AI and machine learning move faster than the law, which is exactly why you want counsel who understands both. Our attorneys came out of software engineering, so when you describe your model architecture, training pipeline, or inference stack, we follow it without a translator. We help AI and ML companies protect what they build, license data cleanly, and stay ahead of regulation that keeps shifting under everyone's feet.
Patents, Models, and Outputs
AI raises ownership questions that off-the-shelf IP playbooks do not answer. We advise on what is patentable in your algorithms and training methods, who owns AI-generated content and inference outputs, and when a trade secret protects your weights and architecture better than a patent ever could. We also handle open-source license obligations baked into common ML frameworks, so a permissive-sounding dependency does not quietly compromise your proprietary stack.
Training Data and Licensing
Your model is only as defensible as the data behind it. We draft and negotiate training data licenses, scrutinize the provenance and usage rights of datasets you acquire or scrape, and structure agreements that spell out who may use outputs and derivatives. When you sell or embed your model, we write the development and service agreements that allocate data rights, performance expectations, and indemnities so that a downstream dispute does not land on you.
AI Regulation and Compliance
Rules for AI are arriving from many directions at once. We help you map obligations under the EU AI Act's risk-based tiers, track US state and federal AI initiatives, and apply sector-specific requirements in healthcare, finance, and transportation. We translate algorithmic accountability and transparency mandates into concrete steps your product and engineering teams can actually ship, rather than a policy document that sits unread in a shared drive.
Governance and Risk
Regulators, customers, and the public increasingly expect AI you can explain and defend. We help you build governance frameworks, bias testing and mitigation practices, and explainability documentation that hold up under scrutiny. We also work through the harder questions: who is liable when an AI decision goes wrong, how to allocate that risk across your contracts, and what your insurance and indemnification position should look like before something breaks.