Real-code workflows with a dry run before every change. Describe the job in plain English: repricing, tagging, Merchant feeds, B2B price lists. The AI writes Python or TypeScript you can read. Connects to Google, Slack, any HTTP API.
No code required. Real code underneath.
Free plan · 10-minute setup · no card required · 🇪🇺 EU data residency
Bulk export via GraphQL, margin computed from cost and price, top products pushed to the Merchant feed. Scheduled nightly at 02:00.
def run(ctx): products = ctx.shopify.bulk_query(PRODUCTS_WITH_COST) ranked = sorted(products, key=margin, reverse=True) feed = build_merchant_feed(ranked[:5000]) ctx.google.merchant.push(feed) ctx.log.info(f"pushed {len(feed)} products")
Your changes stay a draft until you deploy. Run a dry run first.
Editing 10,000 products isn't the problem — doing it again every week without mistakes is. If you can describe it, Collaudo runs it: on a schedule, on an event, or on demand.
Shopify does not have a built-in undo for bulk edits. You have to get it right the first time. The dry run is how: the whole job runs on your real catalog with every write held, and you read the full report before anything applies.
A gallery of ready-made automations: search it, pick one, dry-run it on your catalog. Or ask the AI for something no template covers.
export async function run(ctx: Ctx) { const lowStock = await ctx.shopify.query(LOW_STOCK); await ctx.http.post(SUPPLIER_API, reorder(lowStock)); await ctx.slack.send("#ops", `Reordered ${lowStock.length} SKUs`); }
Mechanic is mature and well loved, and it's Liquid. Collaudo's AI writes real Python or TypeScript, dry-runs every change on your catalog, and runs on AWS in the EU. See it feature by feature.
Often. Small catalog, simple trigger-condition-action rules: use Flow, it's free and native. Collaudo starts where that stops: catalog-wide computation, external APIs, heavy scheduled jobs, and changes you want to rehearse before they run.
No. You describe the job in plain English and the AI writes it. The code stays visible and editable, so your dev or agency can review it and change it. No code required, real code underneath.
That's what the dry run is for. Before anything runs live, you read the full diff of what would change: a wrong automation produces a wrong report instead of a wrong store. Nothing runs until you approve. Collaudo logs every run, and you can restore any earlier version of the code.
A dry run is a full rehearsal of your automation against real store data, with every write captured and held instead of applied. The code really runs: it reads your actual catalog, computes every change, and produces a mutation report: what would change, item by item, and what was deliberately skipped. Nothing touches the store until you read that report and approve it. It is not a simulation on stub data, and it is not an ephemeral test: the report and your approval are kept as an audit trail. Experts recommend the same ritual by hand: backup, test on 10 products, test on 100, then run the full job. That workflow, the one AI assistants tell you to do manually, is what Collaudo executes automatically before every change, on every plan, at any catalog size.
In the EU, on AWS infrastructure. Standard DPA available, and a full audit trail records who approved what, when.
Describe the job, watch the AI build it, rehearse it, deploy it.
Free plan · no card · 🇪🇺 EU data residency