Reach beyond applicants
Discover potential candidates who may never see your posting.
From discovering hidden talent
to hiring with confidence.
Cross-source analysis uncovers hidden candidates and the evidence behind their fit.
Find ML engineers with 3+ years of Python experience,
PyTorch model development and vLLM service deployment experience.
Sourcer
Searching public work for relevant experience
Candidates
A
Candidate A
ML EngineerB
Candidate B
Research EngineerEvidence
Candidate A’s public work
PyTorch development · vLLM deployment
Published PyTorch training code and vLLM deployment configurations. Years of Python experience require further confirmation.
PyTorch training codeGitHub · Pull request
vLLM deployment configurationGitHub · Deployment
Talent leaves signals beyond a résumé.
Applications alone can miss people who are not actively looking. Sourcer connects public contributions and experience to broaden your search.
Evidence beyond profiles
Look at expertise demonstrated through talks, papers and projects.
Less repetitive searching
Gather candidate information across multiple public sources.
Connect public activity into a candidate profile.
Examples of public sources considered in research. Job-related information is reviewed with its sources, within permitted access and usage limits.
DEVIEW
KIPRIS
Google Patents
Posts and discussions in technical communities
Conference talks, workshops and lab updates
Personal blogs and public portfolios
From discovery to the first conversation.
Refine your hiring brief, review individual candidate reports and assessment evidence, and prepare a personal message.
Sample dataThe people and analysis below are illustrative examples, not real candidate information.
Refine your requirements through conversation and compare suggested candidates side by side.
Review matching evidence against the current hiring criteria. Scores are illustrative; unconfirmed information requires further review.
Explore career contributions, GitHub projects and commits, papers and recent technical activity. Scroll through source-linked collaboration examples and points requiring further verification.
Review a personalized message draft based on the candidate’s background and your hiring brief.
Swipe horizontally to explore the screen.
Three specialist agents. One connected workflow.
Shadow Sourcer
Hidden talent discovery
- Cross-reference public sources
- Same-person inference across pseudonyms
- Profile evidence shown right inside the card
Fit Analyzer
Fit analysis
- Match score against required skills and experience
- Switch likelihood from recent activity signals
- Reasons-to-reject captured too
Cold Outreach Agent
Personalized outreach
- Per-candidate references inserted automatically
- Tone & language picker (Korean / English)
- Recruiter review and direct contact
Make the first message about their actual work.
Prepare message and follow-up drafts grounded in job-related talks, papers and projects. Recruiters verify the sources and content, adjust tone and language, and contact candidates directly.
Personalized draft
Your recent work on [project] and [technical topic] caught our attention. It connects with [challenge] our team is working on. We would welcome a conversation.
Review and edit, then contact directlyAI helps discover. People make the decision.
Review candidates and evidence, then refine each message in your team’s voice. The recruiter reviews and edits the draft before contacting the candidate directly.
Find your next teammate ↗Frequently asked questions
Where does Sourcer find candidates?
It cross-references public conferences, papers, open-source projects, patents and community activity, with sources and fit evidence.
Does it only provide a fit score?
Scores are accompanied by matching evidence for the current hiring criteria and points requiring further review. They are not hiring-success probabilities or absolute measures of ability; the recruiter makes the final decision.
Are messages sent automatically?
No automatic sending. Sourcer prepares a personalized draft; the recruiter reviews and edits it before contacting the candidate directly.
How do Sourcer and Probe differ?
Sourcer supports discovery and outreach preparation; Probe evaluates AI capability through practical tasks.
How can I get started?
Open the service with Try Sourcer, or contact us to discuss your hiring needs.