The Challenge Our client, a food delivery analytics and retail intelligence firm, needed to collect restaurant, grocery, and convenience store data from DoorDash – including menus, product names, pricing, ratings, and delivery availability. With thousands of listings updated frequently across multiple cities, manual data extraction was not feasible for maintaining real-time insights. Hurdles We Faced […]
The Challenge Our client, a food tech and analytics company, wanted to extract restaurant and menu data from Menufy, including restaurant names, cuisines, addresses, menu items, pricing, and availability. The goal was to create a centralized database for competitor analysis and pricing insights across different U.S. cities. Manual data collection was impractical given the platform’s […]
The Challenge Our client, a food analytics and restaurant aggregator startup, needed to collect restaurant, menu, and pricing data from Uber Eats across multiple cities. The goal was to analyze pricing trends, menu variations, and restaurant availability in different regions. Manual data gathering was impossible due to the platform’s dynamic structure and massive volume of […]
The Challenge Our client, a B2B analytics and lead generation company, needed to collect buyer and seller data from IndiaMART, including product details, company names, contact info, and location. Manual data gathering was slow, inconsistent, and couldn’t handle the platform’s massive product listings and frequent updates. Hurdles We Faced Our Step-by-Step Approach Requirement Analysis:Defined essential […]
The Challenge Our client, a matchmaking analytics and marketing firm, needed access to user profile data from Jeevansathi, including attributes like age, gender, profession, education, location, and community background. Manual browsing was inefficient and limited their ability to perform large-scale trend analysis and matchmaking insights. Hurdles We Faced Our Step-by-Step Approach Requirement Analysis:Identified essential data […]
The Challenge Our client, a jewelry trading and analytics firm, wanted to collect comprehensive data of diamonds and gemstones from RapNet – including stone type, carat, color, clarity, cut, certification, and pricing. Manual collection was time-consuming, and the dynamic platform required authentication, making traditional scraping methods unreliable. Hurdles We Faced Our Step-by-Step Approach Requirement Analysis:Identified […]
The Challenge Our client, a property analytics and investment company, needed access to commercial property data from MagicBricks – including property type, price, area, location, amenities, and broker information. Manual collection from thousands of listings was slow and inconsistent, limiting timely market insights. Hurdles We Faced Our Step-by-Step Approach Requirement Analysis:Defined key data points – […]
The Challenge Our client, a real estate investment and analytics firm, wanted to collect commercial property data from 99acres – including property type, location, price, area, amenities, and broker details. Manual research across thousands of listings was inefficient and couldn’t keep up with daily market changes. Hurdles We Faced Our Step-by-Step Approach Requirement Analysis:Defined essential […]
The Challenge Our client, a real estate analytics firm, needed accurate and up-to-date residential and commercial property data from Zoopla — including property type, price, location, agent details, and availability. Manual data collection was slow and prone to inconsistencies, making it difficult to perform market comparisons and generate insights. Hurdles We Faced Our Step-by-Step Approach […]
The Challenge Our client, a fintech analytics company, wanted to monitor live Bitcoin prices and other top cryptocurrencies every hour. They also had a custom algorithm to rank cryptocurrencies, and only the top 50 according to this ranking needed to be tracked. Manual tracking was inefficient due to: The goal was to automate hourly price […]