AI Product Data Cleaner for E-commerce

AI Prompts to Build This

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1. 1. Project Setup

   Build the weekend MVP of "ProductDataDoctor": upload a messy supplier file and get a clean catalog you can export for Shopify.
   
   Stack: Next.js (App Router), TypeScript, Tailwind, Supabase (Postgres, Row Level Security, Storage, Auth with email magic link), Claude (Anthropic API) with structured output for the extraction, pdf-parse and xlsx for reading files. Deploy on Vercel.
   
   Tables (Row Level Security on, each user reads only their own catalogs):
   - catalogs(id, user_id, name)
   - raw_uploads(id, catalog_id, source_type, file_url, status) where source_type is one of csv, excel, pdf, email_text
   - normalized_products(id, catalog_id, upload_id, sku, title, unit_value, unit_type, price_cents, spec_json jsonb, confidence, status) where status is one of clean, needs_review, duplicate
   - review_queue(id, product_id, issue_type, suggested_fix jsonb, resolved)
   - correction_history(id, catalog_id, field_name, original_value, corrected_value, created_at)
   
   Screens: /login, /catalogs/[id] (uploads and clean products), /catalogs/[id]/review.
   Env vars (names only): NEXT_PUBLIC_SUPABASE_URL, NEXT_PUBLIC_SUPABASE_ANON_KEY, SUPABASE_SERVICE_ROLE_KEY (server only), ANTHROPIC_API_KEY.
   
   Do not build: billing, plans or SKU limits, a job queue (process small files inline and show the status), scheduled re-sync, the Amazon export, a PIM connection.
   Done when: npm run dev starts, you can sign in, and the five tables exist with Row Level Security on.

2. 2. Core Feature

   Build the one feature that proves the idea: a messy file in, a clean exportable catalog out.
   1. POST /api/upload takes a CSV, an Excel file or a PDF, stores it and processes it inline, updating raw_uploads.status as it goes.
   2. Extract rows: for CSV and Excel, parse directly with a header-mapping guess. For a PDF, run pdf-parse and send the text to Claude with a structured schema { sku, title, unit_value, unit_type, price, specs }.
   3. Normalize each row: convert units to metric, flag a row as duplicate when its SKU fuzzy-matches an existing product above 90%, and flag any row missing a title, SKU or price.
   4. Rows with confidence under 0.85 on any field go to review_queue and not straight to normalized_products.
   5. GET /api/catalogs/[id]/review lists the queue. POST /api/review/[id]/resolve accepts or overrides a suggested fix and writes it to correction_history, which later extractions use as few-shot examples.
   6. GET /api/catalogs/[id]/export?format=shopify|csv returns only status clean rows, mapped to Shopify's product import columns (Handle, Title, Body (HTML), Vendor, Type, Tags, Variant SKU, Variant Price, Variant Grams), with a count of the rows left out for review.
   Empty state: with no uploads, show the upload box and the three supported formats.
   Done when: a messy supplier CSV and a PDF price list both produce clean rows, ambiguous units land in the review queue, and the Shopify export lists only clean products.

3. 3. Landing Page

   Build a one-page landing site for ProductDataDoctor, a supplier data cleaner for e-commerce sellers.
   Hero: "Turn any supplier file into a clean catalog." Sub: "CSV, Excel, PDF or a pasted email in. A standardized, marketplace-ready catalog out." One button: Join the waitlist.
   Sections: the problem (every supplier sends a different mess), how it works in four steps (upload the feed, the AI normalizes it, you confirm the edge cases, export or sync), a before and after of a messy row and a clean one, and an FAQ on accuracy, what needs your review and supported formats.
   Waitlist: store the email in a waitlist table in Supabase. No other service.
   Voice: practical, written for a wholesaler or seller.
   Done when: the page renders on a phone and a submitted email appears in the waitlist table.

4. 4. Branding Package

   Use a design or image tool for this one. A coding agent cannot draw a logo.
   Brand for ProductDataDoctor: a wordmark and an icon that suggest a tidy table with a check mark.
   Colors: off-white, near-black, one calm green and an amber for needs-review. Type: one clean sans for the interface and a mono for SKUs.
   Deliverables: wordmark, icon, a clean and a needs-review status badge, and one launch graphic.
   Done when: each deliverable is saved in one folder and the two badges are distinguishable without color.