Maple Labs: AEO Monitoring SaaS
Delivering a high-performance strategic dashboard and Atomic Design System in a 4-week MVP sprint.
Project Context
As LLMs (Large Language Models) transform into the primary search entry point, Answer Engine Optimization (AEO) has emerged as a critical business frontier. Maple Labs tasked me with designing a dashboard that monitors how brands are perceived and recommended by AI.
The challenge was to move from concept to a production-ready MVP in 4 weeks, requiring a robust UI framework that could handle complex data visualizations and multi-level competitive analysis without overwhelming the user.
Key Information
Role
Freelance UX/UI Designer
Deliverables
Dashboard UI, Design System, Prototypes
The Challenge
Visualizing the invisible: Monitoring AEO
How do we transform complex data from conversational agents into actionable business indicators?
Reducing « Time to Market » without sacrificing UX clarity for early-adopter users.
AEO generates massive textual data. We needed to extract sentiment scores and « Share of Voice ».
Functional Modules
Reducing « Time to Market » without sacrificing UX clarity for early-adopter users.
AI-driven identification of specific content and technical optimizations required to improve brand visibility in AI answers.
Identification of web sources that the AI uses to justify its answers, allowing for targeted source-level optimization.
Design System: Maple Atomic
A high-contrast dark environment designed for data intensity. Primary surfaces use deep blacks to make vibrant status indicators and charts pop.
Canva
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Background
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Primary-accent
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Semantic-warning
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Semantic-critical
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Atomic elements designed for quick implementation and visual consistency across the SaaS platform.
High-Fidelity MVP Preview
