Something is being licensed.
Your technology, someone else's, or a dataset, and the scope of the grant will define the business for years.
The deal should match the architecture. We structure licensing, data, and platform agreements around how the technology actually works.
The most expensive technology deals are the ones where the contract describes a product that does not exist: rights granted that the architecture cannot enforce, restrictions promised that the integration ignores, ownership language that falls apart the first time a model is trained on shared data.
We structure technology transactions from the system diagram outward. Who owns what, who may use what, for which purpose, at what scale, and what happens when the relationship ends. The economics follow the architecture, not the other way around.
Your technology, someone else's, or a dataset, and the scope of the grant will define the business for years.
An API integration, platform listing, or embedded component is creating dependencies the standard terms do not cover.
Joint development, custom builds, or AI training on shared data has raised the question of who owns the result.
We map what is being built, hosted, accessed, and exchanged before anyone drafts a grant clause.
Ownership, usage rights, restrictions, and risk are assigned to match how the system actually runs.
The agreement is built around the allocation, with the leverage spent on the terms that survive the term sheet.
We walk the exit before signing: termination, data return, transition, and what each side keeps.
Tell us what is being licensed or integrated, who owns the technology or data today, and the commercial model behind the deal. Keep confidential details out until conflicts are cleared and an engagement is signed.