Vaazo

Vaazo is a web scraping tool developed by MACOVEL, SIA (Ltd) that enables users to extract data from websites efficiently. It is designed to automate repetitive online tasks and gather information for various purposes. Vaazo is suitable for businesses of all sizes, from small startups to large enterprises. It caters to a range of professions and industries, including business analysts, market researchers, data scientists, e-commerce companies, and real estate agencies.

Key Features

Web scraping capabilities: Vaazo is equipped with a web scraping feature that allows users to extract various types of data from websites, such as text, images, and links. Users can specify the data they want to scrape by selecting elements on the web page or using custom selectors.

Flexible formula builder: Vaazo provides a user-friendly formula builder that enables users to create customized scraping formulas. The formula builder utilizes a drag-and-drop interface, making it easy for users to build their scraping logic. Users can combine different elements and actions to create complex scraping workflows that meet their specific needs.

API integration: Vaazo offers seamless API integration, allowing users to connect their scraping formulas with other applications. This integration facilitates the transfer of data between Vaazo and external software systems. Users can send data to external applications or retrieve data from external sources using API calls, enabling them to incorporate Vaazo into their existing workflows and leverage data from various sources.

Structured data presentation: After scraping data, Vaazo presents the results in a structured format similar to a spreadsheet. This allows for easy analysis and manipulation of the scraped data. Users can download the results as files, such as CSV or Excel spreadsheets, for further analysis or sharing. The structured presentation of data facilitates the extraction of valuable insights and informed decision-making.

Efficient workload distribution: Vaazo enables users to distribute the workload by running multiple scraping tasks concurrently. This feature saves time and increases efficiency, particularly for large-scale scraping projects or time-sensitive tasks.