Competitor price monitoring, delivered as a dataset

Three steps: find who actually sells your products, read the price and stock from each product page, and repeat on a schedule so you have a history rather than a snapshot. Most tools skip step one and make you paste URLs. Here a search pass discovers the sellers, extraction reads price, stock and seller per page, and a scheduled batch re-runs the same list as often as you need.

It's comparing your prices against the same or equivalent products sold by competitors, over time, to see where you are cheap, expensive or invisible. The analysis is only as good as the underlying rows: you need the seller, the exact product match, the price, whether it was in stock, and when it was observed. Those five fields are what this dataset returns.

A retailer selling a coffee grinder at $199 checks the eight other shops carrying it, finds the median at $179 with two sellers out of stock, and drops to $184 — still above the cheapest, but visible in comparison results. The inputs are one row per seller per day: product, seller, price, stock, timestamp.

Price scraping is the automated collection of prices published on public shop pages, converted into structured rows — product, seller, price, stock, timestamp. It's how retailers, brands and analysts see a market's real prices instead of the ones in last quarter's report.

Prices shown publicly to any visitor are generally lawful to collect in most jurisdictions — they're facts, not creative works. Site terms and local law still vary, and how you use the data (e.g. price-fixing) has its own rules, so check with counsel for your market.

Any shop with public product pages. Discovery is search-driven — Quantic AI queries three engines plus Google Shopping to find who actually sells a product, so long-tail shops appear alongside the big marketplaces. Hard targets escalate to a rendering browser automatically; a page that still blocks costs nothing.

As often as you rerun it. The one-off pipeline becomes a schedule through batch jobs — up to 1,000 URLs per job with webhooks or incremental polling — so daily or hourly refreshes of a fixed URL set are just repeat calls at $0.0002 per page.

Whatever the page shows: title, price, currency, stock status, shipping cost, rating, review count, seller name, product URL and collection timestamp. You name the columns; structured extraction returns exactly those fields, validated and typed.

In practice they're the same activity at different frequencies: tracking usually means following a defined product list over time, monitoring includes discovering who sells and at what price right now. This pipeline does both — discovery by search, then scheduled re-scrapes of the URLs it found.

With Quantic AI, planned pricing: $0.03 per record on the first build, then $0.01 per refreshed record — a 500-product daily check runs about $150 a month, contract-free. Developers can meter the same URLs through the raw APIs at $0.0002 per page, about $3 a month, writing their own extraction and diffing.