Siri Meets Foursquare with Saga Roll Out: Priority Invite Code!

Saga is not another Siri clone, per se. It’s more similar to Google Now and the recently launched Friday app, offering a utility that intelligently pins down data based on any number of metrics, most prominently your location, and displays it to the user with actionized intent.  The service is launching its public beta for iOS today.  We have priority invite codes here if you’d like to check it out: just use the code SILICONANGLE.

Saga combines a number of different elements. For one, there’s the hyper-local aspect: the app is location-based at its core and the recommendations systems is tailored accordingly. Saga can alert you of nearby events, pinpoint the nearest gas station on the road and let you know if it’s going to rain in the next 10 minutes.  Such detailed and timely alerts and recommendations are made possible thanks to integration with DarkSky, one of several popular apps that Saga is connected to, along with RunKeeper, Withings and Fitbit.

Beyond third party app integration, Saga also has its own recommendation engine, so there’s far less reliance on Facebook’s social graph for recommendations than you think.   A.R.O., the Seattle-based team behind  Saga is well-versed in developing recommendation technology for the mobile set, with an earlier Android app called Aro that added context to your Contacts with a personalized touch.

A personal story: past, present and future

Saga doesn’t want to generally know where you are for a check-in, as Foursquare does.  It can determine whether you’re inside a building or not, to more accurately determine your location.  This is an extremely important aspect of Saga’s core technology, so it can operate more passively and take the burden off the user for checking into a location.  Saga doesn’t need you to check in, because it’s already taken note of your location and added it to a highly personalized profile that detects your patterns and anticipates your needs.

It’s all presented in a well-charted timeline that breaks down the 24 hour-day into a personal story, clocking travel time, work hours and everything in between.  You can view your past any time a pang of nostalgia hits you, but if you scroll forward far enough you can look into the future.  Saga will predict your next actions and activities–perhaps you’d like to stop by Mulligan’s after work?  Your friend Kevin will be there for $1 pint night.

All these ‘adaptive suggestions’ are based not on GPS info but rather sensor data from the user’s phone.   And there’s also a machine learning component that makes this information a lot more specific, taking historical data and user device settings into accounts, including personal preferences as well as calendar plans and shared schedules.  One very cool feature of Saga is the personalized “infographics” it presents, displaying your patterns on beautifully illustrated charts that traverse your commuter route, favorite dive bars and workout routines.

“Saga is the robot buddy we always wanted growing up. And Saga is the first app in new breed of virtual companions that will meaningfully improve our quality of life,” said Andy Hickl, CEO and co-founder of A.R.O. “We’ve been building Saga for over a year now and it’s an exciting time to debut. We’re seeing a new category of apps today that understand location in context, on their own, and adapt and learn as they go.”

An example of how Saga can be helpful: you’re at a client’s office around lunch time and Saga knows you’re probably getting hungry.  Since your favorite lunch spot isn’t nearby, it will recommend local eateries based on your dining habits (are you vegan?).

Machine-learning: how to think like a human

Machine-learning is an essential component behind Saga’s ability to pick up your very human patterns, and its underlying technology had to be designed to evolve with human nature.  Saga relies heavily on hardware to passively observe your life activities, so you don’t have to manually enter in the data yourself.  Hardware is going to be an increasingly important aspect of machine-learning in future businesses, Hickl says, and soon more machines will be given a “voice” in order to tell even more of your story.

While Saga’s started out with the basic sensors that already exist on most smartphones, Hickl knows that our mobile devices will soon come with temperature and barmeter sensors.  ”That helps determine if I’m indoors our outdoors,” he explains.  ”And smell-o-vision–the phone will be able to tell if I’m frying onions or grilling outside, determining if I’m cooking for a party.”

“Even Angry Birds is a sensor — I spent an hour playing it on the plane.  That might tell me insight to my own mind.  I might be bored, or I might be playing the best game of all time.”

There’s three main points Saga analyzes to provide recommendations and predictions:  routine, incentivizability and high level analysis.  Your routine looks at where you’ve been, how long and how often.  Determining the incentives behind your routine adds another layer of insight into your decision-making process.  You may pass three grocery stores on the way to Whole Foods because you prefer their organic produce and olive bar.  Then there’s high level analysis, which takes into consideration public data in aggregate–Saga generally knows what 30-something guys want to do in Seattle when it’s sunny.

There’s a few ways Saga wants to incentivize you to use the app as well.  Rewards points (what else?).  The more active and engaged you are with life, the more points you’ll earn.  If you hit certain milestones (i.e. redord commute time or a noteworthy 14-hour work day), you’ll earn even more experience points.  Right now this is more of a gamification feature than a redeemable system, but I expect we’ll likely see this become part of Saga’s business model sometime in the future.  For now, Saga’s considering a few directions for future monetization, and has seen interest from lifestyle brands on working with Saga for a highly engaged experience with end users.

Data security gets personal

By the looks of it A.R.O didn’t leave out the much-needed privacy assurance either. Their machine learning technology is presented in a  ‘learning mode’ that can be toggled on and off, and specific information can be permanently deleted from your timeline.  Looking at the bigger picture, the matter of personal data security will become an increasingly important one as more apps turn data into a consume-facing service (see Saga’s privacy policy here).  There’s no telling when or how apps like Saga will be regulated and if this will be done by industry-appointed organizations or the federal government.

“I think that this is something we’re all kind of figuring out at this point,” says Hickl.  ”What needs to be answered at this point is what is personally identifyable information (PII).  It can be as little data as two places you’ve been and the route you took to get there.  You can probably narrow things down to about a dozen people who follow this pattern.

“I think we need a firm definition around what PII really is.  We need to encourage the industry to have some standards around safeguarding that data, and we’re not going to share that data no matter what.  I want to be so respectful of this kind of data…companies working with this data need to be transparent.”

 Contributors: Maria Deutscher

About Kristen Nicole

Named by Forbes as a top influencer in Big Data, Kristen Nicole is currently a Senior Editor at SiliconANGLE.com. She got her start with 606tech, a Chicago blog she dedicated to the social media space, going on to become the lead writer and Field Editor at Mashable. Kristen Nicole has also contributed to other publications, from TIME Techland to Forbes. Her work has been syndicated across a number of media outlets, including The New York Times, and MSNBC. Kristen Nicole published her first book, The Twitter Survival Guide, and is currently completing her second book on predictive analytics. Follow my work (and some sprinklings of personal interests) on Google+