AI-Driven Talent Acquisition Platform
Designing an AI-Driven Talent Acquisition Platform Connecting Early-Career Talent with Companies.
B2B SaaS
AI Hiring
Overview
XSTRYV is an AI‑powered hiring platform built to bridge the gap by connecting next‑generation talent with companies more meaningfully and efficiently. By going beyond keyword matching and résumé screening, XSTRYV helps young professionals discover opportunities that fit their skills and ambitions, while enabling employers to find, evaluate, and hire the right early‑career talent faster and with greater confidence.
Tools
Figma
Figma Make
Google AI Studio
Cursor
Timeline
Oct 2025
Company
XSTRYV
Work
Individual
My Role
Led the end-to-end design of the AI-powered recruiting platform and marketing website, built a scalable design system, and leveraged AI tools for rapid prototyping and iteration.
Outcome & Impact
The XSTRYV platform is fully shipped, with a live website featuring a dedicated Unique Opportunities page.
The Problem
Today’s early‑career talent, especially students and recent graduates, struggles to break into the job market because traditional hiring systems prioritize resumes and keywords over real potential and fit. At the same time, startups and growing teams often waste time sifting through hundreds of unsuitable applications and miss out on motivated candidates ready to contribute from day one.
The Solution
XSTRYV uses AI to automatically match talent with companies, providing explainable insights on why candidates are a strong fit while enabling recruiters to discover, evaluate, and hire the right talent quickly.
The solution offers two connected experiences:
Challenges
Some of the key challenges are rooted in the complexity of the hiring ecosystem itself.
Design Process
I started by defining the visual and brand identity for XSTRYV to establish a clear product foundation that reflects a modern, AI-powered recruiting platform.
After aligning the website design with the brand direction, I built a scalable design system using shadcn/ui as the base, customizing and extending components to match the product’s needs. I then designed the platform in Figma, focusing on intuitive workflows for both talent and companies.
To rapidly prototype interactions, I connected the designs with Figma Make and experimented with functionality using Google AI Studio. Finally, I integrated the prototype into Cursor to evaluate how the interface translated into a working environment and test the overall product flow.
Identifying Design Principles
I started by defining design principles based on our users' pain points and what they valued in their ideal application.
Radical Transparency (Explainable AI)
AI should never be a "black box." Every recommendation or score the system provides must be backed by visible evidence.
Contextual Fluidity
The interface must adapt to the user's intent without forcing them to navigate away from their current task.
Efficiency via Automation (Low Cognitive Load)
Design for "glanceability." Recruiters are overwhelmed, so the UI must surface the most critical data points (Match %, Availability, Core Skills) first, hiding secondary details behind progressive disclosure.
Human-in-the-Loop Interaction
The AI is a "co-pilot," not a replacement. The design must emphasize human agency by providing clear "Action" triggers after every AI insight.
Iterating Layout IA
I explored multiple layout directions in Figma to structure the platform for clarity and efficiency. The focus was on creating clean, modular layouts that support quick talent discovery and simple hiring workflows while keeping the interface intuitive for both candidates and companies.
Design System
To ensure scalability and consistency across the platform, I created a structured design system for XSTRYV. Using shadcn/ui as the foundation, I customized the visual styles, tokens, and UI components to align with the product’s brand identity.
As the product evolved, I modified existing components and introduced new ones to support AI workflows while maintaining a cohesive and efficient design structure.
Few Design Screens
Employee/Recruiter Overview
I consolidated real-time pipeline metrics with a prioritized "My Tasks" to-do list featuring urgent status tags. This layout solves the problem of context switching by providing a 360-degree view of the hiring funnel, with the rationale that linking quantitative data (Applicants/Hired) to qualitative actions (Task List) allows recruiters to instantly identify and unblock bottlenecks in the recruitment process.
Talent Discovery with AI
The AI isn't a separate tool; it lives within the workflow. The chat bubble UI element suggests a partnership. Instead of complex Boolean search strings (the old way), users provide "intent." By placing the "Ask me to find talent..." box at the bottom left, the design utilizes the F-Pattern. Recruiters scan candidates on the right, but always have the "steering wheel" (the prompt box) available on the left to refine the search.
Talent Details & AI Match Verification
A major hurdle in AI is the "Black Box" effect. By breaking down exactly why a candidate is a match (e.g., "React: 6+ years"), the design provides explainability. This aligns with user research suggesting that recruiters only trust AI when it shows the underlying data points.
Roles/ Jobs Management
I implemented a "mini-funnel" visualization within each job card. This change solves the problem of information fragmentation by providing immediate visibility into the conversion stages (Reviewed > Interviewed > Hired), with the rationale that quantifying the funnel progress on the grid view allows recruiters to prioritize which roles need urgent sourcing versus those nearing a successful hire.
Talent Dashboard
I structured the layout to highlight progress as the primary value drivers while introducing a gamified "Weekly Activity" tracker. The "Personalized Recommendations" address user drop-off by providing clear next steps. Through recent activity and upcoming tasks, candidates stay engaged with their career growth.
Job Feed & Discovery
I structured the layout to highlight progress as the primary value drivers while introducing a gamified "Weekly Activity" tracker. The "Personalized Recommendations" address user drop-off by providing clear next steps. Through recent activity and upcoming tasks, candidates stay engaged with their career growth.
Job Detail & AI Match Verification
I integrated an AI-driven "Are you a match?" sidebar that provides instant, personalized validation of the candidate’s background against the role requirements. This solution applies GenAI UX research on "Explainability," using a distinct UI module to clarify why the system recommends the role.
Learnings
Studying GenAI UX patterns helped me integrate AI-driven features seamlessly, creating intuitive workflows and meaningful interactions.
Integrating AI features highlighted the need for transparency and trust in AI-driven experiences, making GenAI patterns crucial.
Aligning brand identity with product design demonstrated how cohesive visuals and messaging enhance credibility and user engagement.
My Reflections
Working on XSTRYV reinforced the importance of designing for multiple user groups with distinct needs while maintaining a cohesive experience.
Building a flexible design system early accelerated iteration and ensured consistency across product and marketing channels.
Leveraging AI tools for prototyping allowed rapid experimentation and validation, while aligning brand identity with the product experience made the platform feel polished, trustworthy, and engaging for both talent and companies.