Filing-grade legal research, not an AI legal assistant.
Most legal AI stops at “found a relevant case” and “did not invent a citation.” Both are table stakes now. The question that decides whether a brief can be filed is harder.
Everyone finds the neighborhood. We return the case you can file.
Did the research surface the proposition that does the legal work, with the correct stance, at the correct procedural stage, still good law, and currently citable in this forum? A real, verified, on-point case that was reversed last month is not a weaker answer. It is an unusable answer that looks identical to a winning one, and no cite-checker under deadline reliably tells them apart. Deep Research is built to.
Right proposition, right stance, right stage, good law, currently citable.
Propositions, not topics
Deep Research is proposition-level legal research. It does not retrieve “cases about X.” It retrieves the rule that says what you need, from the court that established it, still good law, favoring your party, citable in your forum.
Good law, checked case by case
Every confirmed authority gets a validity check for subsequent history: reversed, modified, vacated, review granted, abrogated by statute. A status problem flips the case before it can reach your draft, and the next currently citable authority takes its place.
Your forum’s hierarchy, not the internet’s
Binding versus persuasive authority in your court, the highest controlling authority on the point, and department splits flagged with the controlling line identified, so the better-known rule from the wrong department never ambushes you.
Stage-aware retrieval
Pleading-stage questions get pleading-stage holdings. A summary judgment case decided on an evidentiary record never masquerades as authority on a motion to dismiss.
The cite that hurts you, on purpose
Counter-authority is retrieved deliberately, with the distinguishing patterns to brief around it, so the adverse case arrives from us and not from opposing counsel.
No model in the citation path
Citations enter your memo only from the verified record; the language model never writes one. Measured fabrication rate on the benchmark: 0.00%.
The memo says so, and names the split.
A confident answer to a question reasonable judges decide differently fails in one of two ways: false confidence, which presents a contested conclusion as settled, or arbitrary resolution, which silently picks among legitimate readings. Shallow ambiguity is a research problem, and more retrieval solves it. Genuine ambiguity is a property of the law: courts acknowledge the split, hedge, dissent with vigor, or drift over time. Deep Research reports that state as what it is, so you argue risk where the law is open and certainty only where it is not.
Contested law, in How it works →Ask, review the decomposition, approve, file.
- Describe your scenario in plain language, or dictate it
Up to a page of facts, the question, the posture and the forum.
- Review and edit the decomposition
Facts, issues, exceptions and follow-up questions, with a complexity badge. Approve when it is right.
- Get the memo
Verified citations and a citation table. Select any passage for a deeper expansion.
- Keep the history
Every session is kept. Relaunch past research with changes without losing the original.
A defendant moving for summary judgment on constructive notice must tender evidence identifying the specific area inspected, the time of the last inspection, and the employee who performed it. Generalized testimony about routine maintenance does not carry the initial burden, and where that burden is not met the motion must be denied regardless of the sufficiency of the opposing papers.
Sample drawn from a New York constructive-notice memo. Facts hypothetical. Not legal advice.
Bring one live question.
In a demo we verify the law you would file on it and show you every proposition with its source attached.