At Twin Health, AI is part of how the company operates. Jen Bealer wanted legal to meet that standard, too. Her goal was to move the team beyond manual processes and give the business better access to legal information, while preserving the judgment and oversight that its work requires.
That meant looking closely at where legal’s time was going. Routine contract questions, agreement preparation and ongoing regulatory research were all necessary. Much of the work of finding, assembling and updating information was also a candidate for a different approach.
When Jen considered where to put her budget, she made a choice that would shape the team’s next chapter.
Faced with a choice between legal technology and a legal engineer, Jen chose the engineer and says she is very happy she did.
Lawtrades connected Twin Health with a legal engineer whose legal operations experience and AI fluency helped the team begin translating those needs into working systems. The engagement gave Jen access to someone focused on building: understanding a process, connecting its information sources and making it easier for people to use.
For Twin Health, that investment has supported work well beyond agreement generation, including a Slack-based contract-information bot, a legal website and FAQ, and an evolving approach to regulatory research.
Starting with the questions legal gets every day
Jen describes the old routine as “filing cabinet work.” What are the termination rights in a particular agreement? What are its payment terms? Answering could mean logging into a repository, finding the contract and tracking down the relevant information.
The team now has a Slack-enabled bot connected to its contract repository in Salesforce. People can ask those questions where they already work. Legal has an easier way to retrieve the information, and colleagues have a self-service option for questions they previously brought to the team.
Building around the way the business contracts
The next opportunity was routine agreement generation. Twin Health works with commercial agreements, business associate agreements and nondisclosure agreements. Producing the right document involves more than inserting a company name: the team needs to select the correct form, template and services.
Jen’s team has been automating those choices and generating agreements on its own paper without a CLM. She describes the process as much faster than doing it manually. It is a practical example of what legal engineering can contribute: turning legal and operational requirements into a workflow people can actually follow.
Jen has also made AI-assisted first-pass contract review an expectation for her team, whether the agreement starts on Twin Health’s paper or a third party’s. The human review that follows remains essential.
Her budget decision reflects Twin Health’s needs at this stage. A legal engineer offered the flexibility to work across several processes, including problems that extended beyond contracts. The value came from combining knowledge of legal work with the ability to build and adapt the systems around it.
Reimagining the “color chart”
One of the more ambitious projects is still taking shape. Twin Health’s “color chart” brings together state-by-state information about provider licensing, affiliated practitioners and supervision requirements. The challenge is keeping those interconnected requirements current across all 50 states.
Historically, that work drew on internal effort, online resources and surveys, with outside counsel providing reference points for difficult or changing states. A resource covering one part of the picture did not necessarily address the others.
With the legal engineer’s help, the team is developing an AI-enabled research and updating process for that information. Changes are designed to be visible for legal review. The intended benefit extends to credentialing, compliance and the provider team, all of whom need access to relevant updates.
Jen sees a repeatable approach emerging. Once the licensing work is established, she plans to apply similar methods to other areas, including AI and privacy laws.
Making judgment part of the design
Jen’s approach to adoption is deliberately phased. Legal tests systems first, checking whether answers are accurate, predictable and grounded in the intended sources. Broader use comes with clear expectations about the technology and its limitations.
That discipline also guides where human review stays. The team reviews regulatory updates, and Jen expects continued oversight for high-materiality agreements and other consequential work. She can envision less human intervention for lower-materiality commercial matters as confidence grows, but describes that as a future possibility.
Jen is deciding which work can become self-service, which tasks benefit from an AI first pass and where professional judgment must remain firmly involved. Implementation includes those decisions, as well as the technology itself.
The work is ongoing. Some projects depend on API connections and internal approvals, which can slow progress. Jen is candid about those constraints while remaining enthusiastic about the direction. Building an AI-first legal team requires practical coordination as well as a clear ambition.
A new kind of capacity for legal teams
Twin Health’s experience points to an increasingly useful combination of skills: legal operations knowledge, process design and hands-on AI implementation. In this engagement, the legal engineer’s role is to help build the systems that make the team more capable.
That is the offering Lawtrades is helping legal teams access. For leaders with a clear operational problem and limited internal capacity to build, the right specialist can help turn an idea into a usable workflow, alongside the team’s own expertise and oversight.
Jen sees opportunity on both sides of that match.
“People who are hiring for legal don’t think of engineering. And engineers don’t think of legal.”
Jen Bealer · Twin Health
At Twin Health, she is bringing those disciplines together. Her team is making contract information easier to access, reducing manual agreement preparation and building toward a more maintainable approach to regulatory research. It is a concrete example of legal helping an AI-first company put that ambition into practice.
Twin Health | October 12, 2026