A written opinion can be airtight and still get you excluded — because the fight isn't the report, it's the deposition, where opposing counsel attacks whether your method is reliable and whether you applied it to the facts under FRE 702. Rehearse that cross against a realistic AI examiner — in your field, not medicine — and see exactly where you'd be vulnerable.
Since the 2023 amendments to FRE 702, judges are gatekeeping harder and excluding more experts than ever. The record that gets you struck is built in your deposition — your qualifications, your error rate, whether you actually applied your method to this case's facts, and every "isn't that outside your field?" you didn't see coming. Most experts walk in having only re-read their report. That's the mistake.
Three steps. You're being cross-examined in under two minutes.
Enter your discipline — accident reconstruction, economics, structural engineering, toxicology, whatever it is — and pick which side is crossing you.
A realistic AI opposing counsel questions you out loud on your methodology and this case's facts — adapting to your answers the way a real examiner does.
Every answer is scored on a 5-axis Daubert rubric so you see exactly where a motion in limine would land — then run it again.
It doesn't lecture you on the rules — it makes you perform under them. A voice examiner that asks the hard questions in your field, adapts to what you say, and grades your composure, your consistency, and whether your method survives Daubert. Practice privately, as many times as you need, until the real thing feels routine.
The same FRE 702 / Daubert levers opposing counsel will pull — rehearsed until they're boring.
Where your expertise ends and where opposing counsel will argue you've strayed beyond it.
The core Daubert factors — whether your method is testable, has a known error rate, and is generally accepted.
The amendment excluding more experts every year: did you actually apply your reliable method to this case's facts?
The analytical gap between your data and your conclusion — where an opinion becomes unsupported assertion.
Your fee, your retention history, your ratio of plaintiff-vs-defense work — handled without getting rattled.
Answer only what's asked. Never guess. Pause. Don't volunteer. Scored on every answer.
The methodology attack is universal — often sharper outside medicine than in it. The examiner adapts to whatever field you enter.
Your first full mock deposition is free. Then pick what fits — a flat pass for one upcoming deposition, or a subscription if you get deposed regularly.
No charge for your first mock deposition. Not legal advice — work with retaining counsel.
Weighing your options? Compare training options: AI practice vs live courses (SEAK) — costs compared →
You rehearse a testable methodology before you publish it. Rehearse the deposition the same way. Free to try — start being cross-examined in under two minutes.
No. MedLegal AI started in medical malpractice, but the deposition trainer now cross-examines retained experts in any field — engineering, economics, forensics, accident reconstruction, toxicology, human factors, vocational, and more. You enter your discipline and the AI examiner attacks your methodology, not medicine.
No. This is educational deposition-preparation practice. It does not replace retaining counsel, who always has the final word on your testimony and your case.
It's free to try — no credit card. Create an account and you're dropped straight into a mock deposition. Paid plans add more session time and features.
Yes. Sessions are private by default and are not shared with retaining counsel, opposing counsel, or anyone else. Practice with your case's facts in general terms — don't enter confidential identifiers.
Yes. The examiner and the scorecard are built around the post-2023 FRE 702 standard — including the "reliable application to the facts" prong that is excluding a growing share of experts.
Fair question — a known error rate is an explicit Daubert factor under FRE 702, and almost no AI vendor can state one. We publish ours, with the method behind each figure, the date it was measured, and what it does not cover, at /validation. Where we haven't finished measuring something, we say so rather than publish a number.