We are transitioning to focus on streaming data

This past 6 months has been exciting for us.  We publicly launched our first algorithms as a service offering in April, won pitch competitions in May and June, and have been heads down working with customers since July.

It's been a lot of fun going from idea to revenue. We've had the opportunity to work with great companies in a variety of industries including semiconductor, online education, venture capital.  We've enjoyed building recommendation systems, predictors, and intelligent content classifiers.  But every once in a while, a calling comes that you just can't ignore.

That calling for us is the internet of things (IoT).  While we've had our eye on this space for some time, the last several months have made it clear that the right fit for our technology and team is working with streaming data from connected devices.

Our technical team has been working with massive streams of data for years in the security space security was big data before big data even existed as a term).  In security, as with IoT, classifying data is the name of the game.  Systems need to be able to ingest massive amounts of raw data and figure out WHAT it means.  Is there a threat? Is a system going out-of-whack? Is a system being hacked?

All of these events have a digital fingerprint that can be identified with the right data models.  This requires infrastructure to properly ingest and store streaming data, a modeling process to build the "digital fingerprint" of what you're looking for, and machine learning so that those models get better over time.

Data from IoT devices need the same intelligence.  Devices should be able to easily stream raw data into scaleable storage.  It should be easy to build data models for "digital fingerprints" and apply machine learning to find and refine those fingerprints over time.  Today, there are several great companies and open-source projects tackling the infrastructure piece, but machine learning solutions that solve the big problem for IoT, classification (and anomaly detection to a lesser degree) are still lacking.

It is our goal to make this powerful machine learning technology available to companies of all sizes.  We are starting out with wearables companies, and will be announcing some exciting new projects in manufacturing and healthcare soon so stay tuned.  We're excited to be playing in such a rapidly growing space, and look forward to working with a cadre of great customers and partners in the months to come.