Monitor competitor price shifts with Apify, Postgres, OpenAI, and Slack

Description

Quick overview This workflow runs every morning to scrape competitor product pages with Apify, store daily price snapshots in Postgres/Supabase, detect unusual price moves using SQL views, and post actionable repricing commentary (plus scraping health warnings) to a Slack channel. How it works Every morning it reads your list of competitor product URLs and sends them to the Apify E-commerce Scraping Tool, which handles anti-bot, proxies and per-retailer extraction. There is no scraper to maintain here. A normalize step reduces per-retailer differences to one shape. Prices are parsed without assuming a locale, so 1.299,99 and $1,299.99 both read correctly. Currency is stored as an ISO code rather than a symbol. Stock keeps three states, because "not reported" is not the same as "out of stock". One row per product per day goes to Postgres. A view compares today against the trailing 30-day average and the 90-day low and reports transitions, not states, so a rival who cuts a price and holds it is reported once rather than every morning. It stays silent for the first 14 days, because a baseline of one day is noise. An AI agent turns what moved into a recommendation and posts it to Slack. A second branch reports which URLs returned nothing, so a broken page cannot look like a quiet market. Setup Add the Apify community node. On n8n Cloud, search for it on the canvas; your instance owner must have verified community nodes enabled. Self-hosted, install @apify/n8n-nodes-apify under Settings, Community nodes. Run the schema in supabase_schema.sql (linked in the sticky note) against Postgres 15 or later. It creates a private schema with RLS enabled, so the table is never exposed through Supabase's Data API. Add credentials: Apify, Postgres, a chat model, Slack. Open Pages to watch and replace the example URLs with yours, and set your Slack channel. Check your instance timezone. The schedule says 06:00 and n8n reads that in the instance timezone. Run once manually and confirm a row landed in pricing.price_history. Requirements An Apify account. Postgres 15 or later, or a Supabase project. Version 15 is the floor because the views use security_invoker. A chat model credential and a Slack workspace. Customization The detection thresholds live in one SQL view: the 5% band against the 30-day average, the 90-day low and high, and the 14-day baseline gate. Change them there and the agent's behaviour follows, because detection is deterministic and the model only writes. Swap Slack for email, Sheets or a webhook by replacing one node. To watch a category rather than fixed products, run the Actor in keyword mode once to harvest URLs, then pin them: keyword results drift daily and cannot produce a price series. An n8n automation workflow template by Apify.

Community Metrics

Categories

• Automation
• AI

Tags

• n8n
• workflow
• postgres
• slack
• code
• ai-agent
• openai-chat-model

Capabilities

• 5 nodes

Information