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Lumeir — Identifying Top Talent Using Predictive AI
Lumeir — Identifying Top Talent Using Predictive AI
Utilizing your top performers to help you hire better.
Role
Lead Product Designer
Collaboration
Product Designer (2)
Technical Coordinator (1)
Founder (2)
Duration
2 months
Role
Lead Product Designer
Collaboration
Product Designer (2)
Technical Coordinator (1)
Founder (2)
Duration
2 months
Role
Lead Product Designer
Collaboration
Product Designer (2)
Technical Coordinator (1)
Founder (2)
Duration
2 months
Lumeir is a talent-matching AI startup focused on improving the efficiency of hiring while preserving human judgement.
Lumeir is a talent-matching AI startup focused on improving the efficiency of hiring while preserving human judgement.
Lumeir is a talent-matching AI startup focused on improving the efficiency of hiring while preserving human judgement.
They challenged our team to envision blue-sky, future-forward concepts that push beyond existing industry solutions to set them apart from other competitors.
They challenged our team to envision blue-sky, future-forward concepts that push beyond existing industry solutions to set them apart from other competitors.
User Research
Competitive Analysis
To ensure that our solution has no overlap with existing industry tools, I researched main competitors (Humanly, Juicebox, Ashby, Greenhouse) to understand the current market landscape.
User Research
Competitive Analysis
To ensure that our solution has no overlap with existing industry tools, I researched main competitors (Humanly, Juicebox, Ashby, Greenhouse) to understand the current market landscape.
User Research
Competitive Analysis
To ensure that our solution has no overlap with existing industry tools, I researched main competitors (Humanly, Juicebox, Ashby, Greenhouse) to understand the current market landscape.
User Research
Proto-Persona
Lumeir's target audience are recruiters & hiring managers at small-scale companies responsible for filling mid-to-senior level technical roles. They often operate prioritize speed and quality of hire.
User Research
Proto-Persona
Lumeir's target audience are recruiters & hiring managers at small-scale companies responsible for filling mid-to-senior level technical roles. They often operate prioritize speed and quality of hire.
User Research
Interviews & Card-Sort Workshop
I interviewed nine recruiters, then transcribed and thematically coded the data, using affinity mapping to surface key industry pain points. Using those pain-points, I facilitated a card-sorting workshop. This guided recruiters through prioritizing their pain points, uncovering what mattered most and why.
User Research
Interviews & Card-Sort Workshop
I interviewed nine recruiters, then transcribed and thematically coded the data, using affinity mapping to surface key industry pain points. Using those pain-points, I facilitated a card-sorting workshop. This guided recruiters through prioritizing their pain points, uncovering what mattered most and why.
Research Findings
Optimizing hiring process time is priority.
The main performance metric for recruiters is 'Time-to-fill': the time it takes from a job requisition being approved to the candidate accepting the job offer. Recruiter & Hiring Manager misalignment further slow this process down.
Resumes aren't enough when evaluating candidates.
Recruiters voiced that existing ATS systems and AI screening tools overemphasize buzzwords, often dismissing candidates who may have strong transferable skills.
Hesitancy towards AI hiring tools.
Most recruiters expressed wariness of AI’s limitation, particularly around accuracy, the impersonal nature, and the potential dangers of AI replacing human roles.
Research Findings
Optimizing hiring process time is priority.
The main performance metric for recruiters is 'Time-to-fill': the time it takes from a job requisition being approved to the candidate accepting the job offer. Recruiter & Hiring Manager misalignment further slow this process down.
Resumes aren't enough when evaluating candidates.
Recruiters voiced that existing ATS systems and AI screening tools overemphasize buzzwords, often dismissing candidates who may have strong transferable skills.
Hesitancy towards AI hiring tools.
Most recruiters expressed wariness of AI’s limitation, particularly around accuracy, the impersonal nature, and the potential dangers of AI replacing human roles.
Leveraging research findings and Lumeir’s business model, I refined our project focus to prioritize the front-end of the recruiting workflow.
Leveraging research findings and Lumeir’s business model, I refined our project focus to prioritize the front-end of the recruiting workflow.
Opportunity Area
How might we reduce time-to-fill by optimizing the early-stage hiring process, enhancing candidate evaluation beyond the resume, and design a solution that assists without replacing human judgement?
Opportunity Area
How might we reduce time-to-fill by optimizing the early-stage hiring process, enhancing candidate evaluation beyond the resume, and design a solution that assists without replacing human judgement?
Ideation
Future Thinking Design Workshop
To tackle Lumeir’s blue-sky challenge, I directed 2 future-thinking design workshops with recruiters, guiding them through exercises to articulate their pain points, priorities, and aspirational tools. This approach surfaced innovative ideas that are grounded in their real, day-to-day experiences.
Ideation
Future Thinking Design Workshop
To tackle Lumeir’s blue-sky challenge, I led 2 future-thinking design workshops with recruiters, guiding them through exercises to articulate their pain points, priorities, and aspirational tools. This approach surfaced innovative ideas that are grounded in their real, day-to-day experiences.
Concept Development
'Reverse Engineering' the perfect candidate
The system analyzes post-hire performance to understand what drives success, uncovers patterns in resumes and skills, recommends top candidates, and evolves as it gathers more data.
Solves: Recruiter & hiring manager mis-alignment, manual labor in resume reviewing.
Cultivating AI Trust
Optional candidate prioritization features and automated workflows keep humans in control. Resume analysis backed by statistics and predictive data link candidate traits to real-world success.
Solves: Retaining 'human touch' in decision-making, AI skepticism
Tooling Optimization
Seamless integration with major ATS platforms like Workday or Greenhouse are essential given the numerous tools recruiters currently use.
Solves: Time-to-fill hindered by platform-switching
Concept Development
'Reverse Engineering' the perfect candidate
The system analyzes post-hire performance to understand what drives success, uncovers patterns in resumes and skills, recommends top candidates, and evolves as it gathers more data.
Solves: Recruiter & hiring manager mis-alignment, manual labor in resume reviewing.
Cultivating AI Trust
Optional candidate prioritization features and automated workflows keep humans in control. Resume analysis backed by statistics and predictive data link candidate traits to real-world success.
Solves: Retaining 'human touch' in decision-making, AI skepticism
Tooling Optimization
Seamless integration with major ATS platforms like Workday or Greenhouse are essential given the numerous tools recruiters currently use.
Solves: Time-to-fill hindered by platform-switching
Concept Development
Product Flow
I structured the product flow to guide recruiters from ATS & performance data sync, to reviewing AI-ranked candidates and making final decisions. Candidates are scored based on predicted success, with recruiters having full control of the candidate approval. This ensures that recommendations are data-driven and move beyond buzzword matching.
Concept Development
Product Flow
I structured the product flow to guide recruiters from ATS & performance data sync, to reviewing AI-ranked candidates and making final decisions. Candidates are scored based on predicted success, with recruiters having full control of the candidate approval. This ensures that recommendations are data-driven and move beyond buzzword matching.
Concept Development
Core System (Front-End & Back-End)
The solution is supported by a layered system architecture: a front-end interface for recruiter workflows, a back-end system that parses resumes and powers predictive ranking, and a data and integration layer that insures security and centralization.
Concept Development
Core System (Front-End & Back-End)
The solution is supported by a layered system architecture: a front-end interface for recruiter workflows, a back-end system that parses resumes and powers predictive ranking, and a data and integration layer that insures security and centralization.
After presenting the concept to Lumeir’s founder, I pivoted to a rapid, prototype-driven approach to meet a tight timeline and deliver a portfolio-ready solution.
I leveraged vibecoding to quickly test assumptions, using intentional prompt generation to guide key design decisions and ground them in user and stakeholder feedback.
After presenting the concept to Lumeir’s founder, I pivoted to a rapid, prototype-driven approach to meet a tight timeline and deliver a portfolio-ready solution.
I leveraged vibecoding to quickly test assumptions, using intentional prompt generation to guide key design decisions and ground them in user and stakeholder feedback.
Prototyping
93 Iterative Prompts In FigmaMake
With Figma Make, I rapidly turned concepts into testable prototypes to explore and validate key interaction patterns.
Prototyping
93 Iterative Prompts In FigmaMake
With Figma Make, I rapidly turned concepts into testable prototypes to explore and validate key interaction patterns.
I grounded each iteration in core heuristics to ensure the system communicates value clearly, supports efficient decision-making, and maintains user trust.
I grounded each iteration in core heuristics to ensure the system communicates value clearly, supports efficient decision-making, and maintains user trust.
I incorporated feedback from Lumeir's founder and user tested with recruiters to refine the design, ensuring it aligned with business goals while addressing real-world hiring workflows. This input allowed me to improve clarity, usability, and overall effectiveness of the system.
I incorporated feedback from Lumeir's founder and user tested with recruiters to refine the design, ensuring it aligned with business goals while addressing real-world hiring workflows. This input allowed me to improve clarity, usability, and overall effectiveness of the system.
Refinement
Business Alignment & User Feedback
The final iteration considers branding alignment, guidance to assist learning curves, contextual references, and opportunity for scalability.
Refinement
Business Alignment & User Feedback
The final iteration considers branding alignment, guidance to assist learning curves, contextual references, and opportunity for scalability.
Refinement
Final Prototype
Preview the working prototype below.
Refinement
Final Prototype
Preview the working prototype below.
Impact
Future Considerations
If I had more time, I would design to consider the user flow for organizations that don't have any performance data. This could look like an initial reliance on industry-level patterns across similar organizations, and gradually configuring suggestions as company gathers & inputs evaluation data.
I would also further improve the performance data system to mitigate manager bias and unreasonably harsh evaluations. This could look like evaluation based on an aggregate data system across multiple employees and team members, seeking pattern recognition amongst individuals, encourage performance criteria that prioritize observable signals, and detecting anomalies such as consistent low ratings deriving from a single manager.
Impact
Metrics & Impact
The final prototype was tested with users, measuring the potential impact of applying this tool to their workflow. Overall results showed a minimized time spent reviewing candidates, providing value to the recruiters by allowing them to efficiently review high volumes of candidates.
Lumeir founders also expressed satisfaction with the speed of project completion and data presentation.
Impact
Metrics & Impact
The final prototype was tested with users, measuring the potential impact of applying this tool to their workflow. Overall results showed a minimized time spent reviewing candidates, providing value to the recruiters by allowing them to efficiently review high volumes of candidates.
Lumeir founders also expressed satisfaction with the speed of project completion and data presentation.
Impact
Future Considerations
If I had more time, I would design to consider the user flow for organizations that don't have any performance data. This could look like an initial reliance on industry-level patterns across similar organizations, and gradually configuring suggestions as company gathers & inputs evaluation data.
I would also further improve the performance data system to mitigate manager bias and unreasonably harsh evaluations. This could look like evaluation based on an aggregate data system across multiple employees and team members, seeking pattern recognition amongst individuals, encourage performance criteria that prioritize observable signals, and detecting anomalies such as consistent low ratings deriving from a single manager.