Crawfish solves the challenge of fragmented restaurant data across the internet. Restaurant information is often scattered across various websites (like Zomato, Yelp, Google Maps, etc.), making it difficult for businesses or developers to compile consistent data. Crawfish automates this process by scraping relevant details and presenting them in a usable format, reducing manual effort, saving time, and improving accuracy in restaurant discovery or data integration.

Building Crawfish came with several significant challenges, especially during the data scraping process. Many websites implemented anti-bot protections such as CAPTCHAs, JavaScript rendering, and IP rate-limiting, making it difficult to access data reliably. Each platform also presented restaurant information in different formats, requiring custom scraping logic for each one. Additionally, the same restaurant often appeared on multiple sources, leading to duplication issues and the need for thorough data cleaning. Keeping restaurant details like menus, prices, and hours updated in real-time required regular crawling and scheduling. Legal and ethical considerations also played a role, as the scraping had to align with platform terms of service and data privacy rules. One of the biggest challenges was rendering the scraped restaurant data in a user-friendly UI—this involved mapping complex nested structures like categories, menus, and reviews, and in cases where scraping failed, integrating external APIs to ensure complete and accurate results.