AI Playbook for Intellectual Property Due Diligence
A strategic acquirer is buying a software company for $220M. The target's primary product is built on a combination of proprietary code, open-source libraries, and licensed third-party components. A preliminary IP audit flagged 3 open-source libraries used under licenses (GPL, AGPL) that may have viral copyleft implications.
When to use this playbook
- Use this playbook when the decision looks like the situation above: A strategic acquirer is buying a software company for $220M.
- It is a fit when you have source files in hand and need a structured, reviewable analysis — not a generic chat answer about "Intellectual Property Due Diligence".
- Do not use it as a substitute for licensed, legal, clinical, or authorized official judgment in the domain.
What you'll need
- Software bill of materials (all open-source components, licenses, and versions)
- Proprietary source code ownership declarations
- Third-party license agreements
- GPL/AGPL analysis for the 3 flagged libraries
- Employee and contractor IP assignment agreements
Attachments: Documents (Documents)
The Prompt
You are an IP counsel conducting software IP due diligence for a $220M acquisition. I am attaching: Work only from the attached source files. If a conclusion is not supported, say so. Produce: 1. Assess the 3 flagged GPL/AGPL libraries: are they used in a way that triggers copyleft requirements (linked, embedded, or modified), and what is the disclosure or re-licensing obligation? 2. Identify any other open-source components with license conflicts (GPL mixed with commercial, CC-NC in commercial product) beyond the 3 flagged. 3. Assess the IP ownership chain: are there any employees or contractors whose IP assignment agreements are missing, incomplete, or jurisdiction-specific gaps? 4. Estimate the remediation cost if copyleft obligations require code replacement or open-source disclosure. 5. Tell me whether the IP issues are deal-killers, price-reducers, or manageable with representation and warranty insurance. Call out where independent models are likely to disagree, and list follow-up documents a reviewer should request.
What to expect
- Copyleft trigger analysis for 3 flagged libraries
- Full license conflict inventory
- IP ownership chain gap analysis
- Remediation cost estimate
- Deal impact classification (kill/reduce/manage) with R&W insurance assessment
Review before you act
- Validate this output against source files before relying on it: Assess the 3 flagged GPL/AGPL libraries: are they used in a way that triggers copyleft requirements (linked, embedded, or modified), and what is the disclosure or re-licensing obligation?.
- Validate this output against source files before relying on it: Identify any other open-source components with license conflicts (GPL mixed with commercial, CC-NC in commercial product) beyond the 3 flagged.
- Validate this output against source files before relying on it: Assess the IP ownership chain: are there any employees or contractors whose IP assignment agreements are missing, incomplete, or jurisdiction-specific gaps?.
- Validate this output against source files before relying on it: Estimate the remediation cost if copyleft obligations require code replacement or open-source disclosure.
- Confirm every cited figure, date, counterparty, or requirement against the attached originals — models compress and can drop a qualifier.
- Treat disagreement between models as a review item, especially on classification, materiality, and recommended next action.
- Do not authorize an operational, clinical, legal, credit, or enforcement action solely because the models agree.
Why compare models on this
For Intellectual Property Due Diligence, running the same attachments across independent models is useful because the hard part is classification and completeness, not fluency. The workflow is already designed to surface copyleft trigger analysis for 3 flagged libraries; full license conflict inventory; ip ownership chain gap analysis; remediation cost estimate. Those are comparison artifacts — they only exist if more than one model runs. Models disagree on whether revenue is pull-forward, whether a contract is terminable, and how much working capital to normalize. Those fights are the diligence memo.
See governed multi-model AI on your own prompt
Compare GPT-5, Claude, and Gemini side by side, with human review and a decision record built in.

