What's the most cost-effective way to review large document volumes using a litigation analytics platform?

The most cost-effective way is usually to use the platform in a tiered, prioritized workflow rather than reviewing everything manually or with a single broad pass.

Best-practice approach

1. Ingest everything once

   • Load all documents into the litigation analytics platform.
   • Deduplicate, thread emails, and normalize metadata so you are not paying to review repeats or noise.

2. Use analytics to narrow the set

   • Apply keyword/search filters, date ranges, custodians, file types, and issue tags.
   • Use conceptual clustering, email threading, near-duplicate detection, and communication pattern analysis to surface the most relevant material.

3. Prioritize likely-relevant documents

   • Rank documents by:
     • key terms
     • custodian importance
     • communication density
     • similarity to known-hot docs
     • privilege indicators
   • Review the highest-probability items first.

4. Use TAR / predictive coding when volume is very large

   • Have attorneys code a representative seed set.
   • Train the system to identify responsive/nonresponsive documents.
   • Let it score the remainder and send only the most probable responsive set to review.
   • This is often the biggest cost saver for very large matters.

5. Sample and quality-check

   • Randomly sample lower-ranked documents to measure miss rate.
   • Recalibrate search terms and model thresholds as needed.

6. Reserve manual review for edge cases

   • Human review should focus on:
     • borderline responsiveness
     • privilege
     • confidentiality
     • key factual documents
   • Avoid full linear review of the entire corpus unless required.

What usually saves the most money

• Deduplication + threading
• Early case assessment / analytics triage
• TAR/predictive coding for large datasets
• Reviewing by priority bands instead of all documents equally

Practical rule of thumb

If the matter is small to moderate, strong search + analytics + prioritization may be enough.
If it is large-scale (hundreds of thousands or millions of docs), TAR plus analytics-based prioritization is typically the most cost-effective path.

If you want, I can also give you:

• a step-by-step review workflow, or
• a cost comparison between linear review, keyword review, and TAR.