ERSR Compliance Framework
Integrating AI search for an end-to-end hiring experience
My role
Conceptualization & Ideation
UX Design
Dev Handoff
Company
TalentMesh
Team
Product Manager/Founder
Product Designer
Engineering Lead
Year
2025
Background and problem
Imagine being a recruiter.
You log into TalentMesh to create a job spec and manage candidates. But when it’s time to actively search for talent, you leave the platform, open LinkedIn, and then copy profiles back into TalentMesh. Constant context switching slows everything down.
TalentMesh was originally built for passive recruitment — posting jobs and tracking applicants. To stay competitive, the company needed to expand into active recruitment with AI Search, scanning millions of profiles to surface the best matches.
The challenge? The product’s information architecture wasn’t designed for this. AI Search had to be integrated into the existing recruiter workflow in a way that felt seamless — and scale to meet enterprise hiring needs.
IMPACT
Integrating AI Search at the heart of recruitment.
Recruiters can now manage the entire hiring journey in one place — from sourcing to shortlisting to tracking candidates. This reduced context switching, made workflows faster, and improved adoption with a more cohesive experience.
For the business, the redesign positioned TalentMesh as a full recruitment solution, created a scalable information architecture (IA) for future features, and opened opportunities for enterprise growth and tiered pricing.
PROCESS
Designing for integration and scalability.
TalentMesh wanted to integrate AI Search into its existing platform so recruiters could manage both passive and active hiring in one place. But the current IA and tab-based navigation weren’t built to support such a central workflow.
PROCESS
Designing for integration and scalability.
TalentMesh wanted to integrate AI Search into its existing platform so recruiters could manage both passive and active hiring in one place. But the current IA and tab-based navigation weren’t built to support such a central workflow.
To solve this, I:
Mapped the recruiter journey to understand where AI Search should naturally fit.
Redesigned the IA, moving from flat tabs → scalable navigation that could grow with future features.
Create the candidate table, making it the central space where recruiters review and act on candidates.
Added supporting features, including a user overview drawer and a refined filter modal, to make AI Search feel seamlessly integrated into the product.
Unclear entry point for managing recruitment
Unclear entry point for managing recruitment
Metadata lacks actionable insight
Metadata lacks actionable insight
AI Search requires a scalable structure and dedicated UI
AI Search requires a scalable structure and dedicated UI
solution
Designing a scalable interface for AI Search
To truly integrate AI Search, I designed it as a central hub within the recruiter workflow, not an add-on. The new IA and navigation gave it visibility and room to grow, while keeping the recruiter’s experience simple and cohesive.
Key solution highlights:
Dedicated AI Search interface → a clear entry point where sourcing begins.
Scalable navigation → side navigation replaced tabs, allowing the product to expand without clutter.
Enhanced candidate table → candidates move clearly through stages, supported by meaningful metadata.
Contextual tools → filters, criteria modals, and a candidates overview drawer helped recruiters act quickly and confidently.
Clear entry point for managing each recruitment
Clear entry point for managing each recruitment
Metadata and status provide meaningful insights at a glance
Metadata and status provide meaningful insights at a glance
Passive recruitment positioned as a secondary workflow
Passive recruitment positioned as a secondary workflow
Recruiters get a dedicated interface for AI-powered candidate search
Recruiters get a dedicated interface for AI-powered candidate search
Other focus area
A side drawer gives recruiters a quick candidate overview without leaving the workflow.
Other focus area
Advanced filters and criteria make candidate search faster and more precise.
Learnings
Building for today while preparing for tomorrow
Building for today while preparing for tomorrow.
The challenge was less about designing AI Search and more about integrating it into an existing workflow without disrupting recruiters.
In startups, founders often act as the main users, making it essential to document flows and decisions for future scalability.
Scalable IA is strategic: it supports usability today while enabling future growth, role-based access, and pricing models.
My role
Conceptualization & Ideation
UX Design
Dev Handoff
COMPANY
TalentMesh
Team
Product Manager/Founder
Product Designer
Engineering Lead
Year
2025
AI Search
Integrating AI search for an end-to-end hiring experience
PROCESS
Designing for integration and scalability.
TalentMesh wanted to integrate AI Search into its existing platform so recruiters could manage both passive and active hiring in one place. But the current IA and tab-based navigation weren’t built to support such a central workflow.
PROCESS
Designing for integration and scalability.
TalentMesh wanted to integrate AI Search into its existing platform so recruiters could manage both passive and active hiring in one place. But the current IA and tab-based navigation weren’t built to support such a central workflow.
IMPACT
Integrating AI Search at the heart of recruitment.
Recruiters can now manage the entire hiring journey in one place — from sourcing to shortlisting to tracking candidates. This reduced context switching, made workflows faster, and improved adoption with a more cohesive experience.
For the business, the redesign positioned TalentMesh as a full recruitment solution, created a scalable information architecture (IA) for future features, and opened opportunities for enterprise growth and tiered pricing.
solution
Designing a scalable interface for AI Search
To truly integrate AI Search, I designed it as a central hub within the recruiter workflow, not an add-on. The new IA and navigation gave it visibility and room to grow, while keeping the recruiter’s experience simple and cohesive.
Key solution highlights:
Dedicated AI Search interface → a clear entry point where sourcing begins.
Scalable navigation → side navigation replaced tabs, allowing the product to expand without clutter.
Enhanced candidate table → candidates move clearly through stages, supported by meaningful metadata.
Contextual tools → filters, criteria modals, and a candidates overview drawer helped recruiters act quickly and confidently.
Background and problem
Imagine being a recruiter.
You log into TalentMesh to create a job spec and manage candidates. But when it’s time to actively search for talent, you leave the platform, open LinkedIn, and then copy profiles back into TalentMesh. Constant context switching slows everything down.
TalentMesh was originally built for passive recruitment — posting jobs and tracking applicants. To stay competitive, the company needed to expand into active recruitment with AI Search, scanning millions of profiles to surface the best matches.
The challenge? The product’s information architecture wasn’t designed for this. AI Search had to be integrated into the existing recruiter workflow in a way that felt seamless — and scale to meet enterprise hiring needs.
AI Search requires a scalable structure and dedicated UI
Clear entry point for managing each recruitment
Metadata and status provide meaningful insights at a glance
Learnings
Building for today while preparing for tomorrow.
The challenge was less about designing AI Search and more about integrating it into an existing workflow without disrupting recruiters.
In startups, founders often act as the main users, making it essential to document flows and decisions for future scalability.
Scalable IA is strategic: it supports usability today while enabling future growth, role-based access, and pricing models.
To solve this, I:
Mapped the recruiter journey to understand where AI Search should naturally fit.
Redesigned the IA, moving from flat tabs → scalable navigation that could grow with future features.
Create the candidate table, making it the central space where recruiters review and act on candidates.
Added supporting features, including a user overview drawer and a refined filter modal, to make AI Search feel seamlessly integrated into the product.
Passive recruitment positioned as a secondary workflow
Recruiters get a dedicated interface for AI-powered candidate search
Other focus area
A side drawer gives recruiters a quick candidate overview without leaving the workflow.
Other focus area
Advanced filters and criteria make candidate search faster and more precise.
Unclear entry point for managing recruitment
Metadata lacks actionable insight