Business Problem
As teams grow, they lose UX knowledge, repeating studies and relying on assumptions. Traditional research is too slow and expensive to scale. This leads to low adoption features and wasted resources. NUVUN automates and structures research to make it accessible within real team workflows.
Failed hypothesis #1
AI could fully replace research expertise in small teams.
Failed hypothesis #2
Only trained researchers could generate meaningful insights.
Failed hypothesis #3
Advanced proactive AI should be built first for maximum value.
The “No New Tool” Decision
We decided not to build a standalone platform and instead integrate NUVUN into tools teams were already using, like Slack, Notion and Google Meet.
Why this was controversial
Building a full-featured app would offer more control and visibility. But we prioritized zero learning curve and minimal disruption, even if it meant sacrificing some UI flexibility.
The Transparency-by-Default Rule
Instead of keeping AI reasoning hidden, NUVUN always shows the source data and confidence level for every insight.
Why this was controversial
Some feared it would overwhelm users or expose AI limitations. We believed transparency was essential to build trust, especially when insights could influence high-stakes product decisions.
V1: Lean Research Automation
• Delivers quick wins through a PRD Document, no learning curve or extra tools.
• Designed for full-cycle research across multiple departments at launch.
• Envisioned a separate platform with its own interface.
V2: Memory-Based Knowledge Assistant
• Starts with lightweight automation and conditional logic to deliver quick results.
• Acts as a contextual memory layer that helps teams scale their strategic thinking over time.
• Works invisibly within tools teams already use, extending their workflows without disruption, reducing cognitive load.
vs. Hiring UX Researcher
More consistent, no sick days, scales infinitely, preserves knowledge forever
vs. Generic AI (ChatGPT, Claude)
Domain expertise, structured memory, integrated workflow, learns from your specific context
vs. Documentation Systems (Notion)
Active intelligence vs. passive storage, proactively suggests relevant insights.
Reduce research cycle time through more intelligent flows
Multiply by 3 user data touch points per sprint.
Increase team participation in research in non-UX roles.