Atticus: Design to Drive Retention and Monetization
Redesigned Atticus to launch monetization and achieve a 2.5% free-to-paid conversion.
Role: Product Designer, Co-founder
Timeline: Sep 2023
Platform: Web
Snapshot
Challenge
Initial user retention was near zero; users didn't return after first use.
Goal
Enhance user experience to improve retention and enable monetization.
Outcome
Achieved a 2.5% free-to-paid conversion rate and launched a $20 pricing package.
Context & Problem
Atticus is an LLM‑powered contract assistant that helps non‑lawyers identify, understand, and negotiate critical clauses. After an initial launch on Hacker News, the product saw hundreds of users but only one returning user. The core issue was that users couldn't easily access the primary value proposition: determining if a contract was safe to sign. Key features were buried or unclear, leading to poor retention.
Scope & Responsibilities
Team: 2 Engineers
Responsibilities:
  • Conducted user research through interviews.
  • Redesigned the user interface and experience.
  • Defined go-to-market strategy and pricing.
Research & Insights
I interviewed 10 users for 30 minutes each to understand their needs and pain points. These are the top problems I identified:
1
Buried Value
Users want a fast answer to: "Is this contract safe to sign?" This was hidden below the fold and hard to find.
2
Undiscoverable Chat
Users couldn't find or effectively use the Q&A functionality that enables deeper contract analysis.
3
Unclear Priority
Users didn't know what issues were urgent concerns versus nice-to-have changes to focus negotiations effectively.
Solution
Using Figma, I prototyped a new UI in 2 days. Before implementation, I ran lightweight user testing sessions on the prototypes to validate whether users immediately recognized key outputs and understood what to do next.
Key Changes
Split-screen layout
Displayed AI analysis alongside the chat interface to encourage deeper engagement without context-switching.
Prioritized Issue List
Introduced explicit labels to highlight critical risks and missing elements.
Smart Chat Suggestions
Provided users with starting points for questions, making the AI more approachable.
Results
2.5%
Free-to-paid conversion
User testing gave us confidence to begin charging. Users had 1 free analysis and could purchase a 4-analysis pack for $20.
2
Industry Features
Picked up by tech newsletters Superhuman and Ben's Bites.
Reflections & Learnings
  • Design for Knowledge Gaps: Non-lawyers don't know what to look for in contracts. The design must provide clarity, prioritization, and surfacing what users didn't know to ask—matching how lawyers actually review documents.
  • Better Input Enhances AI Output: Instead of sending full contracts to the model, we determined contract type and party role first, then guided what to extract and analyze. This context and step-by-step approach created high quality AI output.
  • Focus on Goals, Not Trends: Prioritized delivering clarity and actionable insights over building generic chatbot features.
Other Design Case Studies
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