The Problem we solved.

It is well understood that with the advent of the interactive Web [Web 2.0] the amount of information available to any user underwent explosive growth. Opportunities and tools were widely available to anyone who had the desire to share some (small or large) part of their interests with the rest of the world.

As a result, critical market data was no longer the province of larger Corporate, Government or Educational institutions, as any individual with a browser and the time necessary to evaluate results generated by interest-specific queries could gather large, relevant sets of data just as easily, including these results in their decision-making process.

This presented a problem of multiple dimensions:

  • How much information was out there?
  • What of that information was good?
  • What information was, by extension, absolutely worthless?
  • Where did the information reside?
  • Could the information even be extracted from the [superficially] related info of no interest?

The task of finding the answer(s) the question(s) could very often be overwhelming, if not absolutely discouraging. Add to this the time and market constraints imposed by a global economy/market, and business decision-making took on a new urgency, with corporate agility (or, the ability to detect, measure and then respond to gross or subtle market forces) increasingly defining the line between success, and failure.

Trendicy Corporation realized a radical approach was needed to short-circuit the drawn-out process of gathering, evaluating and then incorporating the value of all this new data into strategic planning. What was needed was a tool that anyone, in any business function, could use and be immediately productive. A tool that could, given simple instruction, be launched and then return results displayed in such a way that they were easily, quickly and efficiently evaluated for  applicability to the question being asked. A tool that, given some small amount of guidance, would remember good from bad, desired from undesirable, and learn with each successive use the values of the user, producing ever-more refined results each time.