Start with what you actually mean to buy, then use the web app to filter noise out of the result set quickly.
How the flow works
Discovery on the landing. Decisions in the app.
AliPriceTracker is built around one idea: SEO pages should capture intent, and the app should convert that intent into a fast shortlist and a disciplined watchlist.
Step by step
What a strong user journey looks like.
Discounts alone are not enough. Rating, orders, and promo presence help separate worthwhile listings from weak ones.
Good filter packs usually repeat. Saved searches turn that into one tap instead of rebuilding the same query tomorrow.
The web watchlist records price history and checks again on the serverless timer contour.
Why this contour is safe
The web product expands capability without destabilizing the bot.
The architecture is intentionally conservative: separate tables, cheap Azure primitives, and no dependency that forces changes back into the working Telegram runtime.
The Telegram bot remains untouched and live.
This keeps product risk low while still letting the web contour move much faster on UX and SEO.
Web tracking writes only to web-specific tables.
This keeps product risk low while still letting the web contour move much faster on UX and SEO.
Azure cost stays low by using static hosting, Functions, and Table Storage.
This keeps product risk low while still letting the web contour move much faster on UX and SEO.
Where the web wins
Fast scanning, clean repeats, and a visible price-history loop.
The web app is strongest when users need a dashboard, not a message thread: compare more listings, save more intent, and return to a structured watchlist.
Next step
Open the app and run the exact flow this page describes.
The explanation is useful once. The dashboard is useful every session after that.