Abstract: Disclosed herein are embodiments of systems, methods, and products comprises a server, which identifies optimized potential suppliers based on the client's request. The request comprises search specification and preselected bidders. The server extracts a first set of keywords from the search specification and finds web pages of potential suppliers based on the first set of keywords. The server identifies the websites of the preselected bidders, extracts a second set of keywords from the websites, and finds web pages of more potential suppliers based on the second set of keywords. The server determines the web pages are associated with real suppliers by excluding non-supplier web pages. The server determines a similarity score for each potential supplier by vectorizing keywords extracted from the supplier's web pages. The server determines a risk score for each potential supplier. The server generates a GUI comprising a list of suppliers ranked based on the similarity scores.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprises a server, which provides trigger-based personalized sales outreach based on a user's request. The request comprises a list of target companies/people. The server scans a variety of sources by web crawling the sources' web documents and finds news items relevant to the target companies/people. The server determines an importance score for each news item that measures the probability of a sales representative being interested in the news item. The server applies a set of filters comprising a network of neural networks to filter out false positives. The analytic server determines a relevancy score for each news item. The server generates a GUI to display the news items satisfying certain thresholds. Based on the user's selection on the news items, the server uses the news items as triggers of outreach and generates an electronic message template prepopulated with the news items.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprises a server, which identifies optimized potential suppliers based on the client's request. The request comprises search specification and preselected bidders. The server extracts a first set of keywords from the search specification and finds web pages of potential suppliers based on the first set of keywords. The server identifies the websites of the preselected bidders, extracts a second set of keywords from the websites, and finds web pages of more potential suppliers based on the second set of keywords. The server determines the web pages are associated with real suppliers by excluding non-supplier web pages. The server determines a similarity score for each potential supplier by vectorizing keywords extracted from the supplier's web pages. The server determines a risk score for each potential supplier. The server generates a GUI comprising a list of suppliers ranked based on the similarity scores.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprises a server, which provides trigger-based personalized sales outreach based on a user's request. The request comprises a list of target companies/people. The server scans a variety of sources by web crawling the sources' web documents and finds news items relevant to the target companies/people. The server determines an importance score for each news item that measures the probability of a sales representative being interested in the news item. The server applies a set of filters comprising a network of neural networks to filter out false positives. The analytic server determines a relevancy score for each news item. The server generates a GUI to display the news items satisfying certain thresholds. Based on the user's selection on the news items, the server uses the news items as triggers of outreach and generates an electronic message template prepopulated with the news items.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprises a server, which receives a request from a client's electronic device to optimize one or more media elements in an optimization zone of a web page. The server may receive a slideshow or a video and determine a candidate media dataset based on images from the slideshow or video. Alternatively, the server may receive an article and determine the candidate media dataset by searching images related with key words in the article. The server may modify the markup code of the web page and publish the media element within the candidate media dataset in the optimization zone. The server may further query the click-through rate (CTR) associated with each published media element. Based on the CTR results, the server update the rendering rate of each media element to produce the maximum CTR.