Introduction

In the fast-paced world of online food delivery, real-time inventory visibility is crucial to ensure seamless customer experience and minimize order failures. Latin America's top food delivery platforms, Rappi and PedidosYa, handle thousands of orders daily, making accurate restaurant inventory tracking essential. With dynamic menus and fluctuating stock levels, food delivery aggregators and restaurant chains need reliable solutions to monitor stock availability efficiently. This case study explores how web scraping for restaurant inventory for Rappi and PedidosYa has enabled businesses to stay ahead of demand fluctuations, maintain updated listings, and prevent customer dissatisfaction. Leveraging advanced Quick Commerce Scraper technologies, our client gained actionable insights across multiple vendor menus. By implementing a tailored product data scrape solution, the client achieved full visibility into Rappi and PedidosYa’s restaurant ecosystems. This case delves into the strategy and outcomes of applying web scraping to support inventory optimization and decision-making across food delivery networks.

The Client

The client is a multinational FMCG brand that supplies ready-to-eat and frozen food products to a wide network of restaurants across Latin America. Operating in over 12 countries, the brand partners with hundreds of restaurants listed on platforms like Rappi and PedidosYa. The client sought to maintain visibility into how their products were being offered, stocked, and priced in third-party restaurant menus on these platforms. As they expanded their B2B presence, they required a robust mechanism to monitor product availability and ordering patterns in real time. The client specifically wanted to track menu data, stock status, and ingredient usage to align its supply chain and promotional strategies. Through web scraping for restaurant inventory for Rappi and PedidosYa, they aimed to strengthen their vendor support strategy and detect gaps in stock and pricing. The project aligned with their broader digital transformation roadmap to make their supply network more responsive and data-driven.

Key Challenges

One of the major challenges was the lack of centralized access to restaurant stock and menu availability across Rappi and PedidosYa. With no open API support for granular stock monitoring, data had to be extracted from the platforms' front-end layers using compliant scraping methods. Restaurants frequently updated their menus and pricing, and product availability fluctuated throughout the day, making static data scraping ineffective. The client also faced difficulties in monitoring product stock levels on Rappi and PedidosYa due to inconsistent naming conventions, ingredient combinations, and dynamic UI rendering across cities. They needed reliable, location-specific tracking that could distinguish between in-stock, low-stock, and out-of-stock statuses. Additionally, extraction of crucial data like ingredients, items, and pricing required complex logic to interpret localized formats. Standard scraping tools failed to capture deeper insights such as ingredient details and combo configurations. The client also wanted a flexible data model for integration with their ERP and supply chain platforms, which demanded a custom eCommerce dataset scraping approach with high accuracy.

Key Solutions

We developed a scalable and intelligent scraping solution tailored for real-time restaurant inventory tracking on Rappi and PedidosYa. The core of the system revolved around web scraping for restaurant inventory for Rappi and PedidosYa, with built-in modules for Rappi restaurant inventory scraping and PedidosYa restaurant product data scraping. Using advanced crawlers, we captured structured data from thousands of restaurant listings, identifying real-time stock indicators and pricing changes. The solution included Rappi restaurant menu data scraping logic that filtered items by SKU and brand mapping, while also collecting relevant metadata like portion size, ingredient lists, and combo options. With our Food delivery data scraping framework, we enabled deep data extraction down to the item and ingredient level, along with accurate timestamping for stock status.

Our team also designed a Quick Commerce Scraper to periodically fetch listings from Rappi Turbo and PedidosYa Express, allowing us to extract quick commerce product data from rapid-delivery menus.

To ensure precision, data was enriched through NLP and product classification algorithms. The system allowed the client to scrape PedidosYa prices data by restaurant and location, identify pricing mismatches, and monitor competitor offerings. Ultimately, this enabled restaurant inventory optimization using scraped data, empowering the client to align inventory planning with actual menu visibility and adjust regional fulfillment based on market demand. The data output was API-ready and seamlessly integrated with the client’s analytics dashboards and CRM tools.

Conclusion

By leveraging web scraping for restaurant inventory for Rappi and PedidosYa, the client gained the ability to monitor product availability, track pricing trends, and manage inventory disruptions proactively. The solution provided comprehensive visibility across dynamic restaurant listings, enabling timely restocking and enhanced vendor performance. Through a customized and scalable approach, the client overcame the platform-specific challenges of scraping Rappi and PedidosYa, while integrating the data into their decision-making systems. This case study demonstrates how Web Scraping Rappi Quick Commerce Data and PedidosYa inventory data can transform operations across the food delivery ecosystem. For businesses aiming to enhance inventory intelligence in quick commerce, structured data scraping is no longer optional—it’s a strategic imperative.

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