Trace claims. Verify evidence.
TraceLayer turns technical resume claims into an evidence report recruiters can review and candidates can improve before the interview.
Source graph
Senior ML Engineer CV
Publications
Google Scholar, Semantic Scholar, DOI metadata
Repositories
GitHub commits, releases, README evidence
Timeline
ORCID, institutional pages, personal website
Why it matters
Hiring needs evidence signals that stay calm under pressure.
AI-generated resumes are noisy
Technical profiles are easier to produce and harder to evaluate. Hiring teams need structured evidence signals, not more open tabs.
Strong candidates need better evidence signals
Public work, publications, repositories, and technical profiles can show depth when they are connected to specific claims.
Recruiters need structured claim review
TraceLayer organizes claim support so reviewers can focus on judgment instead of repeating manual searching.
Workflow
From resume claim to reviewable evidence.
Upload resume/CV
Start with a PDF or profile export.
Extract claims
Identify roles, skills, publications, projects, and timelines.
Match public evidence
Connect claims to public technical sources and metadata.
Generate TraceLayer Report
Review support levels, gaps, and source trails in one report.
What TraceLayer checks
Concrete outputs from every evidence report.
Claim inventory
A structured list of roles, skills, publications, projects, education, and impact claims extracted from the resume.
Evidence map
A source-linked view of public evidence from GitHub, publications, technical profiles, and personal websites.
Timeline consistency
Role dates, publication dates, repository activity, and profile history organized for reviewer context.
Review queue
Claims with limited or unclear support are separated into a queue for human review before a decision is made.
Source metadata
Compact metadata for URLs, authors, dates, repository activity, publication identifiers, and source categories.
TraceLayer Report
A source-specific report reviewers can actually use.
The sample report groups claims by support level, source metadata, timeline consistency, skill signals, and items that require human review.
TraceLayer Report TL-2408
Technical CV Evidence Review
Sources scanned: GitHub, Google Scholar, ORCID, Semantic Scholar, personal websites, institutional pages, and publication metadata
Verified publications
NeurIPS workshop paper
Google Scholar title, author, and year match
Systems preprint
Semantic Scholar and DOI metadata match
Citation profile
ORCID linked from personal website
Timeline consistency
Research role
Institutional page dates align
Open-source project
GitHub activity overlaps role
Graduate program
ORCID and publication dates align
Skill evidence map
Claims requiring review
Maintainer scope
GitHub evidence found, ownership unclear
Production scale
Claim lacks a public source
Patent reference
Publication identifier not included
Use cases
Built for both sides of technical hiring.
Candidates
Find unsupported claims before recruiters do, strengthen your evidence links, and turn your CV into a cleaner technical portfolio.
Recruiters
Turn manual claim checking into a structured review flow with clear evidence status and human decision points.
Research and technical hiring teams
Review publications, project history, skill evidence, and timeline consistency before deeper interviews.
Trust language
Evidence support, not automatic judgment.
Not a lie detector.
TraceLayer organizes public evidence and support levels. It does not replace reviewer context, candidate conversation, or domain judgment.
Waitlist
Join the early TraceLayer.cv waitlist.
We are opening early access for candidates, recruiters, founders, researchers, and technical hiring teams who want structured evidence review.