Skip to content

Paralegal

Prepare deposition summaries

Enhances✓ Available Now

What You Do Today

Read deposition transcripts, create page-line summaries organized by topic, flag key admissions and contradictions, and prepare digest formats for attorney use at trial.

AI That Applies

Deposition analysis AI generates topical summaries with page-line citations, identifies key admissions, contradictions, and impeachment material, and cross-references against other testimony.

Technologies

How It Works

For prepare deposition summaries, the system identifies key admissions. NLP models process the text input by identifying entities, classifying intent, and extracting the structured information needed for downstream decisions. The output — topical summaries with page-line citations — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

A 300-page deposition summary that took a full day is produced in an hour. AI catches contradictions between depositions that manual review might miss.

What Stays

You still verify AI summaries against the transcript, flag the strategically important testimony that AI might not recognize, and organize the digest for the attorneys' specific needs.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for prepare deposition summaries, understand your current state.

Map your current process: Document how prepare deposition summaries works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: You still verify AI summaries against the transcript, flag the strategically important testimony that AI might not recognize, and organize the digest for the attorneys' specific needs. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support NLP tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long prepare deposition summaries takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your general counsel or managing partner

What data do we already have that could improve how we handle prepare deposition summaries?

They set the firm's AI adoption posture

your legal technology manager

Who on our team has the deepest experience with prepare deposition summaries, and what tools are they already using?

They manage the tools and can show you capabilities you don't know exist

a client who's adopted AI in their legal department

If we brought in AI tools for prepare deposition summaries, what would we measure before and after to know it actually helped?

Their expectations for outside counsel are shifting

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.