AI-trained model for commercial contracts

We are currently configuring our hosting so as to support (at high performance) a fully AI-trained model for commercial contracts. Weagree’s AI-tooling will automatically recognise more than 40 contract provisions in the first-draft contract proposed by your counterparty, retrieve the CLM data out of those provisions, qualify and categorise them for their inherent risk impact, and identify the provisions for (automated) mark-up. The 40+ provisions for which the AI-tooling in the Weagree Wizard will be equipped include all common parameters for which contract lifecycle management is usually configured:

* Document name
* Parties
* Signing date
* Effective date
* Expiration date
* Renewal term
* (Automatic) extension or renewal
* Termination notice period
* Termination for convenience
* Applicable law
* MFN (most-favoured customer)
* Non-compete
* Exclusivity
* Non-solicitation of customers
* Non-enticing away of employees
* Non-disparagement
* Right of first refusal, offer or negotiation (ROFR/ROFO/ROFN)
* Change of Control
* No-assignment
* Revenue/profit sharing
* Price restrictions
* Minimum purchase commitment
* Volume restrictions
* IP ownership assignment
* Joint IP ownership
* License grant
* Non-transferable license
* Affiliate IP license- licensor
* Affiliate IP license- licensee
* Source code escrow
* Post-termination services
* Audit rights
* Uncapped liability
* Cap on liability
* Liquidated damages
* Warranty duration
* Insurance requirement
* Covenant not to sue
* Third-party beneficiary

Not all automated review parameters are equally effective (there will be ‘false positives’ and ‘false negatives’) but for the frequently used ones, the AI results will be surprising.

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Terms of Use

I hereby accept (or reconfirm my acceptance of) Weagree’ Terms of use, in which: