Nara Logics has spent the better part of three years building the artificial intelligence necessary to deliver personally relevant search results. The company believes that it is no longer good enough to deliver the most universally relevant results to a given search query, but rather that it’s necessary to deliver results tailored to the individual user’s tastes, preferences, and interests.

In June 2012, the company launched its restaurant search Web product as an early proof of concept – eventually announcing integrations with GrubHub, OpenTable, and Foursquare – and then subsequently expanded the platform to mobile in November with native iOS and Android apps. Eventually, it plans to expand beyond restaurants into the broader search market. Today, the company is taking the next step in its evolution with the launch of its Digital DNA social product, which allows users to combine their personalized search preferences to arrive at a universally appealing result.

“Up until now, Nara has been focused on helping people find their own personalized restaurant recommendations, but most of us don’t usually dine alone,” CEO and co-founder Thomas Copeman says. “Nara removes the hassle, time and effort spent on searching for the perfect group dining experience.”

Again, restaurant recommendations are just an early test market for Nara’s technology. But even in this single vertical, the product is compelling. Anyone who’s ever attended a large group dinner, or tried to pick a date destination with an indecisive partner knows first hand the potential for frustration. Similarly, anyone who’s made a dining decision based on online reviews only to find their eventual dining experience to be nothing like the reviews knows that one man’s heaven is another man’s hell.

The Digital DNA social tools allow Nara users to create a venn diagram of each of their personalized preferences and tastes, at the center of which should be their collective ideal restaurant option – or in the future vacation destination, concert, book, movie, or luxury sports car, among countless other categories.

The output of Nara’s Digital DNA search is not as simple as adding two separate personalized searches together – for example, my top three results plus your top three, equals our top six. Rather, the artificial intelligence algorithms actively combine each individual user’s tastes and arrive at a fresh list of recommendations based on the collective data.

“Nara uses a complex, scientific algorithm to compute a unified taste profile for a group of users,” CTO and co-founder Dr. Nathan Wilson says. “Nara then matches that integrated profile to a diverse range of restaurant  options based on their features, rather than just presenting restaurant recommendations that are similar to others in the pool.”

For new users to the Nara platform, the software begins by asking a series of basic questions about their restaurant genre, ambiance, and entertainment preferences, as well as their favorite restaurant choices. Connecting to existing social media accounts can add an additional layer of preference and historical behavioral data. From this broad picture of a user’s preferences, the Nara algorithms construct a personalized taste graph that continues to improve over time.

Nara currently supports restaurant searches in 50 North American cities including Atlanta, Boston, Chicago, Las Vegas, Los Angeles, Miami, Memphis, New Orleans, New York, Portland, and San Francisco. The company was founded in Cambridge, Mass. in 2010 by a team of MIT scientists, who have since raised $7 million to date from private investors through Peter de Roetth’s Account Management, LLC.

The big challenge for the company, as with any in the personalization space, is varying levels of user discomfort around privacy protection. Nara is asking consumers to trust them with so much data about their behavior and preferences that it can make recommendations about things that they’ll like in spite of the superficial clues that suggest otherwise. This level of omnipotence, while useful, can be offputting to the less progressive among us.

Personally, I am of the mindset that countless Web companies already have this level of information about us all, so we might as well benefit from it ourselves. Nonetheless, Nara’s ability to handle the privacy and security concerns going forward will go a long way toward dictating its ultimate mass-market adoption.

Secondly, the company has the very real challenge of expanding its offering horizontally into new categories while maintaining both the same level of domain expertise as well as continuing to articulate its value to the end user. For example, Facebook’s newly launched Graph Search offers a number of powerful discovery features, but the average user either has never used it or has only used it to search for amusing and embarrassing insights about their social connections. If Nara is not careful, it risks similarly diluting its impact by offering more horsepower than the average user knows what to do with.

Despite its intentions of reinventing search, Nara is not necessarily disruptive to Google at present. Rather, it’s most likely to challenge Yelp or UrbanSpoon in the restaurant information and discovery category. Once the company expands into other consumer categories, the product could present similar disruption to GoodReads for books (recently acquired by Amazon), Consumer Reports for electronics, Rotten Tomatoes for movies, and so on. But, unless Nara begins facilitating commerce through its portal, marketplaces like OpenTable, Amazon, and Fandango are not in its immediate crosshairs.

Whether it be through social signals, interest graphs, or another next-generation technology, the Web will continue to get more personalized. Nara has arrived at a compelling vision for how this personalization can be delivered to improve even the most basic everyday experiences.

“Our vision is to build a more personal, actionable, and liberating Web,” Copeman says.