Your Workforce Moved to Chat. Your eDiscovery Workflow Didn’t.
StreemView reduces review volume by 90%+ through pre-RSMF search, conversation-level deduplication, and purpose-built processing for Teams, Slack, Discord, and mobile data.
Data reduction before review
More relevant messages
Copy per message regardless of custodians
The eDiscovery Industry Is Still Built for Email
Most platforms retrofit Teams, Slack, and Discord support onto email-centric architectures. The result: inflated volumes, broken context, and review costs that have nothing to do with what actually matters.
- Email-centric architecture retrofitted for chat
- RSMF created first, searched second — massive review sets
- Broken conversation threading, orphaned messages
- Custodian-level duplication multiplies volume 10–50×
- SharePoint attachments not linked to source messages
- No native understanding of reactions, threads, or context
- Native pipeline for every modern messaging platform
- Search and filter before RSMF — only relevant messages produced
- Full conversation threading preserved end-to-end
- Message-level dedup: one copy per message, all custodians retained
- SharePoint attachments auto-linked to originating messages
- Reactions, threads, and context treated as first-class evidence
Search Before You Commit. Reduce Before You Review.
Four sequential reduction layers eliminate volume, cost, and complexity before a single document enters your review platform.
Collection & Normalization
Native ingestion from Purview exports, Slack EML, Discord archives, and mobile extractions. Auto-separation of data types, format normalization, and metadata preservation before anything enters the pipeline.
Conversation-Level Deduplication
Message-level dedup across all custodians simultaneously. One copy per message, full custodian attribution retained. Eliminates the 10× to 50× volume inflation typical of multi-custodian chat collections.
Pre-RSMF Search & Filter
Apply Boolean, proximity, and natural language search across the full conversation graph before any document is created. Context windows auto-expand around hits. Only responsive messages proceed.
AI Early Case Assessment
Pre-built classifications surface likely non-responsive content, financial misconduct indicators, behavior misconduct patterns, and sensitive private content — before review begins.
See the Reduction in Action
A real matter: 39 million messages reduced to 1.1 million before a single document enters review. Click each stage to see how volume shrinks at every step.
Pipeline Progress
Current Metrics
Import & Normalization
Raw data ingestion and standardization
Messages Received
Purpose-Built for Every Modern Messaging Platform
Not retrofitted. Not bolted on. Each data source has a native ingestion pipeline purpose-built for that platform’s structure.
Microsoft Teams
Native Purview ingestion
Ingest MSG/PST exports directly from Microsoft Purview. Auto-link SharePoint attachments to originating messages. Full Microsoft 365 Copilot interaction support. Zero manual pre-processing.
Slack
Workspace-level collection
Full workspace ingestion — public channels, private channels, DMs, group DMs. Emoji reactions captured as first-class evidence with full attribution and timestamps.
Discord
Server & community investigations
Server-level collection with full channel and thread reconstruction. Handles enterprise Discord deployments and community investigations. Reaction attribution and attachment linking preserved.
Mobile Data
iOS, Android, WhatsApp & more
Process extractions from iOS and Android devices — SMS, iMessage, WhatsApp, and third-party messaging apps. Conversation threading and media attachment handling purpose-built for mobile data structures.
Two Controls That Make Chat Search Defensible — in Both Directions.
Chat eDiscovery has two distinct search problems: under-capture — where relevant messages are missed because terms fall across artificial day boundaries — and over-capture — where AND searches return results so far apart they share no real context. StreemView’s Context Window and Hit Window address both.
Capture What Surrounds the Hit,
Not Just the Hit Itself.
When a search term hits a message, Context Window automatically pulls in the neighboring messages — the conversation before and after — so reviewers see the full exchange, not an isolated result. ESI protocols increasingly address context windows for short-message content; StreemView implements them precisely and reports direct hits and context-expanded hits separately.
Window Size Options
All messages tagged for in-platform review or selective RSMF export.
Context Window Expansion Around a Search Hit
Stop AND Searches from Reaching Too Far —
or Not Far Enough.
Hit Window controls how far apart two terms can be in a conversation before an AND or proximity search stops counting them as a hit. Set it to Same Day (Relativity’s default) and you under-capture — missing hits that span a midnight boundary. Remove it entirely and you over-capture — returning terms with no real relationship. Hit Window gives you control in both directions.
Hit Window Presets
Same Day matches Relativity behavior for defensible comparison. User Defined enables fine-tuned ESI protocol compliance.
AND Search: “approve” AND “wire transfer”
4-minute conversation split by midnight → no hit returned. Relevant message lost.
Terms within 30-day window → hit returned, conversation surfaced for review.
No temporal relationship between terms → false positive without a Hit Window constraint.
Real-World Impact · AM Law 200 Slack Matter · 700K Messages · 5,400 Conversations · ±10 Context Window · 30-Day Hit Window
Preserve Everything.
Pay Only for What
Matters.
Modern matters arrive with tens of thousands of attachments. Traditional platforms bill for all of them from day one. StreemView’s two-tier model changes that calculus.
Tier 1 — Preservation Only
All attachments ingested at a fraction of full cost. Defensible coverage from day one.
Tier 2 — Selective Activation
Activate only the attachments linked to relevant messages. Everything else stays preserved at Tier 1 — never billed at full rate.
Example: 10 Custodians — Teams Matter
Never billed at full rate
36,200
Cost reduction
90.5%
Message Level Decision Making
Review, tag, and disposition individual messages directly within their conversation context. Hover over reactions to see who responded, select messages in bulk, and apply Responsive or Non-Responsive tags without leaving the thread.
- Hover over reactions to see exactly who reacted
- Select individual or multiple messages for bulk tagging
- Apply Responsive or Non-Responsive tags in one click
We need to move forward with the revised terms — Sarah confirmed approval in the thread above 👍
Original: Confirmed - reviewed the final version this morning. Good to proceed.
Updated: Confirmed - reviewed the final version this morning. Good to proceed. Looping in @Marcus Chen for final sign-off
Reviewed and aligned on the terms. Consider this my formal sign-off. 🖊️
Finding the Needle Is the Hard Part.
We Built the Magnet.
In enterprise chat data, >90% of messages are non-responsive in any given matter. Without AI purpose-built for short-message content, reviewers spend the majority of their time reading noise — not finding evidence. StreemView’s AI surfaces what matters before review begins.
AI Q&A
Ask questions in plain English. StreemView surfaces cited answers directly from your custodial data with source attribution.
Based on 14 messages across 3 custodians:
Yes, there are discussions between James Baltimore and David Johnson discussing the reverse engineering of the ACME Systems solution.
Natural Language Search
Search using plain English. StreemView translates your query into structured search syntax automatically.
Natural Language Input
“Messages from Jason Richard in Q1 containing mentions of merger that were not marked as responsive”
Generated Query
FROM:"Jason Richard" DATERANGE:2024-01-01/2024-03-31 AND (merger OR "merger discussion") AND NOT tag:responsive
Pre-Built Classifications
Automatically surface content patterns before manual review begins.
Common Questions
What makes StreemView different from traditional eDiscovery platforms?
Traditional eDiscovery platforms were built for email and documents. StreemView is purpose-built from the ground up for modern chat data — Microsoft Teams, Slack, Discord, and mobile messaging — with native ingestion pipelines, conversation-level deduplication, and pre-RSMF reduction that eliminates 90%+ of volume before review.
How does StreemView achieve 90%+ data reduction?
StreemView applies four sequential reduction layers: collection normalization, conversation-level deduplication (one copy per message regardless of custodian count), targeted search and filter before RSMF creation, and AI-enhanced early case assessment. Each layer reduces volume before any data enters your review platform.
What is pre-RSMF search and why does it matter?
RSMF (Relativity Short Message Format) is the standard production format for chat data. Most platforms create RSMFs first, then search them — resulting in massive review sets. StreemView searches and filters the full conversation dataset before RSMF creation, so only relevant messages become production documents.
Does StreemView support Microsoft 365 Copilot interactions?
Yes. StreemView provides full support for Microsoft 365 Copilot interactions within Teams data, including chain-of-thought visibility and proper separation from regular Teams chat content.
See 90%+ Reduction On Your Own Data
The best time to validate your modern data workflow is before a preservation notice lands. StreemView’s pilot program lets you test the full pipeline on real data — no production pressure, no surprises.
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