Upwork Proposal Generator for Shopify Developers

The part that quietly sinks these embed widgets isn't the script tag itself, it's style isolation. Load into a Shopify theme with aggressive global CSS and a WooCommerce site running some ancient page builder, and your widget inherits their font stack, their box-sizing resets, their z-index wars. Shadow DOM plus a scoped stylesheet is the difference between "looks perfect on our test store" and a support ticket from every third retailer.

The Shopify side has its own trap: variant data is never as clean as the docs suggest. Pagination cursors, options that don't map one to one across products, sizing stored in a dozen inconsistent formats. That's exactly where the LLM/vision layer earns its keep, but only if the rules-based parse runs first and the model handles the leftovers, not the whole feed.

Two builds that map closely to your stack:

PerfectMatchSchools (perfectmatchschools.com): React, TypeScript, Supabase ATS built solo, the same schema-normalisation discipline your catalogue ingestion needs.

StockMarketScan (stockmarketscan.com): production Next.js SaaS condensing thousands of noisy tickers into clean structured output through an AI layer, plus a public REST API, shipped revenue-ready.

How I'd move on this:
1. Start with the Supabase schema for products and variants, so both frontend and ingestion agree on shape before anything gets pulled.
2. Build the Shopify sync with cursor pagination and idempotent upserts, then feed only unresolved fields to the vision/LLM step with strict JSON output and retry-on-malformed.
3. Ship the widget in a Shadow DOM boundary, tested against a real Shopify theme and a WooCommerce store early, not at the end.
4. Wire the retailer portal analytics off the normalised tables so embed config and dashboard read one source of truth.

On the existing AI integrations you'd extend rather than rebuild: are the current failures mostly malformed JSON coming back from the vision model, or rate-limit and timeout errors under load?