AI Search 🤖
Turning manual candidate search into an intelligent, built-in experience
My contribution
40% Ideation
40% UX/UI
20% Dev Handoff
Team
Product Manager/Founder
Product Designer (Me)
Engineering Lead
Timeline
2025
Impact 💥
Helped TalentMesh evolve from a recruitment manager into a complete end-to-end hiring platform by integrating AI Search directly inside the existing workflow. This shift positioned the product as a more scalable solution — and a key milestone in the company’s growth story.
Problem 🎯
Recruiters had to leave TalentMesh to actively search for candidates on LinkedIn or other tools, then return to manage applications. The constant back-and-forth made the process longer and fragmented — especially for senior or niche roles, where active sourcing is crucial.
Approach 🧩
My approach was hands-on and fast-paced — as a startup gaining momentum, we needed to deliver something within the next quarter that could fit into the existing workflow and IA. The challenge was to make AI Search feel natural, not forced — powerful enough to add value, yet simple enough to fit the product’s current maturity.
Solution 💡
1. Connecting the dots for a seamless workflow
When I joined, the plan was to separate the hiring and search workflows. I re-framed them into one unified recruitment flow, so recruiters could move smoothly between sourcing and managing candidates without switching contexts.
Below: Recruitment overview card before redesign, with 2 workflows
Before: Unclear entry point for managing recruitment and metadata lacked actionable insights
Below: Image of recruitment overview card after redesign
After: Clear entry point for managing recruitment, with metadata and status providing meaningful insights at a glance
2. Introducing a scalable left navigation
To prepare the product for future AI capabilities, I replaced the top-tab layout with a persistent left navigation, which scales more effectively as the product grows and helps users keep orientation within the workflow.
The new structure organizes tabs into:
AI Search – explore, filter, and shortlist candidates
Applications – manage and track progress
Below: Initial idea to add AI Search to the current passive recruitment
Before: Incorporate AI Search in the current IA wasn't scalable
Below: The new AI Search overview page
After: AI Search is now the main feature of the recruitment process
3. A faster and more powerful way to review candidates
I introduced a scalable candidate table that allows recruiters to sort, filter, and perform bulk actions directly within the same view. Each row surfaces key metadata — such as company, match score, and criteria ratings — making it easy to compare candidates at a glance. Alongside this, a drawer panel enables recruiters to preview and shortlist profiles quickly, reducing context switching and keeping the workflow smooth and focused.
Below: The new AI Search table
Below: Filter functionality help refine AI Search
Below: Drawer for quick overview of candidates
Below: release version of AI Search
Outcome 📈
Creating AI Search was also a strategic business move for TalentMesh — showing investors a path from recruitment management toward a full self-service hiring platform. The candidate table and bulk-action framework became a foundation for scalability, later reused across other areas of the product such as onboarding and user management. This redesign helped define that broader vision and set the tone for future AI-powered features.
Learning 🔍
Working hands-on under tight deadlines and in close collaboration with the founder was energising but also quite fast-paced. Early discussions brought different viewpoints, but as trust developed, our collaboration became more open and exploratory.
