The Significant Cost of Going Direct to RSMF: $1.1MM Saved

This case study demonstrates the material cost, time, and risk reduction achieved by avoiding a "direct-to-RSMF" workflow for chat data and instead applying search, filtering, and contextual expansion prior to RSMF formulation using StreemView. By operating on native chat messages first — rather than prematurely committing data into static 24-hour RSMFs — the legal team produced a dramatically smaller, more relevance-dense review population.

The outcome: a 97% reduction in review volume, a 96% reduction in review cost, and substantial downstream efficiencies that would not have been achievable in a traditional workflow.

Background and Challenge

Modern chat data (Slack, Teams, text messages, and similar platforms) is fundamentally conversational — not document-centric. Traditional workflows that promote all collected chat data directly into 24-hour RSMF files before any search or filtering treat a message feed the same way they treat a static document. The result is a massive, context-bloated review population where each search hit drags in an entire calendar day's messages — most of them irrelevant.

This matters more when the data volume is large.

Data Profile

Rather than committing this population to RSMF upfront, the team used StreemView to interrogate the data in its native conversational form.

The StreemView-First Workflow

1. Search Before Structure

All negotiated search terms were applied before any RSMF files were created, ensuring relevance decisions were made at the message level.

Direct search hits identified: 97,695 messages

2. Hit Window Contextual Expansion (±5 Messages)

StreemView automatically expanded each hit to include five messages before and after, preserving conversational context and eliminating the need for manual splicing during review — without over-capture.

3. Selective RSMF Export

Only messages deemed relevant plus their contextual window were promoted into RSMF for downstream review.

Final export set: 1,155,283 messages — 57 GB total

What a Direct-to-RSMF Workflow Would Have Produced

Analytical modeling showed that if the same data had been promoted directly into 24-hour RSMFs before searching, every keyword hit would have pulled in all messages from that calendar day — ballooning the review population with irrelevant content.

Message volume reduction: 97%

Review Cost Impact

Modeling assumptions: 600 messages/hour review rate, $40/hour review cost.

Estimated review cost reduction: $842,861

Hosting Cost Impact

Modeling assumption: $12/GB/month over an 18-month matter lifecycle.

Estimated hosting savings: $133,707

Total Modeled Cost Comparison

Total estimated savings: ~$976,568

Why This Matters

This case study illustrates a core truth of modern eDiscovery: once you commit chat data to RSMF, you inherit all of its inefficiencies. By delaying RSMF creation until after relevance decisions are made, legal teams:

• Eliminate massive volumes of non-responsive content before review begins
• Reduce redaction and splicing complexity significantly
• Accelerate review timelines
• Lower hosting and review workspace costs by an order of magnitude
• Improve reviewer accuracy by delivering relevance-dense records

These gains are not theoretical. They are the direct result of treating chat data as chat data — searchable, contextual, conversational — rather than forcing it prematurely into a document paradigm it was never designed for.