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Data-driven solution helping startups become more successful in the early-stage fundraising process by providing an exhaustive analysis of VCs. Platform speeds up the initial research into potential investors so that founders can make the most of their time and close their funding rounds faster.
The perfect match analyzes and scores both startups and VCs for optimal matchmaking, weighing more than one hundred criteria plus historical data to help everyone make better investment decisions. Platform scans the complete venture capital community and their portfolio companies to generate a contextual understanding of a startup’s ideal VC “matches.” Through an interactive dashboard and VC Matching Report, startups can review the list of VCs who are most likely to add value and be interested in their space.
Development of an AI-driven platform that analyzes characteristics of startups to help them fundraise faster, matching them with the best-suited venture capitalists and predicting potential success via the score rating system.
Algorithm of working:
Firstly, users (startupers and investors) have to get an access to the platform. While registering on the landings they provide detailed information about their companies, founders and current owners, and their personal qualities.
Then all the data is stored and processed by the Search Module. It's a web-based service used to search for companies. It analyzes the characteristics of startups, matches them with the most suitable venture capitalists and predicts potential success via rating system.
With the help of the ICO module, responsible for forming the rating on the basis of the processed data, the original data is loaded and received from the API service. It is calculated according to the customer's formula. The received report is saved and data is displayed.
An analytical tool uses artificial intelligence to provide information about large companies, start-ups and investments. It is based on the analysis of data from open sources by launching data through a neural network.
The rating is formed on the basis of the personal qualities of its founders and employees and changes dynamically. These qualities, for example, can be altruism, emotionality, anxiety, trust, achievement striving and so on. As a result, the rating allows its user to analyze the atmosphere in the selected company and decide to work with it or not.
After all the analysis is completed, a web service with a fast and flexible interface provides its users with on-demand information about companies and their ratings.
While searching for companies, an AJAX-request is sent to the server. It reflects whether the company is or is not present. In case the company is found, the list of companies corresponding to the request is displayed to the user. There is an ability to search and view information not only about the founders, but also about the employees of the company on each page of the company.
A user can add or delete participants to find out the percentage of personal qualities of a team. Then the company's rating will be calculated in accordance with the data changes. Thus, the chart dynamically changes and company's rating is formed.
In this way platform using the neural network helps founders fundraise faster by matching them with investors who are most likely to add value and be interested in their idea and space.
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