Covers the baseline resume fields end to end: 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
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
No output artifact
The verdict rests on the tester's written observation alone — no file was captured for this cell.
Also checked on this input — same tool, 4 other criteria
Accuracy◐ MixedMisreads the email local-part on a clean resume, returning "rugged.nichite@email.com" instead of "rugved.nichite@email.com".Accuracy◐ MixedSilently truncates a compound job title, returning "AI Research Analyst" and dropping "& Software Developer".Input handling✓ WorkedAccepts a PDF resume directly and parses it on first upload without manual configuration.Output quality◐ MixedProduces readable, structured JSON, but the overall parse quality is reduced by a wrong email address and a truncated job title.
Provenance
- Observation
- 222e2531-2544-42f5-8874-3e88f504d609
- 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 only
- 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: "airparser",
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.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.HrFlow✓ WorkedOn the clean resume, it extracted the core resume fields: name, email, phone, work experience, education, and skills.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