Mobile Data Is the Last Dark Corner of eDiscovery. Not Anymore.
iMessage. WhatsApp. SMS. Signal. The most sensitive corporate communications often live on personal devices in apps that traditional eDiscovery tools weren’t built to handle. StreemView processes mobile data with the same conversation-first approach applied to enterprise platforms.
Request a Mobile Data DemoMobile Data, Handled Natively
Six capabilities that bring the same conversation-first rigor to mobile data that StreemView applies to enterprise chat.
iOS & Android Extraction Processing
Process device extractions from both iOS and Android. StreemView normalizes data from Cellebrite, GrayKey, and other standard forensic extraction formats.
iMessage & SMS Threading
Full thread reconstruction for iMessage and SMS conversations. Read receipts, reactions, message effects, and thread metadata are preserved and searchable.
WhatsApp Conversation Processing
WhatsApp conversations — individual and group — are processed with full media handling, contact attribution, deleted message metadata, and thread integrity.
Third-Party App Support
Signal, Telegram, and other third-party messaging apps are supported. Coverage across the modern mobile messaging ecosystem — not just the platforms that are easy.
Cross-Device Deduplication
One conversation, one copy — even when the same thread exists across multiple devices, backups, or extractions. Cross-device dedup eliminates redundant review.
Pre-RSMF Reduction Before Export
Search and filter across full mobile conversations before RSMF creation. Only responsive message threads enter the production set.
The Complete Mobile Narrative
Mobile discovery isn’t just text messages. The full picture of a custodian’s mobile activity — calls, notes, voice memos, location data — often provides essential context that messages alone cannot.
StreemView supports call logs, voicemails, voice memos, notes, and contact data — allowing legal teams to correlate mobile activity with messaging, validate timelines, and surface the complete mobile narrative.
12 Devices. 4.9M Messages. 88% Reduction.
A government fraud investigation required analysis of communications across 12 mobile devices — iMessage, SMS, and third-party apps — with significant cross-custodian duplication. Standard practice would have delivered 741,000 review records. StreemView delivered 86,000.
Mobile devices
iMessage, SMS, 3rd-party apps
Messages ingested
Across 500K conversations
Key individuals normalized
Cross-device attribution
Direct cost savings
Review fees alone
StreemView Workflow
- 1
Data Consolidation
All 12 device extractions ingested and normalized into a unified dataset across formats, OS versions, and extraction sources.
- 2
De-Duplication
Conversations appearing across multiple custodian devices reduced to a single review record with full custodian attribution.
- 3
Profile Normalization
23 key individuals identified and unified across devices — resolving different numbers, names, and identifiers per custodian.
- 4
Pre-RSMF Filtering
Keyword filtering applied to the normalized, de-duplicated dataset. RSMF conversion applied only to responsive conversations.
Standard Mobile Workflow (baseline)
Full dataset converted to RSMF before any search or filtering — presenting the complete raw volume to outside counsel at standard per-document review rates.
StreemView-First
RSMF conversion applied only to conversations identified as potentially responsive — after deduplication, normalization, and keyword filtering on the full conversation graph.
88% RSMF volume reduction · ~$100K direct review savings · Auditable, defensible workflow
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
Mobile Data eDiscovery Questions
What mobile data sources does StreemView support?
StreemView supports iOS and Android device extractions, iMessage, SMS, WhatsApp (individual and group conversations), Signal, Telegram, and other third-party messaging applications. Both device-level extractions and application-level exports are supported.
How does StreemView handle data from forensic extractions?
StreemView processes forensic extraction outputs from industry-standard tools including Cellebrite and GrayKey. The pipeline normalizes data across extraction formats and reconstructs conversations from the extracted data structures.
How is WhatsApp data different from SMS or iMessage?
WhatsApp uses its own message protocol and data format, with group conversations, media, voice notes, and document sharing that don't exist in SMS. StreemView processes each app's data natively — preserving the structure, metadata, and content types specific to each platform.
Can StreemView deduplicate data across multiple devices from the same custodian?
Yes. Cross-device deduplication is a core capability. If the same iMessage conversation exists on a custodian's iPhone, iPad, and Mac, StreemView produces one copy with attribution to all devices — eliminating redundant review without losing any custodial context.
What about deleted messages and call logs?
When deleted messages are recovered in a forensic extraction, StreemView preserves them with their deletion metadata. Call logs, voicemails, voice memos, and other mobile data types beyond messaging are also supported for complete mobile narrative reconstruction.
Mobile Data eDiscovery Insights
Case studies and deep dives on mobile data in litigation and investigations.
The Significant Cost of Going Direct to RSMF: $1.1MM Saved
How searching first across 39 million chat messages produced a 97% reduction in review volume and nearly $1.1 million in cost savings.
Read case study →Hiding Below the Surface: StreemView Uncovers 500% More Relevant Messages
A side-by-side comparison showing how StreemView's contextual threading recovers messages that keyword-only searches miss entirely.
Read case study →Large and Complex Mobile Phone Investigation: 88% Review Volume Reduction
How StreemView processed 4.9 million messages across 12 mobile devices, reducing 741,000 RSMFs to 86,000—saving approximately $100,000 in review costs.
Read case study →Have a Matter with Mobile Data?
Tell us about the platforms involved and we’ll walk you through exactly how StreemView handles it.
Request a Mobile Data Demo