PRODUCT NEWS
TeQatlas’s Relevance Score
PRODUCT NEWS
TeQatlas’s Relevance Score
While running pre-screens with the TeQatlas extension, investors can see the company's relevancy within seconds. This became possible for investors with the Relevance score designed to easily qualify the companies by relevance to the investment focus.
TeQatlas aims to assist investors in selectively targeting relevant startups to increase the quality of their deal flow of private companies by accelerating their workflow and fastening the time to insights.
TeQatlas aims to assist investors in selectively targeting relevant startups to increase the quality of their deal flow of private companies by accelerating their workflow and fastening the time to insights.
While running pre-screens with the TeQatlas extension, investors can see the company's relevancy within seconds. This became possible for investors with the Relevance score designed to easily qualify the companies by relevance to the investment focus.
TeQatlas aims to assist investors in selectively targeting relevant startups to increase the quality of their deal flow of private companies by accelerating their workflow and fastening the time to insights.
TeQatlas aims to assist investors in selectively targeting relevant startups to increase the quality of their deal flow of private companies by accelerating their workflow and fastening the time to insights.
Definition
Definition
TeQatlas’s Relevance score helps investors qualify and prioritize the most relevant companies out of the hundreds in their deal flow. This proprietary score was designed to surface the most relevant companies and help you reach them out at the right time.
TeQatlas’s Relevance score helps investors qualify and prioritize the most relevant companies out of the hundreds in their deal flow. This proprietary score was designed to surface the most relevant companies and help you reach them out at the right time.
How it works
How it works
The investors visit companies' websites (startups) to find out what they do and whether they fit their investment focus. An investor must not spend hours manually screening startups. TeQatlas Extension provides a Relevance Score for each company (startup) in the TeQatlas database in seconds. The extension has functionality that redirects the investor user to the platform, where the Intelligent Personal Assistant called Toshi notifies the investor about new messages in the Chat Room on the platform.
The investors visit companies' websites (startups) to find out what they do and whether they fit their investment focus. An investor must not spend hours manually screening startups. TeQatlas Extension provides a Relevance Score for each company (startup) in the TeQatlas database in seconds. The extension has functionality that redirects the investor user to the platform, where the Intelligent Personal Assistant called Toshi notifies the investor about new messages in the Chat Room on the platform.
What you need to know about the Relevance Score:
What you need to know about the Relevance Score:
- The company receives a Relevance score between 0 and 100, with 100 being the highest.
- The algorithm calculates the relevance IN SECONDS from the extensive company dataset.
- There are 1+ mln companies under the hood and counting.
- Neural networks accurately define companies’ industries.
- The company receives a Relevance score between 0 and 100, with 100 being the highest.
- The algorithm calculates the relevance IN SECONDS from the extensive company dataset.
- There are 1+ mln companies under the hood and counting.
- Neural networks accurately define companies’ industries.
One-click to the insight
One-click to the insight
Insight generation happens under the hood based on investment preferences and data on the startup landscape. The system design makes it possible to start using TeQatlas in one click, applying advanced technologies, computational analytics techniques, and scientific approaches to find pairs of only relevant investees and investors.
Insight generation happens under the hood based on investment preferences and data on the startup landscape. The system design makes it possible to start using TeQatlas in one click, applying advanced technologies, computational analytics techniques, and scientific approaches to find pairs of only relevant investees and investors.
Tech and Data Background
Tech and Data Background
There is a massive amount of (un)structured data on the global private market that needs to be stored and interpreted, identifying correlations and patterns to predict new trends and needs across diverse markets.
In the absence of similar solutions on the market, the TeQatlas team has hand-crafted potential indicators of investor preferences, incorporating our knowledge of the capital-raising market.
We rely heavily on data mining, machine learning, and neural networks to analyze these data with accuracy and reliability quickly. Thus investors can save time, simplify decision-making, and get things done.
In the absence of similar solutions on the market, the TeQatlas team has hand-crafted potential indicators of investor preferences, incorporating our knowledge of the capital-raising market.
We rely heavily on data mining, machine learning, and neural networks to analyze these data with accuracy and reliability quickly. Thus investors can save time, simplify decision-making, and get things done.
There is a massive amount of (un)structured data on the global private market that needs to be stored and interpreted, identifying correlations and patterns to predict new trends and needs across diverse markets.
In the absence of similar solutions on the market, the TeQatlas team has hand-crafted potential indicators of investor preferences, incorporating our knowledge of the capital-raising market.
We rely heavily on data mining, machine learning, and neural networks to analyze these data with accuracy and reliability quickly. Thus investors can save time, simplify decision-making, and get things done.
In the absence of similar solutions on the market, the TeQatlas team has hand-crafted potential indicators of investor preferences, incorporating our knowledge of the capital-raising market.
We rely heavily on data mining, machine learning, and neural networks to analyze these data with accuracy and reliability quickly. Thus investors can save time, simplify decision-making, and get things done.
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