(Photo by Brady Dale)
Q-Sensei took some time to find itself, but now that its team has a focus, they’re doubling down on a strategy they believes has legs — once people get it. The basic idea is metadata based search for big companies.
Have you ever gone looking for something on Google, say, but the words you are are looking for are the same words that someone else might use to look for something else. But most people are looking for that other thing, so you can’t find any pages that relate to what you want.
It’s a step further than searching on Google for “Turkey” by excluding words relevant to another search like “-recipe” and “-food.”
More specifically, if Q-Sensei were Google, you’d get the option to pare down the results based on facets of the data. So if you were looking for the name of a startup but it also shared its name with, say, a common plant (think Parse.ly), you could trim out all the pages related to the plant. Only, the company isn’t doing this for Internet Search. It’s doing it for “Enterprise Search,” in which big companies want to cull through their internal data.
So, companies that have loads of documents, data and content in all kinds of different computer programs: millions of customers, thousands of charts, massive databases and huge project files. The company’s product makes searching all those sources at the same time easier and faster.
The big picture for the company, Jason Simon, VP of Product Development, told us, “Long term vision: we want to make working with massive amounts of data extremely easy.”
Right now, that’s enterprise search. In a year or so, it will also be analysis. And by easy, he means a product that is very nearly plug and play, as opposed to a bespoke, consultant-built system, which is what big companies usually go for. It’s not, Simon believes, what newer giants of tech want, though.
“You can’t fail fast with a giant consultant built enterprise product,” Simon said.
Now that the company is clear on that, they look to make it clear to the world soon, with a full relaunch of their website and rebranding across all their assets. Look for the new look to come in late April, early May. We got a preview and there is a big shift coming.
Here’s how the strategy Simon calls “progressive adoption” works for the company.
Their fundamental product is called Fuse. Fuse is an algorithm-driven search program that starts its searching with metadata and uses patented software to sort through it quickly. It wraps that software in a service layer that makes it fairly quick to implement for a company’s development team.
A way to ease a company into the program, though, is through a product line they have built called Sparks. Sparks are plugins that can be added to a variety of popular enterprise software packages, such as Confluence, Jira and Box. If a company buys the plug-in for each package, a user can search all the company’s data in all the software suites with a Spark plugin at once.
Simon argues that not only is this faster, but it spurs collaboration by showing employees within a company each other’s work.
So, for example, you might have one team that lives in a product like Jira and another in Confluence, but each team hardly visits the other. By using Spark across the packages, employees may find important information created by other teams. Information available to them, but not in a place they have been accustomed to looking.
The team believes this is a powerful business development strategy. A Spark is inexpensive and easy to implement, he said. It is just a plugin for a program a company has already committed to. That said, once people start using it, they believe companies will start to understand the power of their overall internal search system and want it for everything. That’s when they will come to license Fuse, a higher-ticket and more robust offering.
That’s at the heart of the company’s vision.
Q-Sensei was formed in a merger in 2007 between Lalisio and Quasm. It began as an effort to better search academic documents, but the team realized it had broader applications, particularly for enterprise search. It moved first into the traditional approach of doing enterprise business: consulting, bespoke systems, massive customization.
Now, though, the company has settled on a new vision. It wants to be relatively small and product focused. It wants to be there to help clients implement, but it wants to build something a company’s internal developers can deploy on their own. This, Simon believes, is the sort of solution modern companies want and expect. It reminded us of other projects we’ve covered, such as Yhat.
The company was founded in Germany. It has ten employees now, half here and half there. The Brooklyn half of the team came to New York around the start of 2013, in part, because Verizon Ventures was here, one of their investors.
It also came here because it believes New York gives it access to enterprise players who can use their product. The Brooklyn team works from two apartments in the Brooklyner, in Downtown Brooklyn, where some of its staff also live. It expects to add at least three more developers and three or four more business development staff this year, said Simon.
To date, the company has raised $5 million in investment and looks to close a Series B this year. It is not yet discussing revenue.