Thanks for clearing up my mistakes on lumifi. I would have liked to discuss more aspects of lumifi in this post. In addition to the collaboration tools, I neglected to mention another of lumifi’s nice features: extensive content highlighting options. That is one of the reasons I linked to the video of lumifi in action.

-jr

*Note – I changed the title and a bit of the language in the first two paragraphs based on the last comment.

Lumifi is not a search engine – our public offering, http://www.lumifi.com, leverages a search feed, GigaBlast. We restrict our analysis to the first 50 results returned by GigaBlast, which is why the results shown is less than Google.

What we do is add value to a search engine by reading all the results and doing statistical analysis on them. So, if you searched for “american revolution”, for example, we take the first 50 pages that the search engine returns and then we read each page to determine which topics within those pages are statistically relevant. In the case of “american revolution”, we see topics like “articles of confederation”, “benedict arnold”, “benjamin franklin”, “boston tea party”, etc.

What this means is that these topics appear with a high frequency across all pages found by the search engine. The search engine could be anything that conforms to our XML specification from Lexus Nexus and Pub Med to private archives and libraries.

As you mentioned, lumifi can also analyze uploaded documents to find statistically relevant terms in them; and, we can crawl websites several levels deep to do the same.

The lumifi application only starts with this statistical/contextual analysis. It is essentially an authoring application to compile research, annotate it, collaboratively refine it, and then publish the results. This end publication then becomes fodder for more analysis effecting a feedback loop that reflects the iterative, winnowing nature of research.

Feel free to contact me, Jeff Ploughman, at jploughman@lumifi.com if you have questions.