Moha Intel
AI research workspace · Multi-source synthesis
An AI research workspace for investment analysts working across documents, conversations, and live data.
Role
Lead product designer owning product definition, information architecture, and interaction patterns through iterative testing cycles.
Team
Lead designer, researcher, frontend developer, backend developer
The challenge
Existing AI chat tools treated research like a conversation, but research is continuous . Sessions reset every time, notes disappeared, and sources were completely untraceable, so analysts had no way to build on their own work.
The approach
Restructured the product around four persistent research surfaces with embedded trust signals, source attribution, save states, and version history, validated through iterative testing.
The problem
Research teams work in fragments. Tools don't share memory. Every tool switch breaks focus. AI chat made this worse. Sessions reset, notes disappeared, sources were untraceable. The fundamental metaphor was wrong: research is not conversation. Research is continuous.
Discovery through testing failure
The first version failed immediately. Users couldn't complete basic tasks. Terminology obscured intent. Nothing felt persistent. Core insight: researchers needed memory. They needed to build on their own work, not start fresh every session.
Researchersneededmemory.
From chat to research workspace
Restructured the entire product around four persistent surfaces: Quick Notes: Fast capture, always saved. Workstation: Primary surface where everything stays visible. Channels: Organized threads by topic. Summaries: Condensed insights with traceable sources.
Validation
Previously impossible tasks became intuitive. Users trusted the system. They saved freely, built on previous research, and cited sources confidently.
Validation results
Designing for trust
Trust required visible proof at every interaction: Source attribution: Clickable sources so users could verify AI output. Save state visibility: Real-time indicators confirming persistence. Version history: Users see how their thinking developed over time. Human/AI separation: Clear visual distinction between user input and AI generation.
Trust architecture
Key risks of investing in Kumamoto?
Population decline accelerating、TSMC inflates land values
What are the key risks of investing in Kumamoto prefecture?
Three factors: population decline is accelerating faster than national average, TSMC facility construction inflates short-term land values, but infrastructure spending signals long-term regional commitment
- Challenges1/3
The primary challenges were conceptual, not technical. Terminology became critical. Wrong words derailed entire task flows. Multi-topic synthesis required new interaction patterns that had no existing precedent.
- Insights2/3
Researchers struggled with trust, not input. The solution wasn't better AI. It was better feedback. AI interface design is fundamentally different from traditional product design: the interface must make the system's reasoning visible.
- What’s next3/3
Evolving into a collaborative surface: shared channels, insight extraction, live co-editing, and smarter memory.
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