three-item test confirmed that Häfele’s US website displayed:

• The exact item number

• The suggested retail price

• The selling unit, such as piece or set

• The corresponding product description

  
  

Once those fields could be collected consistently, the remaining products could be processed as a repeatable workflow.

The result

The AI agent then processed the full workbook.

It:

• Looked up all 87 Häfele item numbers

• Collected the displayed US suggested retail price

• Confirmed the product and selling unit

• Replaced every 9999.99 placeholder

• Preserved the workbook’s existing structure and formatting

• Left the Multiplier and Total columns unchanged

• Confirmed that all 87 completed prices were numeric

• Confirmed that no placeholder prices remained

• Reopened and visually checked the completed workbook

The repetitive lookup and spreadsheet work was completed in minutes rather than the hours it could reasonably take someone working through the list manually.

AI still needed judgment and verification

The process also produced a useful example of why AI automation should include controls rather than blindly copying whatever appears on a screen.

One workbook entry listed Häfele SKU 807.78.741. Häfele returned no result for that number.

The surrounding products followed the 807.87.xxx numbering pattern, so the agent investigated instead of skipping the row or inventing a price. Häfele item 807.87.741 matched the workbook’s product description, size, finish, product family, and neighboring SKU pattern.

The matching item was a 5-inch Winter drawer divider priced at $37.18 per set.

The verified price was entered, but the original SKU was left unchanged so that the source workbook was not silently rewritten.

That exception may have taken longer than a routine lookup, but it is also where a controlled AI workflow becomes more valuable than basic copy-and-paste automation.

What this demonstrates for closet companies

This was a small project, but the underlying lesson applies to many parts of a closet business.

AI is especially useful when a task has:

• A clearly defined starting file

• Stable identifiers such as SKUs or order numbers

• An authoritative information source

• Repetitive actions

• Explicit rules for handling exceptions

• A final result that can be independently checked

Potential examples include vendor price updates, product-catalog cleanup, hardware cross-references, purchase-order comparisons, job-cost reviews, proposal auditing, and spreadsheet normalization.

The goal is not to remove human judgment. It is to stop spending human time on the portions of a job that do not require it.

A practical experiment from Nesting Systems

At Nesting Systems, we are exploring practical ways AI can remove repetitive administrative work from closet and cabinet businesses.

This experiment was not about producing an elaborate AI demonstration. It was about solving a real problem raised by a real closet company owner—and turning several hours of tedious work into a short, controlled, and verifiable process.

Sometimes the most useful applications of AI are also the least flashy.

They simply give business owners their time back.

This resource is provided for informational purposes. Pricing was collected from publicly displayed Häfele USA suggested retail prices on the date shown and may change without notice. It was cross-checked by a human using random sampling selection for price verification. Verify all pricing before quoting, selling, or purchasing. Nesting Systems is not affiliated with or endorsed by TAG Hardware, Häfele America Co., ClosetPro Software, or their respective owners.