AI-Powered Recruiting Copilot
PolarisJobs
Developed an automated interview process for staffing agencies.
PolaisJobs is an AI-Powered Recruiting Copilot that combines AI support, recruiter tools and candidate workflows to simplify hiring for staffing agencies. It aims to leverage AI/ML to support, but not replace, human decision-making in candidate evaluation and placement.
MY IMPACT
Led end-to-end design of third product phase, aligning user needs with product strategy through research, IA, and user flow optimization.
Designed the AI-driven interview flow and Copilot scheduling system, improving recruiter efficiency and reducing time-to-fill by 50%.
Conducted user research to refine workflows, improved handoff efficiency by 30%, and contributed to securing $200K in pre-seed funding from Harvard Innovation Labs and Spark Grants.
HIGHER PLACEMENT RATES
The Challenge
Automated & Copilot Interview Agent
The focus was on building an Interview Agent module that bridges gaps between recruiters and candidates by automating core interview workflows. Key user problems included:
Scheduling friction
Interview completion inefficiency
Miscommunication around timing, preparation, and feedback
From a business standpoint, the goal was to streamline interview coordination, reduce manual overhead, and increase the throughput of successfully completed interviews — thereby improving placement velocity and recruiter productivity.
Phase 3 Goals
Enable recruiters to conduct automated candidate screenings using an AI-powered agent.
Provide analytical insights into candidate responses.
Improve Efficiency
Significantly reduce the time recruiters spend on initial candidate screening.
Enhance Trust
Build trust with design partners by addressing their immediate screening needs.
Scalability
Design a solution that can evolve to offer more in-depth interview capabilities in the future.
Discovery
Secondary & Primary Research
Building on insights from Phases 2 and 3, I conducted a competitive analysis and developed interview questions tailored to staffing agencies’ expectations for the Interview Agent feature.
Key Insights from Interview
Collaboration
External collaboration with hiring managers collaboration is the most important
Candidate Re-answer Feature
All participants support candidate re-record option
Recruiters want visibility into number of attempts used
Review Preferences
Transcript and AI summarized notes are deemed the most valuable response formats
Review Efficiency
Recruiters desire tools for automatic flagging of specific keywords/ skills
Structuring User Flow
Drawing from competitive analysis and user research with staffing agencies, I developed user flows for both recruiters and candidates, integrating their feedback.
Recruiter User Flow
Recruiter Priorities
Access real-time interview insights and performance summaries
Review and compare candidate readiness through AI-generated reports
Easily create and manage new interview sessions
Navigate seamlessly between monitoring, reviewing, and planning actions
Candidate User Flow
Candidate Priorities
Provides clear instructions and relevant information to guide candidates through each interview step
Allows technical setup for preparation and practice
Offers flexibility and maintain control during the session
Enables candidates to confidently showcase their skills and potential through a supportive, user-centered flow
Iterations based on Feedback
Through initial testings, I've made iterative improvements on the low-fi structure to translate insights into high-fi prototypes.
Outcomes
Through initial testings, I've made iterative improvements on the low-fi structure to translate insights into high-fi prototypes.
Reflection
What I've Learned
Learned to balance features for AI implementation and feasibility with recruiter needs for building Interview Agent Module
Gained expertise in product strategy and delivering development-ready designs
Gained confidence in articulating ideas and leading responsibilities with ownership
Developed a unified UI style guide, strengthening brand identity and accelerating development efficiency
Areas of Improvement
For future work, I would aim to formally document all technical constraints and design trade-offs made during the process to help the team understand and align on why certain design decisions were made
Conduct more testings on edge cases specifically to identify unusual or failure scenarios
Build a more continuous feedback loop for gathering micro-feedback from live users (even internal users if pre-launch) directly within the prototype or application