A low-code experiment: using Retool to bulk identify and fix product issues in Shopify

I am working with a Shopify merchant at the moment that has a large inventory (over 6,000 SKUs).

Obviously, this brings multiple challenges—particularly regarding merchandising and categorizing products. At this scale, it’s essentially impossible to manually identify or fix issues with product information—especially with the interfaces built into Shopify.

So, I have been playing with a bunch of tools—looking for the ultimate tech stack that might make it easy to work with large numbers of products efficiently.

I know there are many platforms and services that can help with the problem I am trying to solve; but, I am finding that a lot of the work I am doing is one-off or doesn’t quite fit in the box of these platforms.

Most platforms such as PIM (product information management) systems have high learning curves and come with a heavy headspace cost for brands. And, ideally, I don’t want my clients to have to sign up for more tools and services.

Lastly, because I am curious by nature and I am extremely bullish on no-code and low-code methodologies at the moment, I wanted to see if I could do this myself using open-source and/or freemium tools.

I have looked at and tried stitching together countless systems including complex Google Sheets, Make (formerly Integromat), Zapier, Fivetran, Plytix, Sales Layer, Gadget, Sourcetable, and many others!

After a lot of trial-and-error, and numerous calls and product demos, I am getting close to a solution:

  1. Supabase for the Postgres database
  2. Airbyte to replicate the date from Shopify
  3. Retool to build queries and interfaces to work with the data

Here’s a quick demo:

If you have questions or thoughts, feel free to reach out on Twitter or LinkedIn.

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