AI consulting · Trial teams
Your students already use AI. Is it making them better advocates?
Used badly, AI produces students who can generate an argument and can't defend one. Used well, it gives every student more reps, sharper feedback, and a tougher opponent. I help programs get the second result.
Who it's for
Directors, coaches, law schools, and competition programs.
I coach a trial team myself. I know what a practice week costs, how coaches burn out, and why a program that runs on one person's memory falls apart the year that person leaves.
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Case preparation without outsourcing the thinking
Where AI belongs in case analysis, and where students have to do the work themselves.
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Repeatable coaching systems
Rubrics, practice plans, and feedback that stay consistent across coaches and seasons.
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Personalized practice
Workflows that give each student targeted reps on their own weak spots.
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Simulated witnesses and drills
AI witnesses for cross practice, objection drills, and adversarial review of a case theory before a real opponent finds the gaps.
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Training coaches and students
Responsible use, academic integrity, and the competition rules your teams compete under.
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Measuring improvement
Simple ways to see whether a student is getting better, not just getting more practice.
How an engagement runs
Assess, build, train.
Each stage has its own scope and its own deliverables. You can stop after any one of them.
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Assess
How your program prepares, practices, and evaluates today.
- Conversations with the director and coaches
- A review of your practice and feedback routines
- A written plan for where AI helps and where it shouldn't
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Build
The practice tools and systems that made the cut.
- Rubrics and practice plans
- AI witness and drill setups for your case materials
- Written guidelines for responsible student use
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Train
Coaches and students who can run it themselves.
- Working sessions for coaching staff
- Hands-on student workshops
- A follow-up check during the season
The rule I coach by
AI can prepare an argument. It can't prepare an advocate. Every system I help build keeps the student doing the hard part: choosing the theory, building the chapters, standing up and delivering.
The machine's job is to make practice harder and feedback faster. That's it.
Student data, integrity, and human judgment
Student work stays with your program. Student records and program materials belong to your school and your students. Anything that touches student data starts with your institution's policies, its FERPA obligations, and your students' consent.
Integrity and competition rules come first. Guidelines are written around your school's academic integrity policy and the rules of the competitions your teams enter.
Coaches make the coaching calls. AI supports practice. Decisions about students, roles, and strategy stay with the people who know them.