360° Data Collection

Different data sets for different insights

You never know where the next great idea, company, or technology may come from. This is why Mergeflow collects and analyzes data from across various disparate data sets and sources. As a result, you are aware of what's going on around you, and you get a 360° perspective.

Mergeflow collects data from across technology and business

We have built our own, fully automated and scalable data collection infrastructure that works 24/7 for you.

Scientific Publications

Scientific publications help you identify experts and leading organizations in a given technology field.

Patents

Patenting activity in a technology or application often indicates future commercial activities in this area.

News & Blogs

News and blogs provide new ideas, forecasts, and scenarios, often from an unconventional angle.

Market Research

Market research connects technologies to applications, provides size and growth estimates, and identifies major players.

Venture Capital

Updates on venture investments help you discover new, market-relevant technologies and business models.

Technology Transfer

Technology transfer offerings from universities and R&D organizations help you strengthen and speed up your own R&D projects.

Research Projects

Updates on funded research projects help you discover innovative, early-stage companies that would otherwise remain under the radar.

Clinical Trials

Use clinical trials to discover the latest applied medical research, and the companies and organizations behind these advances.

"The good researcher could not act in academic isolation. In order to succeed, they must know something about markets, finance, and the organization of a business."

From G. Pascal Zachary, "Endless Frontier: Vannevar Bush, Engineer of the American Century"

Background image of Vannevar Bush's differential analyzer, from Wikipedia.

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Unsupervised learning algorithm

Unlike supervised learning algorithms, unsupervised learning algorithms do not require labeled data. Instead, they try to find an optimal grouping of data by considering various attributes of the data. In our context, the algorithm classifies each document part as either "relevant" or "not relevant", based on a set of data attributes.