imagine this:

YOUR company,


"Greetings, Professor Falken. A strange game. The only winning move is not to play."

WarGames (1983) (4:05)

Litigation is like that. On defense, even when you win, you lose. But it doesn't have to be that way.

Intraspexion trains a "deep learning" algorithm to provide an early warning of a specific litigation risk to in-house counsel in near real time, so that in-house counsel can conduct an investigation, and then help control group executives address the issue and, hopefully, in time to avoid the lawsuit altogether.

For the first time, there is a technology enabling your company to go from reactive to proactive.

The phrase deep learning refers to a neural network algorithm which allows computers to learn. For an introduction, please see this TED talk by Jeremy Howard,"The wonderful and terrifying implications of computers that can learn." (Under 20 minutes; over 240,000 views.)  


On November 7-9, 2016, Intraspexion is co-sponsoring the AI World Conference & Exposition 2016 in San Francisco. Nick Brestoff, Intraspexion's founder and CEO, will give a 20-minute talk as part of the Conference's AI Solutions Theater. For information about the Conference:

On September 15, 2016, our Team demonstrated (internally) an enterprise-level system. We agree that we've achieved a state known as a Minimal Viable Product (MVP). We can now go from processing, say, last night's emails, to and through a deep learning algorithm trained to assess a specific litigation risk, and then to a User Interface (UI). The UI presents a distribution of risky emails as a bar graph (for visualization of the data); a spreadsheet which ranks the risky emails; and buttons which allow in-house counsel to call the high-ranking emails (in their native state) to the fore for review. We'll post a screencast to present the UI in the very near future.

On September 8, 2016, because its first interview with Nick Brestoff reached most viewed status in August, AI Trends asked him about the "business case" for using deep learning to achieve less litigation. That interview, "Why Intraspexion's Use of Deep Learning May Be Deeply Valuable," is here:

On August 30, 2016, NVIDIA (NASDAQ: NVDA) informed Intraspexion that it had been accepted into NVIDIA's Inception Program for AI startups. The program, announced on June 8, 2016, will supercharge Intraspexion's development. NVIDIA's program overview is here:  

On August 22, 2016, AI Trends published an interview with Nick Brestoff. The article was (by far) the most viewed article that month, despite the late publication date. The article, "Here's What Happens When A Deep Litigator Understands Deep Learning," is here:

When AI Trends asked Brestoff to explain why Intraspexion was attracting such high interest, Brestoff put it this way: "Litigation is like a dragon.It burns up profits, and no one's been able to curtail it. Intraspexion is a dragon-slayer with a new weapon. A deep learning sword." 

Patent pending.

On July 1, 2016, Nick Brestoff filed a provisional patent application entitled "Using classified text and deep learning algorithms to identify risk and provide early warning." The application follows decisions by the Court of Appeals for the Federal Circuit in Enfish LLC v. Microsoft Corp., __ Fed. Cir. __, U.S. App. LEXIS 8699, 2016 WL 2756255 (Fed. Cir. May 12, 2016) and Bascom Global Internet Services, Inc. v. AT&T Mobility LLC, Case No. 2015-1763, __ Fed. Cir. __ (June 27, 2016) ("As is the case here, an inventive concept can be found in the non-conventional and non-generic arrangement of known, conventional pieces.").   

By the Way, What's the Business Case?

"Companies worldwide spend $3 trillion a year trying to prevent, detect and deal with some kind of misconduct. If you can just capture one-tenth of one percent, it's a $3 billion idea." -- Bennett B. Borden, partner & Chief Data Scientist, Drinker Biddle & Reath (July 2015).

With litigation risk in mind, the average cost of commercial tort litigation, per a Towers Watson report for 2001 through 2010, is an average of $160 billion "per year." But how much is that pain on a "per case" basis? As CEO Nick Brestoff showed in his book (Preventing Litigation: An Early Warning System, Etc., endorsed by Sir Richard Susskind), the cost (i.e., payouts for settlements and verdicts, defense attorneys fees, and administrative costs) is at least this much:

$350,000 per case


So if your company avoids just three average lawsuits, it preserves net profit of over $1 million.

There's a lot of work for us to do.

We offer our system only to enterprise legal departments, and there are about 10,000 legal departments in the U.S. "Of the 10,000 legal departments in the U.S., 31% have less than 10 attorneys, 59% have between 10 and 39 attorneys, and 10% have more than 40 attorneys on staff." Catching the Wave: Legal Technology Spend at $3 Billion and Growing, p. 2 (Mitratech Holdings, Inc., February 15, 2016).

Will the System Replace Attorneys?

No. Our system augments the intelligence of in-house attorneys, and empowers them to make the decisions. In-house attorneys are currently blind to the risks in their company's own internal communications, even though they are closest to that data. With Intraspexion, in-house counsel will be able to see those risks. Then they can investigate and advise control group executives who, in turn, will decide what action to take to avoid the lawsuit.   


Of course, you may say, "Bah, it can't be done."

But because we've learned how to train a deep learning algorithm to detect the risk of an employment discrimination lawsuit (for starters), we've already done it.

Please watch our (silent) "explainer" video. It takes only about five (5) minutes

Then please see the one (1) minute capstone video.

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