Correctly 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.
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
Research media screenshot 202026 05 05 20124028.png
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
Also checked on this input — same tool, 3 other criteria
Field coverage✓ WorkedCovers the standard resume fields needed for the baseline test: name, email, phone, experience, education, and skills are all present.Noise in output⚠ StruggledPuts four programming languages — Python, JavaScript, SQL, and Bash — into the Languages field even though the resume does not contain a spoken-languages section.Output quality✓ WorkedReturns a clean, minimal JSON payload with the core resume sections extracted and no extra metadata noise.
Provenance
- Observation
- af7f942c-0377-4544-847c-c687a02bf8ac
- 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: "extracta-labs",
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.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◐ MixedThe clean resume education GPA was captured, but as the string "CGPA: 8.2 / 10" instead of a clean normalized numeric value.OpenResume✗ FailedMisplaces GPA/CGPA into Date and leaves GPA blank, so academic data is routed to the wrong field.Parseur✓ WorkedOn the baseline resume, the identity/contact values were extracted correctly, including the candidate name, title, location, phone number, and LinkedIn URL.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