AI agents as real users

Challenges of traditional research methods

  1. Current testing methods are time-consuming, expensive and often left incomplete

  2. Difficult to test across different user types and scenarios

  3. Major companies (like Sonos and CrowdStrike) have faced expensive failures from bad software that impacted customers

Role & approach

  • Synthesised months of founder research: took all their scattered customer conversations, notes, and insights and organised them into actionable knowledge

  • Drafted the initial PRD: created the first coherent product requirements document that aligned everyone on scope and functionality

  • Used design partner insights: incorporated feedback and requirements from potential customers they were about to sign

  • Designed and tested a demo experience: put together simple screens for the demo experience & tested it to validate concept

Design & Implementation

  • Designed information architecture: structured how information and features would be organised

  • Created UX flows: detailed the step-by-step user interactions for core features like agent setup and test execution

  • Managed rapid iteration: adapted designs when built features didn't work as expected, ultimately returning to your original recommendations

  • Designed the Figma plugin: a separate lead generation tool that showcased the AI feedback concept

Measurable impact

  • 5x increase in monthly active users in the first month post-launch

  • 5-month timeline from start (December) to successful launch (May)

  • Rapid iteration and focused execution despite working part-time

  • User growth trajectory that are supporting growth conversations

  • Created alignment across the team on core user journey and value proposition

Studio Dashboard
Studio Dashboard
Studio Dashboard
Studio Dashboard
Studio Dashboard
Studio Dashboard

Email us

hello@swirlypeak.com

Email us

hello@swirlypeak.com

Email us

hello@swirlypeak.com