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eDiscovery Specialist

Manage review platform and reviewer workflows

Automates✓ Available Now

What You Do Today

Configure review workspaces, build coding panels, manage reviewer access, monitor review progress and quality metrics

AI That Applies

AI monitors reviewer consistency, identifies coding outliers, and optimizes workflow routing to balance quality and speed

Technologies

How It Works

The system ingests reviewer consistency as its primary data source. 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 Changes

Review management is more automated; AI identifies reviewers who need retraining and optimizes document routing for efficiency

What Stays

Designing the review protocol, managing reviewer teams, and the project management that keeps massive reviews on track and budget

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 manage review platform and reviewer workflows, understand your current state.

Map your current process: Document how manage review platform and reviewer workflows works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Designing the review protocol, managing reviewer teams, and the project management that keeps massive reviews on track and budget. 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 Relativity 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 manage review platform and reviewer workflows 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

How would we know if AI actually improved manage review platform and reviewer workflows — what would we measure before and after?

They set the firm's AI adoption posture

your legal technology manager

If we automated the routine parts of manage review platform and reviewer workflows, what would the team do with the freed-up time?

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

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.