Lowenstein represents both forward-deployed AI engineering firms and the enterprises, investors, and growth-stage companies that engage them, giving our team firsthand insight into how these complex partnerships are structured and negotiated. Our experience representing all sides of these transactions shapes our counsel on the full range of issues that define these engagements, and the bespoke agreements that govern embedded applied-AI development from discovery through deployment.
AI Engineering Firms
Forward-deployed AI engineering teams are specialized firms that embed directly inside their clients’ organizations, working alongside internal engineers, product managers, and subject matter experts to design, build, and ship production-grade AI systems. These teams advise, write code, deploy infrastructure, and transfer knowledge, functioning as a seamlessly integrated extension of the client’s own team. Our clients on this side of the table include some of the leading applied AI firms in the country, organizations that have delivered measurable, production-ready outcomes for companies across healthcare, financial services, logistics, enterprise software, and beyond.
Enterprises and Investors
We also represent the companies on the other side of these engagements: enterprises, private equity-backed platforms, and the growth-stage companies outsourcing from forward-deployed AI engineering teams to transform their operations. Our team understands what these partnerships look like in practice, including:
- Structuring outcomes-based fee arrangements
- Allocating IP rights in co-developed systems
- Managing sensitive data that flows to an outside engineering team
- Building governance mechanisms to keep fast-moving development work aligned with enterprise risk and compliance requirements
The agreements that govern these relationships are highly bespoke and shaped by the speed, complexity, and novelty of the arrangements. A forward-deployment engagement may involve:
- Embedded engineers building agentic systems on a client’s own infrastructure
- Outcomes-tied compensation structures where fees are contingent on measured performance thresholds
- Retainer arrangements giving sponsors on-demand access to AI expertise throughout a diligence process
- Multi-phase engagements that begin with structured discovery and proceed to full implementation with reserved engineering capacity
Each structure demands careful, customized documentation that reflects the commercial realities of applied AI development, including how costs scale, how performance is measured, and how the client’s internal team is upskilled over time.
Lowenstein helps both sides of these engagements get to agreements that position all parties for the kind of transformational outcomes that make these partnerships worth pursuing in the first place.
