The intrepid business development manager and chief technology officer hungry for big new ideas might not have noticed one that’s probably destined to go stellar any time soon: TipSense, a platform that crunches massive, unstructured data sets in an instant to yield practical, simple, clear information.
From Big Data to what’s best
That’s a lot of adjectives for one sentence, but they’re fully warranted. TipSense is the brainchild of Cornell artificial intelligence whizz-kid David Schorr, who built and bootstrapped the platform over the last four years. For anyone who wants ready answers without having to wade through a forest of reviews, TipSense has the solution.
As any tech business development manager can tell you, the online world is awash with incalculably vast quantities of data. A data surfeit which makes for some serious headaches if you just want to know where and what to eat, or which mobile app is better than its apparently identical rivals. TipSense’s algorithms can do both.
In a world where everyone hails the advent of Big Data as a kind of information revolution, very few companies have had the nous to show ordinary consumers why it really matters. Until TipSense came along, that is, with its two fabulous websites: Dish Tip, which crunches literally millions of reviews, web images and online mentions to yield the most buzzed about local food, and AppCrawlr, which does the same for mobile apps.
Why AppCrawlr will convert the most skeptical business development manager
AppCrawlr is Dish Tip’s younger sibling, and anyone who tries it, from the skeptical business development manager to the ordinary Joe, will testify that it does a much better job than Apple or Google’s app stores. It provides an at-a-glance overview chart – the “Uber Grid” – listing all the strengths and weaknesses of rival apps side by side.
There are a few teething problems to be ironed out – neither Dish Tip nor AppCrawlr are completely accurate all of the time. But they definitely showcase the relevance and sheer usefulness of TipSense technology, which uses natural language processing, content fingerprinting, topic modeling and conceptual entity recognition to sift through mega-data in the blink of an eye.
One thing Schorr can be confident of: his platform is set to be a hot acquisition target.