Alphabase was an early-stage concept exploring how AI could structure and prioritize crypto information across fragmented platforms.

The product aggregated content from Twitter, Telegram, Substack, and on-chain data sources into a unified intelligence layer.

Rather than simply summarizing content, Alphabase focused on extracting signals — clustering narratives, ranking relevance, and highlighting emerging trends.

The core problem was not just information overload, but the absence of prioritization and signal confidence.

As Product Designer, I led product direction and UX design across key areas:

• Defined the signal extraction and prioritization framework
• Designed ranking, filtering, and clustering interactions
• Developed scoring systems (Twitter score, trust score, signal weight)
• Structured dashboards for monitoring narratives and emerging trends
• Designed scalable desktop-first architecture for high-density data environments

The vision was to build a signal intelligence layer for crypto — shifting research from endless scanning to structured, AI-assisted analysis.

Instead of consuming fragmented content, users would receive a ranked stream of insights combining:

• AI-generated summaries
• Source credibility scoring
• Cross-platform signal detection
• Personalized ranking based on behavioral patterns

The goal was to reduce cognitive load and enable faster, higher-confidence decision-making in volatile markets.