Redesigning QLU Recruit’s search from a dense, filter‑heavy flow into an AI‑powered conversational experience that delivers faster, more accurate results.

QLU Recruit is a specialized search platform designed for executive recruiting firms. The initial version was built around the workflow of Spencer Stuart, one of our power users, with deep customization for their search process. While powerful, the tool required extensive training and manual onboarding, limiting its scalability and slowing adoption across new clients.

As Head of Product Design, I led the strategic redesign of QLU's search experience to address growing adoption challenges. I focused on three key areas: aligning stakeholders, establishing clear success metrics, and guiding the design team through execution. Throughout the process, I facilitated discovery sessions, provided feedback on design iterations, and ensured our solution tackled both user pain points and business objectives. The outcome was a streamlined, AI-powered search experience that significantly reduced friction, built user trust, and enabled scalable onboarding across our client base.

QLU Recruit was initially built for Spencer Stuart, a power client, based on their executive search workflow. The original UX suffered from complexity: over 15 filters crowded the left side, users faced three different starting options in the center (Prompt, Job Description, or Filters), and completing a search required navigating a multi-step flow with previews, popups, and manual adjustments. These complexities created significant friction:

Manual onboarding required hours of training

Customer Success and Sales teams diverted valuable resources away from acquiring new customers

Users struggled to achieve optimal results

Inconsistent results undermined user trust

Product adoption remained low despite powerful capabilities

As we scaled our client base, these inefficiencies grew more frequent and costly, clearly signaling the need for a solution. One‑liner problem statement:

I led the team through a structured, strategic process:

Discovery & Alignment: Facilitated sessions with CS, Sales, and Engineering to identify key user pain points and business constraints.

Defining Success: Established measurable targets with leadership for speed, adoption, and training reduction.

Direction Setting: Guided the design team to prioritize conversational AI as the primary interaction model, while maintaining manual filters for expert users.

Iteration & Review: Evaluated design milestones to ensure concepts addressed both UX friction and business KPIs.

Stakeholder Advocacy: Presented solutions supported by beta testing data to build trust and maintain buy-in throughout development.

By making the first experience simple, we turned a tool that felt intimidating into one that feels easy to use. This built trust, increased adoption, and gave internal teams more time to focus on growth instead of training, all through teamwork and clear design leadership.

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