AI for Litigation Associates

What you do today

Track discovery deadlines, motion filing dates, court appearances, and internal milestones — missing a deadline can be malpractice

AI that applies

AI calendaring tools auto-calculate deadlines from court rules, track dependencies, and send progressive alerts as deadlines approach

How it works

For manage case deadlines and docket, the system track dependencies. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What you do today

Select and organize trial exhibits, create demonstratives, build the visual story that helps the jury understand complex facts

AI that applies

AI helps organize exhibits, generate timelines from evidence, and create visual presentations that simplify complex facts

How it works

For prepare trial exhibits and presentation, the system draws on the relevant operational data and applies the appropriate analytical models. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — timelines from evidence — surfaces in the existing workflow where the practitioner can review and act on it.

What you do today

Search Westlaw/Lexis for relevant case law, analyze holdings, synthesize authority into persuasive legal memos that frame the winning argument

AI that applies

AI legal research tools find cases, analyze holdings, and draft research memos — reducing the initial research phase from hours to minutes

How it works

The system ingests hours to minutes as its primary data source. NLP models process the text input by identifying entities, classifying intent, and extracting the structured information needed for downstream decisions. The output is a first draft that captures the essential structure and content, ready for human editing and refinement.

What you do today

Write motions to dismiss, summary judgment briefs, discovery motions — building the written advocacy that wins cases before trial

AI that applies

AI generates first drafts of legal briefs from research and case facts, following court-specific formatting and citation requirements

How it works

The system ingests research and case facts as its primary data source. A language model generates initial drafts by synthesizing the input context with learned patterns, producing text that follows the specified tone, format, and domain conventions. The output — first drafts of legal briefs from research and case facts — surfaces in the existing workflow where the practitioner can review and act on it.

What you do today

Review thousands of documents for relevance, privilege, and responsiveness — the most time-intensive phase of litigation

AI that applies

TAR/predictive coding classifies documents using ML, reducing the volume requiring human review by 60-80%

How it works

For review documents in discovery, the system review by 60-80%. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What you do today

Assess case value, participate in mediation and settlement discussions, draft settlement agreements and releases

AI that applies

AI models case outcomes based on judge history, jury demographics, and comparable verdicts — informing settlement value range

How it works

For negotiate settlement terms, the system draws on the relevant operational data and applies the appropriate analytical models. The analytics engine aggregates data across sources, applies statistical analysis to identify significant patterns and outliers, and presents the results through visualizations that highlight what needs attention. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What you do today

Identify experts, manage engagement, review expert reports, prepare experts for deposition and trial testimony

AI that applies

AI searches expert databases, analyzes prior testimony and Daubert challenges, and identifies the best expert for your specific issues

How it works

The system ingests prior testimony and Daubert challenges as its primary data source. The analytics engine aggregates data across sources, applies statistical analysis to identify significant patterns and outliers, and presents the results through visualizations that highlight what needs attention. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What you do today

Record time contemporaneously (or more realistically, reconstruct your day at night), write descriptions that comply with client billing guidelines

AI that applies

AI captures time from your calendar, email, and document activity — generating entries with descriptions that comply with billing guidelines

How it works

For track billable hours and manage billing, the system draws on the relevant operational data and applies the appropriate analytical models. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What you do today

Outline deposition questions, review relevant documents, take or defend depositions — the live examination where cases are won and lost

AI that applies

AI analyzes prior testimony, organizes exhibit sets, and identifies inconsistencies between documents and prior statements

How it works

The system ingests prior testimony as its primary data source. A language model processes the input by identifying relevant context, generating appropriate responses, and structuring the output to match the expected format and domain conventions. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What you do today

Meet with clients and witnesses, review anticipated questions, practice testimony, ensure witnesses understand the process without coaching on substance

AI that applies

AI simulates cross-examination scenarios, identifies likely attack angles from opposing counsel's filings, and generates practice questions

How it works

The system ingests opposing counsel's filings as its primary data source. A language model processes the input by identifying relevant context, generating appropriate responses, and structuring the output to match the expected format and domain conventions. The output — practice questions — surfaces in the existing workflow where the practitioner can review and act on it.