Freestak, a marketplace for endurance influencers, wanted to integrate key insights coming from marketing campaigns with their associated influencers. Freestak required obtaining post data and engagement metrics of posts, stories and reels of Instagram influencers.
We created an intelligent lookup system (using the OpenAI) that can understand natural language prompts and convert them into SQL queries to retrieve the required information from a database.
We used a signaling service (TrendSpider) to find out the right time and price to buy/sell the stocks. Once a signal is received, the bot connects with the TWS interface and makes the required transaction. It happens within a time bracket of 15 minutes.
We devised a multiple-layer strategy to improve the scaling of the scraper and resolve the blocking issue. The scraper was integrated with multiple API providers (including our customized API written in Playwright), to provide a strong backup for retrieving the information.
We fetched the users’ data from an API and checked all the subscriptions of every user to create a filtered list. Once we had the list, we created a templated transactional email which was then used to send relevant newsletters to all the subscribers.
We used the proprietary algorithm of Data Prism to scrape the required data from LinkedIn. It involved the use of certain filters to find the companies/brands that fulfill the criteria. Once we have these results, the scraper would find the relevant employees to gather their details.
We created a web application that asks users for their desired trip and uses chatGPT to find the best possible flights from all the airlines and agents. The user can also choose to mention the specifics of the tickets they need, like price range, airlines, and date(s) of traveling.
We used the Reports API of the Seller Central API (SP-API) to extract the required information. The extracted data was then cleaned and modified to meet the client’s needs to generate useful reports. Amazon provides the reports in the GZIP format which are converted to CSV before emailing them to the clients.
We implemented a smart algorithm with a multi-level crawler to make sure that all the real estate agents are being found. We scraped multiple websites to gather an extensive amount of data and used proxies to prevent blocking and other issues.