On the clean resume, it extracted the core resume fields: name, email, phone, work experience, education, and skills.
What was measured
Field coverage
Are name, email, phone, experience, education, and skills extracted?
decisive for this rankingtransformation
A resume parser should extract the key canonical fields; missing them means it is not doing the main job well. (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 image.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
Accuracy◐ MixedOn the clean resume, it still misses a listed task bullet ('Evaluated 10+ AI/ML APIs...') and does not return CGPA.Noise in output✗ FailedIt injects non-skill fragments into the skills list, including 'ml apis', 'lambda', 's3', 'ml', and 'rest apis'.Output quality◐ MixedOverall output is usable, but the clean-resume parse still shows normalization and classification issues.
Provenance
- Observation
- 16ecdece-b2b7-4e4b-a26e-f6271ad59376
- 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: "hrflow",
scenario: "resume-parsing"
})MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 9 other tools
measured on Field coverage
Affinda✓ WorkedOn the clean resume, the parser covered the standard resume sections expected by the benchmark: identity/contact, work experience, education, skills, and certifications.Airparser✓ WorkedCovers the baseline resume fields end to end: name, email, phone, work experience, education, and skills.CVParserPro✓ WorkedOn the baseline resume, CVParserPro extracted the core benchmark field set: name, email, phone, total experience, education, and skills; the report also says both certifications were captured.Extracta.ai✓ WorkedCovers the standard resume fields needed for the baseline test: name, email, phone, experience, education, and skills are all present.Hireability✓ WorkedThe clean resume returned the core resume sections expected for benchmarking: name, email, phone, experience, education, and skills were all present, along with location, LinkedIn, summary, certifications, and competencies.LlamaParse✓ WorkedThe clean output included the core resume fields name, email, phone, work experience, education, and skills.OpenResume⚠ StruggledIt does not fully cover the baseline fields on a standard resume: Name and Phone remain unpopulated, even though email, experience, education, and skills are extracted.Parseur✓ WorkedOn the baseline resume, the parser populated the core resume fields: name, email, phone, work experience, education, and skills.Skima AI✓ WorkedOn the clean resume, the parser returned the six core fields expected for resume coverage: name, email, phone, work experience, education, and skills.
This evidence is published in
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com