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You say Per-say, I say Persai - let’s call the whole thing off

It hard to separate news aggregator start-up Persai from the personality of one of its notoriously pugilistic co-founders, Ted Dziuba. Before Uncov, however, Ted got a degree in computational math and worked at Google, and co-contributors Kyle and Matt certainly seem to bring deep knowledge to the table: both the Persai blog and Dziuba’s former blog (Epsilon-Delta) at least have enough mathematical jargon to wrap an air of credibility around the enterprise.

So - what is Persai? At a high-level, as I understand it (based on the “evidence” cataloged below), its a news reader/aggregator, similar to in basic appearance to Google Reader. Where Persai differs, apparently, is in the fact that your reader’s content is determined by your interests - not just feeds you’ve subscribed to. Persai, based on your added interests and its own relevance engine, serves up content for you in a Reader-like form - acting as a “recommendation engine.” You can then further refine Persai’s interpretation of your interests by rejecting its selections, creating what I imagine to be a Pandora-like experience. Ultimately, it looks like Persai is closer conceptually to an uber-Memeorandum, Megite, or Tailrank - offering a more customizeable, granular experience integrated into a single personalized stream, compared to other sites canned verticals.

There are three pieces of Persai out there right now: a screenshot at Valleywag (displayed above), and two microsites (eyeonfacebook and appleinsight) apparently built to demonstrate Persai’s ability to generate a topical content stream. There’s also the Persai blog, which is long on technical descriptions and short on use-cases.

So - given that I don’t have access to a beta or whathaveyou, I can’t comment on how well it works, or how accurate my understanding of its functionality is. Assuming that I do have generally the right idea, however, I can comment on the business elements of Persai.

In a nutshell: I think Persai’s user-customizeable recommendation engine is bound to secure a dedicated niche user base, and less likely to ever go “mainstream.” Interest-based recommendations would certainly make an interesting addition to Google reader, especially when combined with your search and Reader clickstream history. As a standalone, however, I’d expect it to float in the same orbit at Megite/Tailrank/etc. - perennially there, but never crossing the threshold that separates those businesses that exist from those that win.