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Affinda

Best overall resume parsing API here for clean, multi-column, and messy PDFs with rich structured JSON.

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Structured JSONMulti-column PDFsCGPA missingSkill hallucinations
TL;DR — our verdictUpdated August 2026 · 22 test artifacts

Best overall parser in this test, set with cleanup still needed.

Where it wins
  • You need a resume parser API that handles clean single-column, multi-column, and messy PDF resumes without manual setup
  • You need machine-readable JSON with contact info, work history, education, skills, certifications, languages, projects, and hobbies
  • You want skill records enriched with taxonomy metadata and can post-process duplicates or noise afterward
Main limitation
  • You need CGPA numeric scores to be captured reliably without review
Pricing (verified plans)
Basic Testing FreeAdvanced Testing $80 one-timeTier 1 $800/yearHigher Tiers Custom pricing
Strongest test artifacts
JSON export →Resume PDF Field Extraction →Project description truncation →

Feature scores on this page: 9.0/10 (1 scored feature)

Our take

Affinda is the clear overall winner in this test set: it parsed all three resumes end-to-end, returned structured JSON, and stayed strong on clean, multi-column, and messy layouts. The tradeoff is that CGPA scores, some certifications, URL handling, and skill hygiene still need downstream validation.

Download video: Demo video
Screen recording of the Affinda review flow across the three tested resumes.

In-Depth Review

Our detailed analysis of Affinda — features, performance, and real-world testing.

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Verified Review

Feature-by-Feature Breakdown

Resume PDF Field Extraction
Reliable
9/10
▾
Test Summary
Feature tested: Resume PDF Field Extraction
Result: Failed (9/10) — Reliable

Feature tested: Resume PDF Field Extraction

Result: Failed (9/10)

Verdict: Reliable

Expected behavior: Accepts resume PDFs and returns structured JSON with core candidate fields such as contact details, work history, education, certifications, and related profile data. The same parser was exercised on clean single-column, two-column, and messy free-form resumes.

Test case: PDF document → Text/code file

Input type: PDF document

Input used: Input artifact (PDF document): Clean resume input — Affinda Input.1.pdf

Observed output: Output artifact (Text/code file): Structured JSON was produced for the clean resume, with the main candidate fields, work history, education, certifications, and skills exported in named fields. — json output 1.txt

Input artifact: Input artifact (PDF document): Clean resume input — Affinda Input.1.pdf

Output artifact: Output artifact (Text/code file): Structured JSON was produced for the clean resume, with the main candidate fields, work history, education, certifications, and skills exported in named fields. — json output 1.txt

What changed: PDF document transformed into Text/code file

Test case: PDF document → Text/code file

Input type: PDF document

Input used: Input artifact (PDF document): Multi-column resume input — Affinda Input.2.pdf

Observed output: Output artifact (Text/code file): Structured JSON was produced for the multi-column resume, with both columns parsed into named fields. — json output 2.txt

Input artifact: Input artifact (PDF document): Multi-column resume input — Affinda Input.2.pdf

Output artifact: Output artifact (Text/code file): Structured JSON was produced for the multi-column resume, with both columns parsed into named fields. — json output 2.txt

What changed: PDF document transformed into Text/code file

Test case: PDF document → Text/code file

Input type: PDF document

Input used: Input artifact (PDF document): Messy resume input — Affinda Input.3.pdf

Observed output: Output artifact (Text/code file): Structured JSON was produced for the messy resume, with candidate data exported despite the non-standard formatting. — Json output 3.txt

Input artifact: Input artifact (PDF document): Messy resume input — Affinda Input.3.pdf

Output artifact: Output artifact (Text/code file): Structured JSON was produced for the messy resume, with candidate data exported despite the non-standard formatting. — Json output 3.txt

What changed: PDF document transformed into Text/code file

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Input — Affinda Input.1.pdf

Observed output: Output artifact (Image): Email, phone, and location were extracted, but the LinkedIn value was split across two website fields and the /in/ path was missing. — image-2.png

Input artifact: Input artifact (PDF document): Input — Affinda Input.1.pdf

Output artifact: Output artifact (Image): Email, phone, and location were extracted, but the LinkedIn value was split across two website fields and the /in/ path was missing. — image-2.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Input — Affinda Input.2.pdf

Observed output: Output artifact (Image): Objective, summary, and expected salary were all empty on the Priya Sharma resume even though the source had content. — image-7.png

Input artifact: Input artifact (PDF document): Input — Affinda Input.2.pdf

Output artifact: Output artifact (Image): Objective, summary, and expected salary were all empty on the Priya Sharma resume even though the source had content. — image-7.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Source resume — Affinda Input.2.pdf

Observed output: Output artifact (Image): The project description was truncated before the Accuracy: 89% metric, so one performance detail was lost. — image-9.png

Input artifact: Input artifact (PDF document): Source resume — Affinda Input.2.pdf

Output artifact: Output artifact (Image): The project description was truncated before the Accuracy: 89% metric, so one performance detail was lost. — image-9.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Input — Affinda Input.3.pdf

Observed output: Output artifact (Image): Junior Developer was extracted, but total years of experience was inflated to 7.3 even though the resume states 3 years. — image-11.png

Input artifact: Input artifact (PDF document): Input — Affinda Input.3.pdf

Output artifact: Output artifact (Image): Junior Developer was extracted, but total years of experience was inflated to 7.3 even though the resume states 3 years. — image-11.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Source resume — Affinda Input.1.pdf

Observed output: Output artifact (Image): The skills list contained repeated entries such as Research, Python, and Artificial Intelligence, so deduplication is needed. — image-3.png

Input artifact: Input artifact (PDF document): Source resume — Affinda Input.1.pdf

Output artifact: Output artifact (Image): The skills list contained repeated entries such as Research, Python, and Artificial Intelligence, so deduplication is needed. — image-3.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Source resume — Affinda Input.1.pdf

Observed output: Output artifact (Image): Certification names were treated as skills, and IBM Mainframe appeared even though it was not in the resume. — image-4.png

Input artifact: Input artifact (PDF document): Source resume — Affinda Input.1.pdf

Output artifact: Output artifact (Image): Certification names were treated as skills, and IBM Mainframe appeared even though it was not in the resume. — image-4.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Source resume — Affinda Input.2.pdf

Observed output: Output artifact (Image): American Welding Society Codes was injected as a skill despite not appearing in the source resume. — image-8.png

Input artifact: Input artifact (PDF document): Source resume — Affinda Input.2.pdf

Output artifact: Output artifact (Image): American Welding Society Codes was injected as a skill despite not appearing in the source resume. — image-8.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Source resume — Affinda Input.3.pdf

Observed output: Output artifact (Image): Business Education was injected as a skill even though it was not in the resume. — image-13.png

Input artifact: Input artifact (PDF document): Source resume — Affinda Input.3.pdf

Output artifact: Output artifact (Image): Business Education was injected as a skill even though it was not in the resume. — image-13.png

What changed: PDF document transformed into Image

Why it matters / Conclusion: A strong core parser pipeline: it accepted every PDF tested and returned structured JSON consistently.

Accepts resume PDFs and returns structured JSON with core candidate fields such as contact details, work history, education, certifications, and related profile data. The same parser was exercised on clean single-column, two-column, and messy free-form resumes.

pdf
Affinda Input.1.pdf
↓
txt
json output 1.txt
Loading file...
Structured JSON was produced for the clean resume, with the main candidate fields, work history, education, certifications, and skills exported in named fields.
pdf
Affinda Input.2.pdf
↓
txt
json output 2.txt
Loading file...
Structured JSON was produced for the multi-column resume, with both columns parsed into named fields.
pdf
Affinda Input.3.pdf
↓
txt
Json output 3.txt
Loading file...
Structured JSON was produced for the messy resume, with candidate data exported despite the non-standard formatting.
INPUT
Affinda Input.1.pdf
↓
OUTPUT
Output artifact for "Resume PDF Field Extraction" test: Email, phone, and location were extracted, but the LinkedIn value was split across two website fields and the /in/ path was missing., image-2.png
Email, phone, and location were extracted, but the LinkedIn value was split across two website fields and the /in/ path was missing.
INPUT
Affinda Input.2.pdf
↓
OUTPUT
Output artifact for "Resume PDF Field Extraction" test: Objective, summary, and expected salary were all empty on the Priya Sharma resume even though the source had content., image-7.png
Objective, summary, and expected salary were all empty on the Priya Sharma resume even though the source had content.
pdf
Affinda Input.2.pdf
↓
image
Output artifact for "Resume PDF Field Extraction" test: The project description was truncated before the Accuracy: 89% metric, so one performance detail was lost., image-9.png
The project description was truncated before the Accuracy: 89% metric, so one performance detail was lost.
INPUT
Affinda Input.3.pdf
↓
OUTPUT
Output artifact for "Resume PDF Field Extraction" test: Junior Developer was extracted, but total years of experience was inflated to 7.3 even though the resume states 3 years., image-11.png
Junior Developer was extracted, but total years of experience was inflated to 7.3 even though the resume states 3 years.
pdf
Affinda Input.1.pdf
↓
image
Output artifact for "Resume PDF Field Extraction" test: The skills list contained repeated entries such as Research, Python, and Artificial Intelligence, so deduplication is needed., image-3.png
The skills list contained repeated entries such as Research, Python, and Artificial Intelligence, so deduplication is needed.
pdf
Affinda Input.1.pdf
↓
image
Output artifact for "Resume PDF Field Extraction" test: Certification names were treated as skills, and IBM Mainframe appeared even though it was not in the resume., image-4.png
Certification names were treated as skills, and IBM Mainframe appeared even though it was not in the resume.
pdf
Affinda Input.2.pdf
↓
image
Output artifact for "Resume PDF Field Extraction" test: American Welding Society Codes was injected as a skill despite not appearing in the source resume., image-8.png
American Welding Society Codes was injected as a skill despite not appearing in the source resume.
pdf
Affinda Input.3.pdf
↓
image
Output artifact for "Resume PDF Field Extraction" test: Business Education was injected as a skill even though it was not in the resume., image-13.png
Business Education was injected as a skill even though it was not in the resume.
Bottom Line
A strong core parser pipeline: it accepted every PDF tested and returned structured JSON consistently.
From our researchParse resumes into structured data using an API
Education and Certification Extraction
▾
Test Summary
Feature tested: Education and Certification Extraction
Result: Partial

Feature tested: Education and Certification Extraction

Result: Partial

Expected behavior: Extracts degree records, institutions, dates, and certification entries from resumes. The tested set also showed a repeated CGPA miss and one missed certification on a messy resume.

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Input — Affinda Input.1.pdf

Observed output: Output artifact (Image): The clean resume's education panel captured the degree and CGPA unit, but the numeric score remained empty at 8.2. — image.png

Input artifact: Input artifact (PDF document): Input — Affinda Input.1.pdf

Output artifact: Output artifact (Image): The clean resume's education panel captured the degree and CGPA unit, but the numeric score remained empty at 8.2. — image.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Input — Affinda Input.2.pdf

Observed output: Output artifact (Image): The multi-column resume repeated the same CGPA miss: unit captured as CGPA, score 8.7 not captured. — CGPA Score missing Input 2.png

Input artifact: Input artifact (PDF document): Input — Affinda Input.2.pdf

Output artifact: Output artifact (Image): The multi-column resume repeated the same CGPA miss: unit captured as CGPA, score 8.7 not captured. — CGPA Score missing Input 2.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Input — Affinda Input.3.pdf

Observed output: Output artifact (Image): Only one certificate was extracted from the messy resume; the AWS Coursera certification was missing. — image-12.png

Input artifact: Input artifact (PDF document): Input — Affinda Input.3.pdf

Output artifact: Output artifact (Image): Only one certificate was extracted from the messy resume; the AWS Coursera certification was missing. — image-12.png

What changed: PDF document transformed into Image

Why it matters / Conclusion: Useful for education and certification parsing, but the repeated CGPA miss and a dropped certification make manual validation necessary.

Extracts degree records, institutions, dates, and certification entries from resumes. The tested set also showed a repeated CGPA miss and one missed certification on a messy resume.

INPUT
Affinda Input.1.pdf
↓
OUTPUT
Output artifact for "Education and Certification Extraction" test: The clean resume's education panel captured the degree and CGPA unit, but the numeric score remained empty at 8.2., image.png
The clean resume's education panel captured the degree and CGPA unit, but the numeric score remained empty at 8.2.
INPUT
Affinda Input.2.pdf
↓
OUTPUT
Output artifact for "Education and Certification Extraction" test: The multi-column resume repeated the same CGPA miss: unit captured as CGPA, score 8.7 not captured., CGPA Score missing Input 2.png
The multi-column resume repeated the same CGPA miss: unit captured as CGPA, score 8.7 not captured.
INPUT
Affinda Input.3.pdf
↓
OUTPUT
Output artifact for "Education and Certification Extraction" test: Only one certificate was extracted from the messy resume; the AWS Coursera certification was missing., image-12.png
Only one certificate was extracted from the messy resume; the AWS Coursera certification was missing.
Bottom Line
Useful for education and certification parsing, but the repeated CGPA miss and a dropped certification make manual validation necessary.
From our researchParse resumes into structured data using an API
Skill Extraction with Taxonomy Metadata
▾
Test Summary
Feature tested: Skill Extraction with Taxonomy Metadata
Result: Failed

Feature tested: Skill Extraction with Taxonomy Metadata

Result: Failed

Expected behavior: Returns skill lists from resumes along with taxonomy-style metadata. The evidence also showed duplicates, certification names treated as skills, and some hallucinated taxonomy entries.

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Input — Affinda Input.1.pdf

Observed output: Output artifact (Image): Research appears twice, Python appears multiple times, and the skills list mixes inferred duplicates with the source skill section. — image-3.png

Input artifact: Input artifact (PDF document): Input — Affinda Input.1.pdf

Output artifact: Output artifact (Image): Research appears twice, Python appears multiple times, and the skills list mixes inferred duplicates with the source skill section. — image-3.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Input — Affinda Input.1.pdf

Observed output: Output artifact (Image): Certification names such as AWS Certified Cloud Practitioner and IBM Mainframe were treated as skills, adding noise. — image-4.png

Input artifact: Input artifact (PDF document): Input — Affinda Input.1.pdf

Output artifact: Output artifact (Image): Certification names such as AWS Certified Cloud Practitioner and IBM Mainframe were treated as skills, adding noise. — image-4.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Input — Affinda Input.2.pdf

Observed output: Output artifact (Image): American Welding Society Codes was hallucinated as a skill even though it was not in the resume. — image-8.png

Input artifact: Input artifact (PDF document): Input — Affinda Input.2.pdf

Output artifact: Output artifact (Image): American Welding Society Codes was hallucinated as a skill even though it was not in the resume. — image-8.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Input — Affinda Input.3.pdf

Observed output: Output artifact (Image): Business Education was injected as a skill despite not appearing anywhere in the John Kumar resume. — image-13.png

Input artifact: Input artifact (PDF document): Input — Affinda Input.3.pdf

Output artifact: Output artifact (Image): Business Education was injected as a skill despite not appearing anywhere in the John Kumar resume. — image-13.png

What changed: PDF document transformed into Image

Why it matters / Conclusion: The skill output is rich, but the duplicate and hallucinated entries are too noisy to trust without cleanup.

Returns skill lists from resumes along with taxonomy-style metadata. The evidence also showed duplicates, certification names treated as skills, and some hallucinated taxonomy entries.

INPUT
Affinda Input.1.pdf
↓
OUTPUT
Output artifact for "Skill Extraction with Taxonomy Metadata" test: Research appears twice, Python appears multiple times, and the skills list mixes inferred duplicates with the source skill section., image-3.png
Research appears twice, Python appears multiple times, and the skills list mixes inferred duplicates with the source skill section.
INPUT
Affinda Input.1.pdf
↓
OUTPUT
Output artifact for "Skill Extraction with Taxonomy Metadata" test: Certification names such as AWS Certified Cloud Practitioner and IBM Mainframe were treated as skills, adding noise., image-4.png
Certification names such as AWS Certified Cloud Practitioner and IBM Mainframe were treated as skills, adding noise.
INPUT
Affinda Input.2.pdf
↓
OUTPUT
Output artifact for "Skill Extraction with Taxonomy Metadata" test: American Welding Society Codes was hallucinated as a skill even though it was not in the resume., image-8.png
American Welding Society Codes was hallucinated as a skill even though it was not in the resume.
INPUT
Affinda Input.3.pdf
↓
OUTPUT
Output artifact for "Skill Extraction with Taxonomy Metadata" test: Business Education was injected as a skill despite not appearing anywhere in the John Kumar resume., image-13.png
Business Education was injected as a skill despite not appearing anywhere in the John Kumar resume.
Bottom Line
The skill output is rich, but the duplicate and hallucinated entries are too noisy to trust without cleanup.
From our researchParse resumes into structured data using an API
Custom Field Configuration and Review Workflow
The interface exposes a configurable field schema and a document review step, but the report did not test authoring a new schema end to end.
▾
Test Summary
Feature tested: Custom Field Configuration and Review Workflow
Result: Partial — The interface exposes a configurable field schema and a document review step, but the report did not test authoring a new schema end to end.

Feature tested: Custom Field Configuration and Review Workflow

Result: Partial

Verdict: The interface exposes a configurable field schema and a document review step, but the report did not test authoring a new schema end to end.

Expected behavior: Provides configurable extraction fields and a confirmation-oriented review interface around parsed resumes. The evidence showed a Configure Fields UI and review workflow, but did not verify that custom mappings changed extraction behavior.

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Source resume — Affinda Input.3.pdf

Observed output: Output artifact (Image): The workspace exposed a Configure Fields panel and a Confirm Document review step around parsed education fields. — image-21.png

Input artifact: Input artifact (PDF document): Source resume — Affinda Input.3.pdf

Output artifact: Output artifact (Image): The workspace exposed a Configure Fields panel and a Confirm Document review step around parsed education fields. — image-21.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Clean resume source PDF used in the review workflow test. — Affinda Input.1.pdf

Observed output: Output artifact (Image): The clean-resume view shows the parsed document in the review interface with an Upload Documents action and a sidebar of extracted fields. — Screenshot 2026-05-05 120443.png

Input artifact: Input artifact (PDF document): Clean resume source PDF used in the review workflow test. — Affinda Input.1.pdf

Output artifact: Output artifact (Image): The clean-resume view shows the parsed document in the review interface with an Upload Documents action and a sidebar of extracted fields. — Screenshot 2026-05-05 120443.png

What changed: PDF document transformed into Image

Why it matters / Conclusion: Useful when you need to inspect or tune extraction in a review workflow, but the report did not verify custom schema creation beyond the exposed Configure Fields UI.

Provides configurable extraction fields and a confirmation-oriented review interface around parsed resumes. The evidence showed a Configure Fields UI and review workflow, but did not verify that custom mappings changed extraction behavior.

pdf
Affinda Input.3.pdf
↓
image
Output artifact for "Custom Field Configuration and Review Workflow" test: The workspace exposed a Configure Fields panel and a Confirm Document review step around parsed education fields., image-21.png
The workspace exposed a Configure Fields panel and a Confirm Document review step around parsed education fields.
INPUT
Affinda Input.1.pdf
Clean resume source PDF used in the review workflow test.
↓
OUTPUT
Output artifact for "Custom Field Configuration and Review Workflow" test: The clean-resume view shows the parsed document in the review interface with an Upload Documents action and a sidebar of extracted fields., Screenshot 2026-05-05 120443.png
The clean-resume view shows the parsed document in the review interface with an Upload Documents action and a sidebar of extracted fields.
Bottom Line
Useful when you need to inspect or tune extraction in a review workflow, but the report did not verify custom schema creation beyond the exposed Configure Fields UI.
From our researchParse resumes into structured data using an API
Structured List Section Extraction
Useful
▾
Test Summary
Feature tested: Structured List Section Extraction
Result: Passed — Useful

Feature tested: Structured List Section Extraction

Result: Passed

Verdict: Useful

Expected behavior: Extracts list-like resume sections such as languages, projects, and hobbies, returning them as arrays when present. The tested resumes showed language proficiency levels, multiple projects, and hobbies in structured list form.

Test case: PDF document → Text/code file

Input type: PDF document

Input used: Input artifact (PDF document): Multi-column resume input — Affinda Input.2.pdf

Observed output: Output artifact (Text/code file): The multi-column resume included extracted language entries with proficiency levels and both project entries in the JSON output. — json output 2.txt

Input artifact: Input artifact (PDF document): Multi-column resume input — Affinda Input.2.pdf

Output artifact: Output artifact (Text/code file): The multi-column resume included extracted language entries with proficiency levels and both project entries in the JSON output. — json output 2.txt

What changed: PDF document transformed into Text/code file

Test case: PDF document → Text/code file

Input type: PDF document

Input used: Input artifact (PDF document): Messy resume input — Affinda Input.3.pdf

Observed output: Output artifact (Text/code file): The messy resume exported hobbies as a structured array, while the language field stayed null because no languages were present. — Json output 3.txt

Input artifact: Input artifact (PDF document): Messy resume input — Affinda Input.3.pdf

Output artifact: Output artifact (Text/code file): The messy resume exported hobbies as a structured array, while the language field stayed null because no languages were present. — Json output 3.txt

What changed: PDF document transformed into Text/code file

Why it matters / Conclusion: Good at structured list sections, but some project text can be truncated and absent fields are not inferred.

Extracts list-like resume sections such as languages, projects, and hobbies, returning them as arrays when present. The tested resumes showed language proficiency levels, multiple projects, and hobbies in structured list form.

pdf
Affinda Input.2.pdf
↓
txt
json output 2.txt
Loading file...
The multi-column resume included extracted language entries with proficiency levels and both project entries in the JSON output.
pdf
Affinda Input.3.pdf
↓
txt
Json output 3.txt
Loading file...
The messy resume exported hobbies as a structured array, while the language field stayed null because no languages were present.
Bottom Line
Good at structured list sections, but some project text can be truncated and absent fields are not inferred.
From our researchParse resumes into structured data using an API

Pricing & Access

Plans as of May 2026. Tested on the free plan

TESTED
Basic Testing
Free
14-day free trial with all features, parsing limit of 200 documents, expires after 1 month
Advanced Testing
$80 one-time
3-month trial period for full integration testing, parsing limit of 2,000 documents, expires after 3 months
Tier 1
$800/year
6,000 parses per year, all features included, API access
Higher Tiers
Custom pricing
Bulk parsing packages available, more parses per year at lower cost per parse as volume increases, self-hosted annual subscription also available

Pricing checked May 2026. We re-check quarterly. Visit affinda.com for current enterprise pricing.

✓ Use This If
●You need a resume parser API that handles clean single-column, multi-column, and messy PDF resumes without manual setup
●You need machine-readable JSON with contact info, work history, education, skills, certifications, languages, projects, and hobbies
●You want skill records enriched with taxonomy metadata and can post-process duplicates or noise afterward
●You need layout-robust extraction of multi-section resumes, including languages with proficiency levels
✕ Skip This If
●You need CGPA numeric scores to be captured reliably without review
●You need the full LinkedIn profile path preserved exactly
●You need experience totals to respect the candidate's stated years instead of being calculated from dates
●You need zero-hallucination skills with no duplicate or certification-derived noise
●You need every certification to be extracted without misses
developer-toolsapistextOther
Yes. In this test set, it parsed all three PDFs directly without manual field mapping or template setup.
It returned structured JSON for all three tested resumes, with named fields throughout.
No. Across the clean and multi-column resumes, the CGPA unit was identified, but the numeric score stayed empty.
Not reliably. The Rugved Nichite resume showed the LinkedIn value split across fields, and the /in/ path segment was missing.
Not always. On the messy John Kumar resume, it calculated 7.3 years even though the resume explicitly stated 3 years.
Yes. The report shows duplicate skills, certification names being treated as skills, and hallucinated terms like American Welding Society Codes, Business Education, and IBM Mainframe.
Mostly yes, but with gaps. It extracted certifications and languages on the multi-column resume, extracted hobbies on the messy resume, and also showed a missed AWS Coursera certification plus a truncated project description in the test set.

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