products,
.See every change before it goes live.
Real-code workflows with a dry run before every change. The AI writes real Python or TypeScript you can read: repricing, tagging, Merchant feeds, B2B price lists. Tell it what you want. 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.
Every job you'd install
another app for.
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.
Test it before it wrecks your store.
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.
Unlock a new era of Shopify operations.
Shopify holds your whole catalog, but it cannot do math on it. So the checks that find money never run: margin per product, prices after every discount, profit after returns. Collaudo adds the missing engine: real code that computes across every product, rehearses on your store, and writes nothing until you approve.
Real catalogs. Real ROI.
Multi-brand fashion retailers running the margin tagger. Same script, two catalogs, budget that follows the margin.
TendenzeAd spend that follows the margin
Tendenze runs 100k+ SKUs on Shopify Plus. One scheduled automation now points their Google Ads budget at the products with real margin behind them.
Gruppo ColluThe automation that paid for itself
Gruppo Collu runs physical stores and a lean online team. Its first automation, the high-margin tagger, returned its cost in 2 weeks.
Collaudo for stores like yours.
Real code on AWS.
- ✓AWS infrastructureReal AWS compute that scales with your catalog, not against it.
- ✓Real Python & TypeScriptActual libraries, actual logic. Your dev or agency can read, edit and version everything.
- ✓EU data residencyData stays in the EU. Standard DPA, full audit trail on every run.
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`); }
Coming from Mechanic?
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.
FAQ
Shopify Flow is free. When is it enough?+
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.
Do I need to write code?+
No. Collaudo's AI writes real Python or TypeScript for you, and the code stays visible and editable, so your dev or agency can review and change it. No code required, real code underneath.
What if the AI gets it wrong?+
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.
What is a dry run?+
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.
Where does my data live?+
In the EU, on AWS infrastructure. Standard DPA available, and a full audit trail records who approved what, when.
Your first automation tonight. Dry-run included.
AI writes real Python or TypeScript, a dry run rehearses it, you approve, it deploys.
Free plan · no card · 🇪🇺 EU data residency