UX Case Study — Enterprise AI Platform
AIDEX — Document Intelligence, Redesigned.
Transforming a fragmented, manual document platform into a unified AI agent suite serving legal, compliance, and knowledge management teams.
Role
Lead UX Designer
Duration
14 Weeks
Platform
Web App · Desktop
Team
PM · 2 Engineers · SME
Year
2024
0%
Reduction in document processing time
0
AI agents shipped across 6 categories
0%
Task completion rate in Round 2 testing
0.0
Average satisfaction score out of 5
Contents
UX Case Study
Enterprise AI · 2024
01 — Problem Statement
The wrong tool for the job.
Legal and compliance teams were spending the majority of their working day doing tasks that were repetitive, error-prone, and cognitively exhausting.
"I always second-guess whether I caught everything. There's no way to know if I missed a PII item unless I re-read the whole document."
— Senior Legal Analyst, User Interview #4
Legacy Excel-based workflows — fragmented, manual, and error-prone
02 — Research & Insights
What the data actually said.
8 stakeholder interviews, a 2-week diary study, and competitive analysis across 6 enterprise document tools surfaced the real picture behind daily workflows.
Method
Stakeholder Interviews
8 interviews across legal analysts, compliance officers, and document operations managers. 60 minutes each with structured protocol.
Method
Diary Study
14-day diary study where participants logged every document-related task, tool used, time spent, and emotional state. 340+ task entries.
Method
Competitive Analysis
Evaluated 6 competing platforms: Kira Systems, Luminance, ContractPodAi, Harvey AI, Relativity, and Casetext.
"We've built elaborate Excel trackers, shared Google Docs, and Slack approval threads just to manage what should be one workflow."
— Document Operations Manager, Diary Study Week 2
Key Pain Points Identified
Pain Point
Context Switching
Users switched tools an average of 11 times per document review cycle. Each switch cost 3–7 minutes of reorientation time.
Pain Point
Output Anxiety
6 of 8 users expressed anxiety about whether they had caught everything. Manual review had no systematic completeness check.
Pain Point
Repetitive Cycles
70% of recurring workflows were identical week-to-week. No mechanism to save, reuse, or automate repeated task sequences.
03 — Target Users & Personas
Who we designed for.
Three distinct user archetypes emerged from research, each with different workflows, anxieties, and success criteria. Click each to expand.
Ananya — Legal Analyst
Mid-level · Legal & Compliance · 4 years
Tap to expand →
Rohan — Compliance Officer
Senior · Risk & Compliance · 8 years
Tap to expand →
Priya — Document Ops Manager
Lead · Document Operations · 6 years
Tap to expand →
04 — Information Architecture
Building the mental model.
The original platform had no clear mental model. I rebuilt the IA from scratch around three primary pillars.
Home
Dashboard KPIs Activity Feed Projects
Agents
Hub Catalog Landing Pages Configure Flow Results
Workflows
Templates Builder Run History Results
Workspace
Projects Documents Members Settings
User Guide
Getting Started Agent Guides Workflow Help FAQs
05 — Design Process
How we got from research to pixels.
A 14-week process across 5 distinct phases, each building directly on the last. No phase was skipped, no shortcut was taken.
Week 1–2
Discovery & Research
8 stakeholder interviews, 2-week diary study, competitive analysis across 6 platforms. Synthesized 340+ diary entries into 12 core insight clusters.
Week 3–4
Information Architecture & User Flows
Rebuilt navigation model from scratch. Mapped task flows for all 12 agent types. Validated IA with 3 participants via card sorting.
Week 5–7
Wireframing & Ideation
Low-fidelity wireframes for all 13 primary screens. Explored 3 approaches for the workflow builder. Node canvas with template-first entry won.
Week 8–11
High-Fidelity Design & Design System
Built 40+ component library in Figma. Designed all 12 agent flows, workflow builder, workspace, and AI assistant.
Week 12–14
Usability Testing & Iteration
Two rounds of testing with 5 participants each. Task completion rate improved from 60% to 94% between rounds.
06 — Before vs After
The transformation.
The same user goal — review and process a contract — through two radically different experiences.
Before
4–6 separate tools for summarization, classification, PII detection, and review
No shared workspace or project continuity between sessions
No confidence indicators or source attribution on any output
Cannot chain tasks or save a workflow for reuse
Generic grey interface with no visual hierarchy
Users described it as: "feels like a form, not a product"
Average contract review cycle: 4+ hours end-to-end
After
12 AI agents under one unified roof with consistent interaction patterns
Persistent workspace with project history, member collaboration, and run tracking
Confidence scores, source citations, and override controls on every result
Agentic workflow builder with 8 pre-built templates and unlimited custom chains
Dark sidebar + warm white content surface — designed with a clear point of view
Users described it as: "finally feels like it was built for us"
Average contract review cycle: under 25 minutes
07 — Interaction Design
One pattern. Twelve agents.
The most important design decision was establishing a single interaction grammar that all 12 agents follow. Learn one, know all twelve.
Home dashboard — KPIs, recent activity feed, and project grid
08 — Challenges & Constraints
What made this hard.
Every project has constraints. The goal is never to remove them — it's to design around them without compromising the outcome.
Constraint
12 Agents, 1 Pattern
+Constraint
Workflow Builder Blank Canvas
+Constraint
AI Trust Communication
+09 — Usability Testing
Testing what we built.
Two rounds of moderated usability testing with 5 participants each — legal analysts and compliance officers from the target user group.
Round 1 — Week 12
60%
Task completion rate on core workflows
Workflow builder blank canvas caused hesitation in all 5 participants
Confidence scores misunderstood — "92%" read as "8% wrong"
Step indicator in agent flow not noticed by 3 of 5 users
Export options not discovered — hidden behind icon, not labeled
Round 2 — Week 13
94%
Task completion rate after iterations
Template-first entry resolved all workflow builder hesitation
Confidence label change resolved misunderstanding completely
Larger step indicator with labels visible to all 5 users
Export moved to labeled button — discovered by 5 of 5 users
"The AI suggestions were surprisingly useful. It suggested adding PII Detection before my export step — I hadn't thought to do that but it made perfect sense."
— Usability Testing Participant #3, Round 2
10 — Impact & Results
What changed.
Outcomes measured from usability testing, stakeholder review, and engineering handoff assessment.
11 — Developer Handoff
Built for engineers too.
The design system was built with engineering handoff as a first-class output — not an afterthought.
Design Tokens
All colors, typography, spacing, shadow, and motion values exported as named CSS variables.
40+ Components
Every component built with Auto Layout, all states (default, hover, focused, disabled, loading).
Spacing System
Strict 4px base unit throughout. All padding, margin, and gap values are multiples of 4.
Motion Specs
All transitions documented: duration, easing curve, and trigger condition.
13 Page Specs
Every screen annotated with component names, interaction notes, responsive behavior, and edge case states.
AI Architecture
Agent system architecture documented with input/output specs, confidence scoring logic, and processing state triggers.
12 — Key Learnings
What this project taught me.
The insights that will change how I design every AI product going forward.
Final Thoughts
AIDEX reinforced my belief that the best enterprise product design is not about adding AI features — it is about designing the human-AI collaboration layer so precisely that users feel more capable, not replaced. The goal is never to impress. The goal is to make users feel like they are doing their best work.