Spark on the website of the online store category 3,000 products

About the Company Trading company. We work with a product group of more than 2000 items across different categories.Current Situation Currently, the nomenclature is maintained in Google Sheets — data is consolidated by tabs (categories). Tab Structure: Product name Price groups: cost price, wholesale, retail Characteristics: weight, quantity per package, etc. Important: the number of columns varies for different product categories, as they have different characteristics.Why the Current Solution is Inadequate Google Sheets does not allow setting access rights at the level of individual columns. We need to: Grant users rights to view certain columns (for example, only cost prices) Grant rights to edit certain columns (for example, retail prices) While restricting access to other columns in the same tabWhat Needs to Be DoneMain Requirements Flexible Rights Management System Access at the level of individual columns (read/write) Assignment of rights by roles or users Management of rights without the involvement of programmers Support for Different Data Structures Different product categories have different sets of characteristics Adding new columns/characteristics without programming Independence from Developers Administration by internal staff Adding categories, columns, users — through the interface Integration with ERP Exporting current prices to our ERP system Export or automatic integration via API Data Analysis Using AI (preferably) Ability to analyze the entire nomenclature list Enrichment, verification, recommendations — if you have ideas, please describeExpected Result A working solution in which: The nomenclature is structured by categories with different sets of characteristics Column rights are flexibly configured (view/edit) Data is exported to ERP The team can manage the system independentlyWhat We Need from You When Responding Describe in general terms how you envision the solution: What tool/platform do you propose

A simple program or Telegram bot is required for parsing participants of chats and discussions in Telegram channels. Functionality: The user enters a link to the channel or chat. If a link to a channel is provided, the program should automatically navigate to the "Discussions" section and perform parsing specifically for discussion participants. The result should export only unique links to user profiles without duplicates. Additional selection requirements: Only users with their own public channel specified in their profile should be included. The user's channel must have at least 200 subscribers. Users with smaller channels are not suitable. A maximally simple and convenient tool is needed: just paste the link and start collecting data based on the specified criteria. I am not a programmer, so I would appreciate an assessment of the feasibility of such a solution and your suggestions for the optimal way to develop it. Standard parsers are not of interest to us — the implementation of the logic described above is required. Responses to your proposals may be within a day.

Good evening, there is a task: you need to check the availability and relevance of the prices of products added to the website (the website is on the prom.ua platform) from the supplier's site 1-2 times a day. Can you calculate the price and how it will look?

Set up automatic daily updates of product availability on our website on prom.ua. We have a supplier who sends a price list of products in Excel format to our email every day. The items on our website and in the supplier's price list are the same. The values in the "stock" column are either out of stock, a number, or more than a box - these need to be updated on the site to either Ready for shipment or Out of stock. Items that are not in the supplier's price list should remain unchanged. Please propose a solution, timeline, and budget. Thank you in advance for your response, I look forward to collaborating with a specialist.

A project needs to be implemented for collecting and structuring a large array of images from open web sources (initially 2000 images). The task includes: - automated image collection; - uploading files in the highest available quality; - classifying images by categories. Expected results: - a structured image database; - a clear cataloging system; - delivery of the results via Google Drive or another agreed method;