Modern Message Viewer

TL;DR — One Timeline to Rule Them All

Imagine an attorney at 9 p.m., deep in an insider-trading investigation. Slack exports in UTC locked inside JSON files. iMessage chats in PST split across multiple archives. Phone-call logs as bare CSVs with no timezone or labels. The partner wants answers by dawn, and the story is buried under a mountain of mismatched files.

I designed a unified conversation viewer that merges Slack, SMS, Teams, Bloomberg, email, and voicemail into a single chronological timeline — with every message retaining its legal-grade metadata. Demoed to Am Law 100 firms and federal agencies. 100% of attendees opted into beta immediately.

Sole designer, but the shape of this came from sitting next to the engineers who own the ingest parsers and the reviewers who live in these exports. The card sort below was run with four reviewers and two engineers — their clustering, not mine, was what made the Context / Evidence / Forensics hierarchy obvious.

Note: All case-study content has been redacted or AI-generated for confidentiality.

Unified timeline — messages from every platform, one scrollable narrative

The Fragmentation Problem

Five fragmented sources → one chronological timeline with platform-origin tracking

Electronic evidence now spans Slack, SMS, Teams, Bloomberg chat, and more — but legacy review tools were built for email. Reviewers manually reconstruct timelines across applications, copy-pasting timestamps into Excel, guessing at timezone conversions. Context gaps mean missed signals: coded language goes undetected, timing subtleties between platforms disappear.

The goal: make reviewing Slack and text messages as natural as reviewing email — one coherent, chronologically ordered narrative that preserves every piece of legal metadata.

What Already Existed (and What Didn't)

“What if every message was a document?”

The shift that unlocked it was treating every timestamped item — a Slack message, a Teams emoji reaction, a voicemail transcription — as a first-class document in the existing review table, instead of building a separate viewer per platform. One row per message, metadata intact, sorted by time.

This meant zero database restructuring — the viewer plugs into the existing schema. And it made the system extensible: adding Bloomberg or Discord support is a new parser, not a new product.

LLM-assisted analysis layer — AI summaries and sentiment alongside the human-readable timeline

Listening Tour

I interviewed litigation partners, corporate investigators, and expert reviewers across multiple firms. Mapped their data collection types (Slack, WhatsApp, Bloomberg, Cellebrite), the tools they were already using (Relativity, Concordance, Excel timelines), and where things broke: manual thread merging and timestamp coordination errors. The same pain points, every firm.

Design Process

Started with a taxonomy card-sort — reviewers and engineers clustered 50 real Slack, SMS, and email records into natural groupings. Three mental buckets emerged: Context, Evidence, Forensics. That hierarchy anchored the UI: conversation flow first, evidence markers second, forensic metadata on demand.

Card sort with four reviewers and two engineers — the three clusters became the page layout

The design principle became “Context First, Details on Demand” — each line shows sender, platform styling, and date banners at a glance. Hover reveals configurable metadata: IP addresses for fraud analysts, privilege flags for litigation reviewers. Different investigators see different layers of the same timeline.

After the Figma prototype demoed to Am Law 100 firms, global-bank compliance teams, and federal agencies, the feedback was specific and actionable: stronger visual breaks between conversations, a toggle between chronological and conversation-grouped views, and a V2 deduplication workflow for overlapping exports.

What Shipped

Released as part of a major platform update. Review time in mixed-data investigations dropped up to 30%. Tagging and timeline errors fell significantly. Audit trails became export-ready — a scrollable narrative instead of a reconstructed spreadsheet.

What actually changed was comprehension. Analysts could see the patterns across platforms that the old tools buried — the Slack message sent 30 seconds before the Bloomberg trade, the iMessage that disappeared from one archive but persisted in another. The story was in the data the whole time. The tool just had to stop hiding it.

See it in action

Explore the interactive Figma prototype — browse conversations, review metadata, and experience the unified viewer.

Interactive prototype