On the baseline resume, the identity/contact values were extracted correctly, including the candidate name, title, location, phone number, and LinkedIn URL.
What was measured
Accuracy
Are extracted values correct and complete?
decisive for this rankingtransformation
Correct and complete values are the essence of resume parsing, so this directly determines whether the tool succeeds. (3 of 3 judges)
What was given, what came back
Test input: Clean single-column resume — Rugved Nichite · pdf · group: resume-parsing
Input — what we sent
Clean single-column resume — Rugved Nichite
A professionally structured single-column resume for Rugved Nichite, used as the baseline input for parser accuracy across standard resume fields.
Why this input is hard
- · baseline field extraction
- · contact info accuracy
- · work experience parsing
- · education and CGPA extraction
- · skills and certifications extraction
Output — unretouched
Also checked on this input — same tool, 4 other criteria
Field coverage✓ WorkedOn the baseline resume, the parser populated the core resume fields: name, email, phone, work experience, education, and skills.Output quality✓ WorkedThe baseline run was described as very good overall: the output was clean, readable, and usable for the major fields that were captured.Output quality◐ MixedCertifications were flattened into one comma-separated string containing two credentials and years instead of separate structured entries.Output quality◐ MixedCGPA was embedded inside the education string rather than extracted as a dedicated field.
Provenance
- Observation
- 29c1ebce-80f1-41c2-9e9e-be6eaf915f4d
- Evidence run
- cbbef4db-964c-49fa-a57f-a2977822bdfc
- Study
- Parse resumes into structured data using an API
- Research task
- 86b9jm30n
- Tested at
- not recorded
- Source
- first-party
- Evidence state
- verified
- Proof shown
- input + output shown
- Cost / latency
- not captured
- Repeat run
- not captured
- Tester
- not captured
The last three rows are honest blanks, not placeholders — our capture has no field for them yet.
Query this
get_evidence({
tool: "parseur",
scenario: "resume-parsing"
})MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 9 other tools
measured on Accuracy
Affinda◐ MixedIt split one LinkedIn profile into two website fields and dropped the /in/ path segment, so the URL was not reconstructed as a single complete value.Airparser◐ MixedMisreads the email local-part on a clean resume, returning "rugged.nichite@email.com" instead of "rugved.nichite@email.com".CVParserPro✗ FailedThe experience field was badly inflated: the profile header showed 11 years, while the report says the candidate actually had only about 2–3 years.Extracta.ai✓ WorkedCorrectly extracts the core personal and work-history values from the clean resume, including both jobs, the education entry, two certifications, and the full 15-skill list.Hireability✗ FailedName accuracy failed: GivenName was split as 'Dev' and FamilyName as 'loper', producing FormattedName 'Dev loper' instead of the candidate's real name.HrFlow◐ MixedOn the clean resume, it still misses a listed task bullet ('Evaluated 10+ AI/ML APIs...') and does not return CGPA.LlamaParse✓ WorkedCore contact data for the clean resume — name, location, phone, email, and LinkedIn URL — was extracted correctly.OpenResume✗ FailedMisplaces GPA/CGPA into Date and leaves GPA blank, so academic data is routed to the wrong field.Skima AI✓ WorkedThe core extracted values were correct on the clean resume, including Rugved Nichite, rugved.nchite@email.com, 9876500000, Vasind, the work histories, the education details, and the 2.9-year total experience figure.
This evidence is published in
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com