UPDATED 12:01 EDT / MARCH 31 2015

Sensai CTO Monica Anderson NEWS

Sensai launches new platform that lets companies leverage their unstructured data

Nearly every established company has a pile of unstructured data that is just waiting to be used, but taking the eclectic mountains of old reports, call transcripts, emails and other files and turning them into something usable often requires a huge investment not only in systems capable of sifting through all of the data, but also in a team of data scientists who can spend months analyzing what it all means and how to use it.

The newly launched Sensai calls itself a “data science platform for the unstructured world,” and it intends to give companies the tools necessary to leverage their unstructured data without spending huge amounts of capital and months of implementation to do it.

Sensai developed its own easy-to-learn scripting language called Content Discovery Language (CDL, or “cuddle”), which is a “linguistically oriented data flow language” that allows data scientists to parse and manipulate unstructured data with a few short commands. Since multiple algorithms and processes are built into CDL, data scientists do not need to spend time creating their own systems. Instead, they can jump right in to analyzing the data.

“Five to 10 years from now, strategic differencing between companies will be about how adept they are at how they interpret the signals of everything around you,” Sensai CEO Jonas Lamis told me.

“Big Data wrangling problems”

 

Lamis explained that Sensai can be a valuable tool for multiple real-world business use cases, and its clients have already found ways to use Sensai to take advantage of their unstructured data. One client, for example, has used Sensai’s tools to wrangle its massive library of documents related to internal audits. The client has its own dedicated audit team, which keeps track of regulatory oversights on the company’s thousands of industrial projects. Sensai gives that audit team the ability to easily search and filter millions of documents that were all dumped into an MS Sharepoint repository, greatly speeding up the audit process.

Sensai has also been used by financial services companies to track data on publicly traded companies to monitor possible price fluctuations and market sentiment. While data teams usually track price history, quarterly earnings and so on, Sensai also analyzes textual data, such as announcements regarding new hires for key executive positions, product launches, and any number of other data points that could be missed in a structured-only approach.

Lamis cofounded Sensai with company CTO Monica Anderson, an artificial intelligence expert who formerly worked for Google, and VP of Product Michael P. Gusek, who previously worked with Lamis on Rally.org.

According to Lamis, the idea for the company sprang from Anderson’s realization that “There are not going to be enough data scientists to solve all of the Big Data wrangling problems that will exist across all enterprises.”

Anderson believed that with her expertise, she could design a new platform with a better interface that any technical leaning people could understand. She pitched the idea to Lamis, and they decided to form Sensai to solve those “Big Data wrangling problems.”

While it officially launches today, Sensai has been around for two years and already has several high-value clients, including Siemens AG, UBS AG, and WorldQuant LLC. The company recently emerged from the San Francisco-based Big Data incubator Data Elite, and it has already earned $900,000 in seed funding.

Image credit: Sensai (c)

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