In this article, we would like to address how to generate good value for labs without being annoying or looking too salesy. The purpose of A-LabInsider was to become an insider within each lab. This is achieved through context crunching and understanding of needs of each lab. The idea was to leverage the data and help to each lab and of course gain a quick overview and benchmarking on available options in any topic. With an ability to overview the market one can gain a view on needs of each lab.
The research done in academic labs matters, here no-one can object it. Even more, there is a lot of research that is taking place in academia, in fact most of it. We want to help researchers to do better research, open more perspectives on their scientific questions in place, and study more aspects. Done in a faster, and more versatile way. To achieve this we can put them in touch with companies who have value for them. These can be product, service providers or collaboration partners. This is known that labs and authors are normally used to sell them something. There is nothing bad in it, everyone lives from top line, but what we want to enable here is a better research within those labs leveraging the available data on their produced research.
That being said, with A-LabInsider we leverage the data to produce insights and generate figure headlines to guide in which direction to go. The big data makes it complex for any small biotech company to extract insights at scale, however we want to provide a leverage to be able to see the market from the other sides besides those that are standardly pursued. This is achieved through data mining at scale and packing it user friendly to an end user.
We believe that every lab matters, whether it is located at a good institution or somewhere in the middle of less heard place. These people produce research and have something to share and bring to the world. They are funded from public money and are doing research. If can improve this research at least on 1% we will see the difference in worldwide research. The good advantage is that going by labs it is less datapoints than if it would be single people. But because each lab has own style, we can decode it and use it as a model of what are the needs of it and how it sounds. With this we generate a tailor made approach to the research of each lab.
History of the lab can show about its trends and the content of research, however everyone will agree here that doing it automatically for all labs can be challenging here. We ask here the questions that may be of importance to those who search for their final market or call it dataset. With this, we reveal the aspects of research for multiple labs to see each lab from multiple sides and approach them easily at scale directly. Both labs and small biotech do not have much time to do a desk research and with own brain identify what is a fit and what can be interesting. There are too many options always and not enough man power or time. We take this on ourselves providing a convenient tool how to approach each from multiple aspects interesting to both parties.