AI Agentic Workflow for Deposition Designation and Counter-Designation

This workflow automates the high-stakes, repetitive bottleneck of manually reviewing hundreds of deposition transcript pages to identify and clip key testimony for trial. By deploying specialized NLP agents to analyze argument strategy, detect pivotal testimony on liability or damages, and flag inconsistent statements, legal teams can reduce designation prep from weeks to hours. The operational upside comes from accelerating trial strategy, improving the quality of evidentiary presentations, and freeing senior attorneys for higher-value work, directly impacting case economics and readiness.

Implementation requires integrating the orchestration layer (e.g., LangGraph) with document management systems like Relativity and video platforms such as CaseNotebook or TrialPad via API. The architecture must include confidence scoring for each suggested clip, mandatory attorney review gates before finalization, and a full audit trail of all designations and counter-designations for defensibility. Practical constraints include handling poor OCR quality from scanned transcripts, managing exception routing for ambiguous testimony, and sequencing the rollout to parallelize work across multiple depositions without overwhelming review capacity.

This workflow automates the high-labor, repetitive task of manually reading deposition transcripts to flag video segments for trial designations. It directly cuts paralegal and junior attorney hours by 70-80% per deposition, accelerating trial prep and improving strategic consistency. The architecture ingests synchronized transcripts from platforms like CaseNotebook, applies specialized NLP agents for testimony scoring, and outputs time-coded clip suggestions with rationale for attorney approval.

Implementation requires integrating with the video deposition platform's API for clip export and the review platform (e.g., Relativity) for the attorney queue. Each agent's confidence scores and the guardrail's rule-based checks (e.g., avoiding privileged content) create a defensible audit trail. The system includes a counter-designation loop, where opposing counsel's designations trigger a separate agentic analysis to recommend rebuttal clips, ensuring a complete, automated workflow for this critical litigation task.

This workflow automates the high-cost, repetitive bottleneck of manually reviewing deposition transcripts to identify and clip key testimony for trial. The operational upside comes from transforming a 40-80 hour manual task per deposition into a 2-hour review, saving hundreds of attorney hours per case. Implementation requires ingesting synchronized transcripts and video from platforms like CaseNotebook or TrialPad, using specialized NLP agents to score testimony for relevance, credibility, and legal argument alignment before suggesting clips.

Implementation follows a phased delivery: first, a core designation engine with human-in-the-loop approval; second, integration of counter-designation logic and opponent clip analysis; third, full integration with courtroom presentation software. Critical controls include confidence scoring for each clip, mandatory attorney review gates, and a full audit trail of all AI suggestions and human overrides to ensure defensibility. The architecture is built to run within a firm's secure VPC, connecting to document management systems via APIs.

This workflow automates the high-cost, repetitive task of analyzing deposition transcripts to suggest video clips for trial. The operational upside comes from reducing paralegal and attorney hours spent manually reviewing hundreds of transcript pages, directly lowering litigation prep costs and accelerating strategy development. Implementation requires integrating with video platforms like CaseNotebook, embedding NLP models for testimony analysis, and establishing clear confidence thresholds for automated suggestions to trigger human legal review.

Controls are enforced through configurable confidence scoring, mandatory attorney review gates for low-confidence designations, and immutable audit trails logging all agent actions and human overrides. A phased rollout starts with a single case team, using their feedback to tune NLP prompts and exception routing before scaling across the firm. This governance layer ensures the workflow delivers cost savings without introducing evidentiary or procedural risk, making the automation both operationally and legally sound.