Features

Now that we’ve covered the many use cases and key evaluation questions, let’s get into useful features of Embeddable Contracts AI solutions. Note that the features of Embeddable Contracts AIs are a subset of Contracts AI software features. For example, a contract management system might have an approval workflow or include e-signature capability. While useful features, these wouldn’t be driven by Embeddable Contracts AI. When we were at Kira Systems (our previous business) much of our roadmap was non-AI features. And even “AI” features typically involve some AI and a whole lot of UI work. An Embeddable Contracts AI vendor can sometimes offer helpful feedback on how to implement an AI feature, but often builders of end systems incorporating Embeddable Contracts AI do this work themselves. This makes sense, in that the end product vendor is the expert on their application, customers, and workflow.

In this piece, we’ll outline a full spectrum of features that we see as potentially useful in Embeddable Contracts AI. Two important notes:

  • As of the time of writing this piece, our belief is that some of the features mentioned here are not yet available in any Embeddable Contracts AI system. We are laying them out here in an effort to be comprehensive and forthright. That is, even though you can’t (yet!) get all these features from us, they are ones that might someday be useful. We are working hard to continue enhancing our product, and ideally many or all of these features will eventually become available in Zuva API (and other vendors’ products too).
  • While having lots of features can be important, it can be even more key that a given system has the features you need most, and that they are well built. Since Embeddable Contract AI solutions tend not to be user-facing, it won’t hurt customer workflow if you implement multiple AI solutions under the hood. So, potentially, you can get multiple features from one vendor, others from another vendor, and build others yourself.

With that, let’s get to features!

Classification and Extraction

Contracts are generally in the form of mostly unstructured text.

Clustering

Clustering is the grouping of related information together.

Defined Term Detection

Defined term detection is tech that finds defined terms in contracts. Defined terms really matter.

Document Comparison

Document comparison shows differences between one document and another (or others, in the case of multiple document comparison).

Related Document Linking

Related document linking (aka related document grouping) attempts to automatically identify documents that are part of the same family.

Risk Scoring

Contract risk scoring assigns a risk grade to agreements (or clauses), based on some predetermined view of what constitutes contract risk.

Signature Detection and Recognition

Signature detection helps determine whether a document has been signed. A related feature identifies whether pages have any handwritten information on them.

Optical Character Recognition (OCR)

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