It’s common practice to classify things into different categories to simplify a concept or a problem. But how often does that classification lead us to apply our predisposed notions of one category on the overall concept? For example, Skim AI is a research management solution that helps users identify value faster.
“Who is that user?” you ask (well, we ask). From grad students and PhD candidates to investors and consultants – anyone who is conducting research has found use in our tools. An unavoidable point of many conversations is whether we, as a company, operate in the EdTech or Future of Work space. Narrowly defining Skim AI as one or the other would automatically ignore a large segment of the market that would benefit from our tool for researchers.
The ethos guiding our development is that a researcher is a researcher, plain and simple.
It would be near impossible to distinguish the pain of one researcher from another based on industry or sector. All researchers face a common problem in today’s world – finding that needle of useful information in the haystack of information available. Our mission is to build tools for researchers powered by machine learning to help overcome that problem. Skim AI achieves this through fast analysis, theme relevant search, and granular data categorization (down to the sentence level).
For example, consider a researcher who is looking for information about the real estate market in Spain. Whether a student writing a case about the Spanish economy or an investor looking for opportunities, they both can use Skim AI to:
During our extensive customer discovery stage, one thing was clear to us: the pain points of conducting research are industry agnostic. This was the foundation on which we built the overarching product philosophy at Skim AI – ‘a researcher is a researcher’. Our mission is to provide these tools for researchers that could use it for personal research, student works, or as a part of a team in an organization.
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