ScriniAI agents that hire for every role
Job seekers
RecruitersThe problem
Boolean keyword parsers miss great candidates with non-standard backgrounds. Static matching fails to understand the context behind experience or how closely it fits the role's requirements.
Neural Match
AI-driven contextual resume matching. Skills and experience matched to the role's requirements, not just keywords and formatting.
- Indeed
- Your ATS
- Past applicants
186matched on skills, location and fit
The problem, and the fix.
The solution
Neural Match moves beyond keyword matching to understand the context behind a candidate's history. It matches skills and experience to the role's requirements and shows the evidence for each match.
How it works.
- 1
Vector embedding captures semantic meaning of experience
Each resume is read for what the experience means, not just the words used.
- 2
Recursive logic mining identifies problem-solving patterns
The model looks for evidence of how a candidate has solved problems before.
- 3
Skill mapping against role requirements
Skills are mapped against the requirements you set for the role.
- 4
Cross-role intelligence surfaces hidden matches
Candidates with transferable skills from other roles and industries are surfaced too.
ContributorSkillLanguageVerified
Dr. A. Mensah4.8EN, FR
L. García4.7EN, ES
K. Tanaka4.6EN, JA
M. Okafor4.5EN
What you get
- Logic-first candidate identification
- Cross-role intelligence for non-traditional backgrounds
- Reasoning history logs for every match
- Semantic understanding beyond keywords
Fits your workflow.
Neural Match integrates with your ATS workflow, analyzing every candidate against role requirements. Matches include detailed reasoning explaining why each candidate fits, so your team can review faster and decide with the evidence in front of them.
GreenhouseLeverWorkdayWho it's for
- Teams hiring for skills over titles
- Technical hiring managers
- Recruiters with large applicant pools
- Employers open to non-traditional backgrounds
Use cases
- Find strong candidates with unusual career paths
- Re-rank past applicants for a new role
- Cut manual resume review
- Show hiring managers why a candidate fits
Benefits
- Surface candidates missed by keyword matching
- Match skills and experience to the role's requirements
- Reduce time spent manually reviewing resumes
- Identify hidden talent in non-traditional backgrounds
Before and after.
Keyword matching
Filters that miss good candidates and pass keyword-stuffed resumes.
With Neural Match
Matches based on what experience means, with the reasoning shown for each one.
See it in action.
Book a 20-minute walkthrough tailored to your hiring stack. We'll show you exactly how this capability fits your workflow.
Questions, answered
How is Neural Match different from keyword matching?
Neural Match understands the meaning and context behind experience, not just keyword presence. It matches skills and experience to the role's requirements and explains each match.
What about candidates with non-traditional backgrounds?
Cross-role intelligence specifically identifies candidates whose skills transfer across domains, surfacing talent that keyword matching would miss entirely.
How accurate is the matching?
Matching is only as good as the requirements behind it, so every match comes with the reasoning behind it: the skills and experience that fit and the gaps that remain. Your team can check that reasoning and override any score. The best way to judge it is on your own roles: start with a 7-day free trial and 50 credits and compare its shortlist with your team's picks.
See it on your roles. Shortlist in hours.
- 7-day trial, 50 free credits
- No card needed
- Live in 24 to 48 hours