Product Design Case Study · 2024
Making legal language human again
82% of people sign contracts they don't fully understand. We designed LegalEasy — an AI-powered app that translates legal jargon into plain language, in seconds.
My role
Product Designer
Team
4 designers
Timeline
3 weeks · Bootcamp
Methods
Design Thinking · UX Research · Prototyping
Tools
Figma · Maze · Miro
01 — The Problem
Legal language excludes the people it's meant to protect
Every day, citizens sign contracts, receive fines, open bank accounts, and navigate bureaucracy using documents written in a language designed for lawyers — not people. The result? They sign without understanding, feel ashamed to ask, and turn to Google, which makes things worse.
We set out to understand the real depth of this problem before designing anything.
82%
of people have signed a contract without fully understanding it
100+
people surveyed across ages and education levels
2
core barriers: document length and complex terminology
#1
workaround was Google — which increased confusion, not clarity
"One of the biggest problems is when clients use Google to clarify doubts. Google creates more problems than solutions. They don't know how to search properly."
— Legal professional, interview participant
02 — Research
We listened before we designed
Our research combined a quantitative survey of over 100 participants with qualitative interviews of both everyday users and legal professionals. This dual perspective revealed not just what users struggled with, but why the problem persisted.
Method 01
Desk research & benchmarking
We mapped existing solutions and identified their shared weakness: they explain terms in isolation, without context or consequence for the user.
Method 02
Survey — 100+ participants
Mixed demographics across Spain. We focused on frequency of document encounters, comprehension difficulties, and help-seeking behaviour.
Method 03
User interviews
Two scripts: one for everyday users, one for legal professionals. This revealed the systemic nature of the problem from both sides.
Method 04
Affinity mapping
We clustered all findings into themes. Three categories emerged: plain language, tools, and accessible education.
Shame as a barrier
Users didn't ask for help not because they didn't need it — but because they felt embarrassed. Invisible in the survey data.
Google makes it worse
The most common workaround actively increased confusion. Users drew wrong conclusions. Our biggest opportunity.
Context beats translation
Users didn't just need definitions — they needed to understand what a term meant for them, in their situation.
Length is a UX problem
Document length alone caused abandonment. Users skipped critical sections not out of laziness, but because they were overwhelmed.
"Most people don't ask questions out of shame or laziness when reading documents."
— Key insight from user interviews
03 — The Solution
One core flow. No noise.
After an extensive ideation phase — gamification, mascots, video pills, Q&A chatbots — we made a deliberate product decision: focus on one core flow and do it exceptionally well.
Upload a document → AI detects complexity → Plain language output with context-aware explanations and risk alerts.
We used a MoSCoW framework to ruthlessly prioritise and avoid the scope creep that plagues most bootcamp projects.
Key design decisions
Decision 01
Highlights over modals
Inline term highlighting with expandable cards kept users in context, reducing cognitive load compared to modal-based definitions.
Decision 02
Risk alerts as first-class feature
Surfacing harmful clauses shifted the product from "dictionary" to "legal companion." The insight that elevated the entire concept.
Decision 03
Summary before terms
Leading with a plain-language summary gave users immediate value and reduced anxiety before engaging with detailed explanations.
Decision 04
AI humanisation
Testing showed users responded better when explanations used "you" and specific examples. We tuned prompts to always personalise the voice.
04 — Testing & Iteration
We tested early and learned fast
We ran usability tests with low-fidelity wireframes in Maze before investing in high-fidelity design. Two flows were tested: uploading a document and navigating the glossary.
Success
Upload task: 84% success rate
Users found the upload flow intuitive. Average completion time was 30 seconds — within our target range.
Problem identified
Glossary: 50%+ failure rate
Users couldn't reliably find saved documents via the glossary. The navigation needed a full structural rethink.
Insight
AI felt cold and impersonal
Early outputs read like encyclopaedia entries. Users wanted to feel spoken to — not lectured. We rewrote the AI prompt voice entirely.
Validation
High perceived usefulness
Despite UX friction, users rated the concept 4–5/5 for usefulness. The problem was real; execution needed refinement.
Based on testing, we restructured the navigation, separated the document library from the glossary, and rewrote AI output prompts to use direct, personal language with practical examples.
05 — What I Learned
Product thinking starts before the first sketch
Key takeaways
Focus is a product decision, not a design decision
The strongest move we made was cutting features. A product that does one thing exceptionally beats a feature list every time. The MoSCoW process was more valuable than any wireframe.
Emotional barriers are invisible in surveys
The shame of not understanding legal language only emerged through qualitative interviews. Quantitative data told us what — conversations told us why. Both are non-negotiable.
Test the concept, not the polish
Our most valuable feedback came from low-fidelity wireframes. Testing early saved weeks of rework and revealed a critical navigation flaw before we invested in visual design.
AI is a design material, not just a technology
How we prompted the AI shaped the entire user experience. Voice, tone, and practical examples were product design choices — not engineering ones.
What's next
With current AI capabilities, LegalEasy could be built as a real product today. The next iteration would include live AI analysis, Q&A per document, document type–specific guidance, and a freemium model targeting individuals and small businesses.
The legal tech market remains underserved for everyday citizens — not just corporations. That's the gap LegalEasy was designed to fill.