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eDiscovery Specialist

Produce documents to opposing counsel

Enhances✓ Available Now

What You Do Today

Generate production sets in required format (TIFF, native, PDF), apply redactions, bates stamp, create load files per specifications

AI That Applies

AI validates productions for completeness, catches redaction errors, and ensures production specifications are met before delivery

Technologies

Relativity ProductionIPROLAW

How It Works

For produce documents to opposing counsel, 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 Changes

Production QC is automated; AI catches errors (missing redactions, wrong format, metadata issues) before documents go out the door

What Stays

Production strategy decisions, managing rolling productions, and the negotiation about production format and scope

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 produce documents to opposing counsel, understand your current state.

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Map your current process: Document how produce documents to opposing counsel works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
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Identify the judgment points: Production strategy decisions, managing rolling productions, and the negotiation about production format and scope. These are the boundaries AI won't cross.
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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 Production 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 produce documents to opposing counsel 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 produce documents to opposing counsel?”

They set the firm's AI adoption posture

your legal technology manager

“Who on our team has the deepest experience with produce documents to opposing counsel, 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 produce documents to opposing counsel, 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.

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