Semantic search and predictive AI that helps hiring teams find who fits — not just who matches keywords. I built the prototype solo, delivered it to the Lumeir founder, and led a team of 4 to scale it into the full product.
Lumeir — Recruit Smarter
Lead Designer · 6 months
Semantic search and predictive AI that helps hiring teams find who fits — not just who matches keywords. I built the prototype solo, delivered it to the Lumeir founder, and led a team of 4 to scale it into the full product.
Lead Designer · 6 months
Semantic search and predictive AI that helps hiring teams find who fits — not just who matches keywords. I built the prototype solo, delivered it to the Lumeir founder, and led a team of 4 to scale it into the full product.
Lumeir — Recruit Smarter
Semantic search and predictive AI that helps hiring teams find who fits - not just who matches keywords. I built the prototype solo, delivered it to the Lumeir founder, and led a team of 4 to scale it into the full product.
Process — 6 months
The team discovered together. I decided where to go next.
Research was a team effort. Strategic decisions about where to go next was mine.
Discovery & Research
2.5 months · Team
Define & Vibecode
2 weeks · Solo
Refine Concept
2 weeks · Team
Lead Team Build
3 months · I Led
My ownership
My ownership
I built it first. Then I led the team to scale it.
I built it first. Then I led the team to scale it.
Hiring managers send requisition files. Recruiters manually re-read them every time they screen a candidate. I built an uploader that pulls requirements from the file automatically, filters candidates against it, and surfaces an AI analysis - so the decision is ready before the recruiter even opens a profile.
The problem
Competitive positioning
The ATS was designed to filter keywords.
AI usage made the resume an unreliable signal.
Now, Lumeir wins where existing tools have no answer.
Resume signal reliability
Once resumes could be optimized in seconds, the signal recruiters relied on for years collapsed almost overnight.
Once resumes could be optimized in seconds, the signal recruiters relied on for years collapsed almost overnight.
The problem
The problem
Existing process: Not efficient. Not accurate. Not aligned.
The ATS was designed to filter keywords.
AI usage made the resume an unreliable signal.
"The process is slow, but judgement can't really be automated. It would be amazing if AI could do that for me."
— Recruiter
"Every resume looks the same now. AI wrote it to pass the filter, not to actually describe the person."
— Hiring manager
"Me and my hiring manager just don't agree on what good looks like. We waste so much time on that."
— Recruiter
"The process is slow, but judgement can't really be automated. It would be amazing if AI could do that for me."
— Recruiter
"Every resume looks the same now. AI wrote it to pass the filter, not to actually describe the person."
— Hiring manager
"Me and my hiring manager just don't agree on what good looks like. We waste so much time on that."
— Recruiter
5 interviews, 4 workshops.
5 interviews, 4 workshops.
How might we improve the efficiency and accuracy of candidate sourcing, especially as AI makes resume quality an unreliable signal?
Market Opportunity
Market Opportunity
A market of users begging for improvement.
A market of users begging for improvement.
Solution concept - my strategic direction
Solution concept - my strategic direction
So I reverse-engineered the perfect hire,
designed to never let you invest in the wrong person.
So I reverse-engineered the perfect hire,
designed to never let you invest in the wrong person.
Top hires are identified through integrated post-hire performance data, which determines what 'best fit' looks like for each role. The more you hire and review, the better the algorithm predicts your next hire.
Top hires are identified through integrated post-hire performance data, which determines what 'best fit' looks like for each role. The more you hire and review, the better the algorithm predicts your next hire.
Semantic search, sharpened by every hire.
Semantic search, sharpened by every hire.
Lumeir is learning from your hires' performance in Workday to sharpen future candidate matching.
Sync performance reviews, ratings, and retention data.
Semantic search understands context within a candidate's profile beyond keyword matching. It also learns from outcomes. Every successful hire teaches the algorithm what a good match looks like, improving search results over time.
Semantic search understands context within a candidate's profile beyond keyword matching. It also learns from outcomes. Every successful hire teaches the algorithm what a good match looks like, improving search results over time.
Key features - team build
Key features - team build
Five key features. Each grounded in what we heard.
Five key features. Each grounded in what we heard.
Type in natural language or click predictive phrases to build your search. No boolean strings, no missed talent.
"Many recruiters are keyword search recruiters - they lack deep knowledge in the field."
Competitive positioning
Now, Lumeir wins where existing tools have no answer.
Competitive positioning
| Tool | Semantic search | Evidence scores | Team alignment | AI transparency |
|---|---|---|---|---|
| Greenhouse | ||||
| Workday | ||||
| Lever | ||||
| Lumeir |
No existing ATS combines all five dimensions hiring teams need. This is Lumeir's advantage, and the design strategy was built around it.
No existing ATS combines all five dimensions hiring teams need. This is Lumeir's advantage, and the design strategy was built around it.
No existing ATS combines all five dimensions hiring teams need. This is Lumeir's advantage, and the design strategy was built around it.
"It just makes more sense to search in natural language. I'm honestly surprised LinkedIn hasn't already done this. Saves me from overthinking boolean string formats."
— Recruiter
Hiring managers send requisition files. Recruiters manually re-read them every time they screen a candidate. I built an uploader that pulls requirements automatically and surfaces an AI analysis.
Discovery & Research
2.5 months · Team
Define & Vibecode
2 weeks · Solo
Refine Concept
2 weeks · Team
Lead Team Build
3 months · I Led
Resume signal reliability
How might we improve the efficiency and accuracy of candidate sourcing, especially as AI makes resume quality an unreliable signal?
Lumeir learns from hire performance in Workday to sharpen matching.
Syncs reviews, ratings, and retention data.
Type in natural language or click predictive phrases to build your search. No boolean strings, no missed talent.
"Many recruiters are keyword search recruiters - they lack deep knowledge in the field."
Competitive positioning
"It just makes more sense to search in natural language. I'm honestly surprised LinkedIn hasn't already done this. Saves me from overthinking boolean string formats."
— Recruiter
Process — 6 months
We discovered together.
I decided where to go next.
Research was a team effort. Strategic decisions about where to go next was mine.
Amanda's prototype provided us a preview on the product direction, giving us the confidence to let the team move forward.
— Tyler Bartholomew, CEO & Cofounder
"The process is slow, but judgement can't really be automated. It would be amazing if AI could do that for me."
— Recruiter
"Every resume looks the same now. AI wrote it to pass the filter, not to actually describe the person."
— Hiring manager
"Me and my hiring manager just don't agree on what good looks like. We waste so much time on that."
— Recruiter
5 interviews, 4 workshops.
Hiring managers send requisition files. Recruiters manually re-read them every time they screen a candidate. I built an uploader that pulls requirements from the file automatically, filters candidates against it, and surfaces an AI analysis - so the decision is ready before the recruiter even opens a profile.
Once resumes could be optimized in seconds, the signal recruiters relied on for years collapsed almost overnight.
The problem
Existing process: Not efficient. Not accurate. Not aligned.
Process — 6 months
We discovered together.
I decided where to go next.
Research was a team effort. Strategic decisions about where to go next was mine.
Impact and outcomes
Impact and outcomes
Validated, approved, and greenlit by the founder.
Validated, approved, and greenlit by the founder.
Amanda's prototype provided us a preview on the product direction, giving us the confidence to let the team move forward.
— Tyler Bartholomew, CEO & Cofounder