You may already know that Kira Systems’ state-of-the-art machine learning technology automatically identifies and extracts information from contracts and comes with over 900 built-in provision models. How do we do it? Our team of in-house lawyers trains Kira to automatically find provisions by giving it sample language of provisions found in publicly filed agreements. Once Kira has sufficient examples of a provision, it can easily find that provision in any new agreements that have been imported into it.
That said, did you know you can conduct deal points studies on the documents of your choice to find information of interest to you? What’s more, the information gathered can then be shared throughout your organization and used for drafting and negotiating future agreements or simply revising standard forms.
Putting Kira’s AI to the test
Recently, we used Kira’s machine learning technology to conduct an M&A deal points study of our own, specifically on non-competition provisions.
Our report summarizes our findings on non-competition provisions in recent private target M&A transactions. More specifically:
- the prevalence of non-competition provisions in private target M&A agreements publicly filed in 2018 and 2019;
- the prevalence and type of non-competition provisions in asset deals versus equity deals;
- the prevalence of non-competition provisions applicable to employees of the selling entity, and;
- the association between the type of selling entity (private equity versus venture capital) and the type of non-competition provision.
Why this study?
We wanted to cover non-competition provisions or agreements (non-competes) as they are routinely included as part of M&A transactions, whether in the primary transaction agreement itself or as an ancillary agreement. Understanding the relationships between non-competes and factors like deal structure and the type of selling entity provides invaluable insight into when it is reasonable for the language to be included, as well as to which parties the language should apply.
But, as with any commercial transaction, the parties’ work isn’t done just because a deal was signed. In fact, if not properly drafted (or drafted too broadly), non-competes can be litigated post-closing, and may even be completely eliminated prior to closing if antitrust agencies become involved in reviewing the transaction.
A recent example includes the Federal Trade Commission (FTC) issuing an administrative complaint challenging Axon Enterprise, Inc.’s acquisition of its competitor VieVu, LLC from parent company, Safariland. As part of the merger, Safariland is said to have “entered several non-compete and customer non-solicitation agreements covering products and services not related to the merger, and both Axon and Safariland entered into company-wide non-solicitation agreements that all run for 10 or more years”. Unfortunately, according to the FTC, the non-competes were deemed “not reasonably limited to protect a legitimate business interest”.
Having this kind of data readily accessible for analysis within Kira mitigates risk by keeping you informed without you having to spend valuable hours to find, collect and sort through information. A great advantage when it comes to negotiations.
Just some of our findings
- When we reviewed the results in our sample sets, we found that in the aggregate, 63% of the transaction agreements included non-competition language.
- In addition to quickly assembling aggregate statistics, Kira automatically creates an easily searchable archive of the actual precedent language. A review of the results in our sample set confirmed that the non-competition provisions include significant variations in their application to different types of parties involved in the transactions.
- We also used data from our sample set to determine the association between the prevalence of non-competition provisions and the type of selling entity. We included categories for private equity-backed and venture capital-backed sellers
Why not conduct your own deal point study?
Using Kira, you can create a similar analysis of your firm’s own deals (and even study a specific industry or geography) with significantly greater speed, consistency, and accuracy than a manual review. Kira allows you to unearth what’s standard practice for the many private transactions which are never publicly disclosed, and easily find real precedent clause language to apply to future transactions. Furthermore, Kira comes pre-trained to find provisions frequently negotiated during the course of an M&A transaction, which can assist in the tracking of market intelligence information.
For a more in-depth discussion of non-competition provisions in M&A, please download our full report today!
[ Artificial Lawyer is proud to bring you this sponsored thought leadership article by Kira Systems. ]