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Extracta.ai

Schema-first resume parsing that stays lean and predictable across clean, multi-column, and messy PDFs.

Visit Extracta.ai
Schema-driven JSONMulti-column parsingMessy resume support
TL;DR — our verdictUpdated August 2026 · 20 test artifacts

Best when you know the schema up front

Where it wins
  • You already know exactly which fields you need and can define the schema up front.
  • You want lean JSON with no extra metadata, taxonomy IDs, or unrelated fields.
  • You need resume PDFs parsed reliably across clean, two-column, and messy layouts.
Main limitation
  • You need LinkedIn, summary, objective, hobbies, or projects inferred automatically without defining them first.
Pricing (verified plans)
Free $0Starter $9/moGrowth $29/moBusiness $79/mo
Strongest test artifacts
Schema-Driven Resume Extraction →OUTPUT →extracta.ai output2.txt →

Feature scores on this page: 9.0/10 (2 scored features)

Our take

Extracta.ai is the strongest fit for schema-first resume parsing: it turns clean, two-column, and messy PDF resumes into lean JSON with no metadata noise or extra taxonomy fields. The tradeoff is that it only returns what you define, so undefined fields are silently skipped and some values like CGPA, marks, languages, and certifications may still need downstream cleanup or normalization.

Download video: Demo video
Screen recording of Extracta.ai's resume data extraction workflow in Chrome.

In-Depth Review

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

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AI Demos Team
Expert Reviewer
Verified Review

Feature-by-Feature Breakdown

Schema-Driven Resume Extraction
9/10
▾
Test Summary
Feature tested: Schema-Driven Resume Extraction
Result: Passed (9/10)

Feature tested: Schema-Driven Resume Extraction

Result: Passed (9/10)

Expected behavior: Extracta.ai lets users define target fields up front and returns only those fields as lean JSON. The member cards show this on schema-bound runs over a clean single-column resume and on examples where unspecified fields are omitted.

Test case: PDF document → Text/code file

Input type: PDF document

Input used: Input artifact (PDF document): Input — Extracta.ai input.1.pdf

Observed output: Output artifact (Text/code file): Clean single-column resume parsed after the schema was defined; the output stayed lean and only included the modeled fields, with LinkedIn omitted because it was not in schema. — extracta.ai output 1.txt

Input artifact: Input artifact (PDF document): Input — Extracta.ai input.1.pdf

Output artifact: Output artifact (Text/code file): Clean single-column resume parsed after the schema was defined; the output stayed lean and only included the modeled fields, with LinkedIn omitted because it was not in schema. — extracta.ai 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): Input — Extracta.ai Input.2.pdf

Observed output: Output artifact (Text/code file): Two-column resume parsed without manual layout setup; the output stayed minimal and schema-bound. — extracta.ai output2.txt

Input artifact: Input artifact (PDF document): Input — Extracta.ai Input.2.pdf

Output artifact: Output artifact (Text/code file): Two-column resume parsed without manual layout setup; the output stayed minimal and schema-bound. — extracta.ai output2.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): Input — Extracta.ai input.3.pdf

Observed output: Output artifact (Text/code file): Messy resume parsed successfully; the same schema-controlled field set was returned in structured JSON. — extracta.ai output 3.txt

Input artifact: Input artifact (PDF document): Input — Extracta.ai input.3.pdf

Output artifact: Output artifact (Text/code file): Messy resume parsed successfully; the same schema-controlled field set was returned in structured JSON. — extracta.ai output 3.txt

What changed: PDF document transformed into Text/code file

Why it matters / Conclusion: Strong and predictable for teams that can define the schema upfront; weak if you expect the parser to infer fields that were never declared.

Extracta.ai lets users define target fields up front and returns only those fields as lean JSON. The member cards show this on schema-bound runs over a clean single-column resume and on examples where unspecified fields are omitted.

pdf
Extracta.ai input.1.pdf
↓
text
extracta.ai output 1.txt
Loading file...
Clean single-column resume parsed after the schema was defined; the output stayed lean and only included the modeled fields, with LinkedIn omitted because it was not in schema.
pdf
Extracta.ai Input.2.pdf
↓
text
extracta.ai output2.txt
Loading file...
Two-column resume parsed without manual layout setup; the output stayed minimal and schema-bound.
pdf
Extracta.ai input.3.pdf
↓
text
extracta.ai output 3.txt
Loading file...
Messy resume parsed successfully; the same schema-controlled field set was returned in structured JSON.
Bottom Line
Strong and predictable for teams that can define the schema upfront; weak if you expect the parser to infer fields that were never declared.
From our researchParse resumes into structured data using an APIearlier research
Layout-Agnostic Resume Parsing
Very Good — lean clean JSON with only defined fields, no metadata noise
9/10
▾
Test Summary
Feature tested: Layout-Agnostic Resume Parsing
Result: Passed (9/10) — Very Good — lean clean JSON with only defined fields, no metadata noise

Feature tested: Layout-Agnostic Resume Parsing

Result: Passed (9/10)

Verdict: Very Good — lean clean JSON with only defined fields, no metadata noise

Expected behavior: Extracta.ai parses resumes without manual layout mapping, including clean single-column PDFs, two-column/sidebar resumes, and messy formatting. The evidence covers automatically reading both columns and handling non-standard resume structure without layout hints.

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Clean single-column resume input. — Extracta.ai input.1.pdf

Observed output: Output artifact (Image): Parsed successfully on first upload with the clean resume layout. — Clean resume parsed succesfully.png

Input artifact: Input artifact (PDF document): Clean single-column resume input. — Extracta.ai input.1.pdf

Output artifact: Output artifact (Image): Parsed successfully on first upload with the clean resume layout. — Clean resume parsed succesfully.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

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

Observed output: Output artifact (Image): The two-column resume parsed without layout setup, and the extracted JSON preserved both the work history and the right-side sections correctly. — Multi colum resume parsed succesfully.png

Input artifact: Input artifact (PDF document): Extracta.ai Input.2.pdf — Extracta.ai Input.2.pdf

Output artifact: Output artifact (Image): The two-column resume parsed without layout setup, and the extracted JSON preserved both the work history and the right-side sections correctly. — Multi colum resume parsed succesfully.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

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

Observed output: Output artifact (Image): Parsed successfully despite inconsistent formatting and missing structural markers. — Messy resume parsed succesfully.png

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

Output artifact: Output artifact (Image): Parsed successfully despite inconsistent formatting and missing structural markers. — Messy resume parsed succesfully.png

What changed: PDF document transformed into Image

Why it matters / Conclusion: Multi-column parsing works excellently. All fields from both columns extracted correctly including the spoken languages (English, Hindi, Marathi) from the right sidebar — correctly identified from the dedicated LANGUAGES section. All 12 skills from the sidebar returned as individual array items. Main weaknesses are LinkedIn URL not extracted (not in schema), key projects not returned (not in schema), and the job title headline not captured. Fields not defined in the schema are never returned regardless of how prominent they are in the resume.

Extracta.ai parses resumes without manual layout mapping, including clean single-column PDFs, two-column/sidebar resumes, and messy formatting. The evidence covers automatically reading both columns and handling non-standard resume structure without layout hints.

pdf
Extracta.ai input.1.pdf
Clean single-column resume input.
↓
text
Output artifact for "Layout-Agnostic Resume Parsing" test: Parsed successfully on first upload with the clean resume layout., Clean resume parsed succesfully.png
Parsed successfully on first upload with the clean resume layout.
file
Extracta.ai Input.2.pdf
↓
file
Output artifact for "Layout-Agnostic Resume Parsing" test: The two-column resume parsed without layout setup, and the extracted JSON preserved both the work history and the right-side sections correctly., Multi colum resume parsed succesfully.png
The two-column resume parsed without layout setup, and the extracted JSON preserved both the work history and the right-side sections correctly.
pdf
Extracta.ai input.3.pdf
Messy resume input.
↓
text
Output artifact for "Layout-Agnostic Resume Parsing" test: Parsed successfully despite inconsistent formatting and missing structural markers., Messy resume parsed succesfully.png
Parsed successfully despite inconsistent formatting and missing structural markers.
Bottom Line
Multi-column parsing works excellently. All fields from both columns extracted correctly including the spoken languages (English, Hindi, Marathi) from the right sidebar — correctly identified from the dedicated LANGUAGES section. All 12 skills from the sidebar returned as individual array items. Main weaknesses are LinkedIn URL not extracted (not in schema), key projects not returned (not in schema), and the job title headline not captured. Fields not defined in the schema are never returned regardless of how prominent they are in the resume.
From our researchParse resumes into structured data using an APIearlier research
Education Extraction and Normalization
▾
Test Summary
Feature tested: Education Extraction and Normalization
Result: Partial

Feature tested: Education Extraction and Normalization

Result: Partial

Expected behavior: Extracta.ai extracts education entries with degree, institution, dates, and related text, including cases where subfields are missing or embedded in free text. The cards show education rows being captured and partially normalized rather than inferred.

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Input — Extracta.ai input.1.pdf

Observed output: Output artifact (Image): The education item was missing start_date because the resume did not provide one. — image-2.png

Input artifact: Input artifact (PDF document): Input — Extracta.ai input.1.pdf

Output artifact: Output artifact (Image): The education item was missing start_date because the resume did not provide one. — 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): Extracta.ai input.1.pdf — Extracta.ai input.1.pdf

Observed output: Output artifact (Image): CGPA 8.2 was embedded in the description field instead of being extracted into a standalone numeric field. — image-4.png

Input artifact: Input artifact (PDF document): Extracta.ai input.1.pdf — Extracta.ai input.1.pdf

Output artifact: Output artifact (Image): CGPA 8.2 was embedded in the description field instead of being extracted into a standalone numeric field. — 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 — Extracta.ai Input.2.pdf

Observed output: Output artifact (Image): CGPA 8.7 also stayed buried in the description string. — image-7.png

Input artifact: Input artifact (PDF document): Input — Extracta.ai Input.2.pdf

Output artifact: Output artifact (Image): CGPA 8.7 also stayed buried in the description string. — 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): Extracta.ai Input.2.pdf — Extracta.ai Input.2.pdf

Observed output: Output artifact (Image): The education entry left both start_date and location empty placeholders because neither value was present in the resume section. — image-8.png

Input artifact: Input artifact (PDF document): Extracta.ai Input.2.pdf — Extracta.ai Input.2.pdf

Output artifact: Output artifact (Image): The education entry left both start_date and location empty placeholders because neither value was present in the resume section. — 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 — Extracta.ai input.3.pdf

Observed output: Output artifact (Image): 72 percent marks stayed as raw text and location was missing in the education entry. — image-10.png

Input artifact: Input artifact (PDF document): Input — Extracta.ai input.3.pdf

Output artifact: Output artifact (Image): 72 percent marks stayed as raw text and location was missing in the education entry. — image-10.png

What changed: PDF document transformed into Image

Why it matters / Conclusion: Good at finding education entries, but normalization is inconsistent and empty placeholders need downstream handling.

Extracta.ai extracts education entries with degree, institution, dates, and related text, including cases where subfields are missing or embedded in free text. The cards show education rows being captured and partially normalized rather than inferred.

pdf
Extracta.ai input.1.pdf
↓
image
Output artifact for "Education Extraction and Normalization" test: The education item was missing start_date because the resume did not provide one., image-2.png
The education item was missing start_date because the resume did not provide one.
file
Extracta.ai input.1.pdf
↓
file
Output artifact for "Education Extraction and Normalization" test: CGPA 8.2 was embedded in the description field instead of being extracted into a standalone numeric field., image-4.png
CGPA 8.2 was embedded in the description field instead of being extracted into a standalone numeric field.
pdf
Extracta.ai Input.2.pdf
↓
image
Output artifact for "Education Extraction and Normalization" test: CGPA 8.7 also stayed buried in the description string., image-7.png
CGPA 8.7 also stayed buried in the description string.
file
Extracta.ai Input.2.pdf
↓
file
Output artifact for "Education Extraction and Normalization" test: The education entry left both start_date and location empty placeholders because neither value was present in the resume section., image-8.png
The education entry left both start_date and location empty placeholders because neither value was present in the resume section.
pdf
Extracta.ai input.3.pdf
↓
image
Output artifact for "Education Extraction and Normalization" test: 72 percent marks stayed as raw text and location was missing in the education entry., image-10.png
72 percent marks stayed as raw text and location was missing in the education entry.
Bottom Line
Good at finding education entries, but normalization is inconsistent and empty placeholders need downstream handling.
From our researchParse resumes into structured data using an API
Skills, Languages, and Certifications Extraction
▾
Test Summary
Feature tested: Skills, Languages, and Certifications Extraction
Result: Partial

Feature tested: Skills, Languages, and Certifications Extraction

Result: Partial

Expected behavior: Extracta.ai itemizes skills into arrays, preserves certification text, and extracts language values when the resume has a clear languages section. The member cards show this across clean, two-column/sidebar, and messy resumes.

Test case: PDF document → Image

Input type: PDF document

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

Observed output: Output artifact (Image): Skills were returned as individual array items from the multi-column resume. — Input 2 Skills were returned as individual array .png .png

Input artifact: Input artifact (PDF document): Input — Extracta.ai Input.2.pdf

Output artifact: Output artifact (Image): Skills were returned as individual array items from the multi-column resume. — Input 2 Skills were returned as individual array .png .png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Extracta.ai input.1.pdf — Extracta.ai input.1.pdf

Observed output: Output artifact (Image): Programming languages from the Skills section were returned under the Languages field, showing a wrong section mapping because the resume did not have a dedicated spoken-languages section. — image-3.png

Input artifact: Input artifact (PDF document): Extracta.ai input.1.pdf — Extracta.ai input.1.pdf

Output artifact: Output artifact (Image): Programming languages from the Skills section were returned under the Languages field, showing a wrong section mapping because the resume did not have a dedicated spoken-languages 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): Extracta.ai input.3.pdf — Extracta.ai input.3.pdf

Observed output: Output artifact (Image): The messy resume produced a blank Languages item, confirming the field can come back empty when no languages section exists. — image-9.png

Input artifact: Input artifact (PDF document): Extracta.ai input.3.pdf — Extracta.ai input.3.pdf

Output artifact: Output artifact (Image): The messy resume produced a blank Languages item, confirming the field can come back empty when no languages section exists. — 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): Extracta.ai input.3.pdf — Extracta.ai input.3.pdf

Observed output: Output artifact (Image): The messy resume's certifications were captured but kept lowercase exactly as written, with no capitalization normalization. — image-11.png

Input artifact: Input artifact (PDF document): Extracta.ai input.3.pdf — Extracta.ai input.3.pdf

Output artifact: Output artifact (Image): The messy resume's certifications were captured but kept lowercase exactly as written, with no capitalization normalization. — 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): Input — Extracta.ai input.1.pdf

Observed output: Output artifact (Image): Skills were returned as individual array items from the clean resume. — Input 1 Skills returned as individual array .png

Input artifact: Input artifact (PDF document): Input — Extracta.ai input.1.pdf

Output artifact: Output artifact (Image): Skills were returned as individual array items from the clean resume. — Input 1 Skills returned as individual array .png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Input — Extracta.ai input.3.pdf

Observed output: Output artifact (Image): The messy resume returned 14 individual skill strings, including soft skills. — Input 3 messy resume returned 14 individual skill strings including soft skills.png

Input artifact: Input artifact (PDF document): Input — Extracta.ai input.3.pdf

Output artifact: Output artifact (Image): The messy resume returned 14 individual skill strings, including soft skills. — Input 3 messy resume returned 14 individual skill strings including soft skills.png

What changed: PDF document transformed into Image

Why it matters / Conclusion: Works well when a dedicated languages section exists, but it can misfire or go blank without one, and certification text may need capitalization cleanup.

Extracta.ai itemizes skills into arrays, preserves certification text, and extracts language values when the resume has a clear languages section. The member cards show this across clean, two-column/sidebar, and messy resumes.

pdf
Extracta.ai Input.2.pdf
↓
text
Output artifact for "Skills, Languages, and Certifications Extraction" test: Skills were returned as individual array items from the multi-column resume., Input 2 Skills were returned as individual array .png .png
Skills were returned as individual array items from the multi-column resume.
file
Extracta.ai input.1.pdf
↓
file
Output artifact for "Skills, Languages, and Certifications Extraction" test: Programming languages from the Skills section were returned under the Languages field, showing a wrong section mapping because the resume did not have a dedicated spoken-languages section., image-3.png
Programming languages from the Skills section were returned under the Languages field, showing a wrong section mapping because the resume did not have a dedicated spoken-languages section.
file
Extracta.ai input.3.pdf
↓
file
Output artifact for "Skills, Languages, and Certifications Extraction" test: The messy resume produced a blank Languages item, confirming the field can come back empty when no languages section exists., image-9.png
The messy resume produced a blank Languages item, confirming the field can come back empty when no languages section exists.
file
Extracta.ai input.3.pdf
↓
file
Output artifact for "Skills, Languages, and Certifications Extraction" test: The messy resume's certifications were captured but kept lowercase exactly as written, with no capitalization normalization., image-11.png
The messy resume's certifications were captured but kept lowercase exactly as written, with no capitalization normalization.
pdf
Extracta.ai input.1.pdf
↓
text
Output artifact for "Skills, Languages, and Certifications Extraction" test: Skills were returned as individual array items from the clean resume., Input 1 Skills returned as individual array .png
Skills were returned as individual array items from the clean resume.
pdf
Extracta.ai input.3.pdf
↓
text
Output artifact for "Skills, Languages, and Certifications Extraction" test: The messy resume returned 14 individual skill strings, including soft skills., Input 3 messy resume returned 14 individual skill strings including soft skills.png
The messy resume returned 14 individual skill strings, including soft skills.
Bottom Line
Works well when a dedicated languages section exists, but it can misfire or go blank without one, and certification text may need capitalization cleanup.
From our researchParse resumes into structured data using an API
Contact and Work History Extraction
▾
Test Summary
Feature tested: Contact and Work History Extraction
Result: Passed

Feature tested: Contact and Work History Extraction

Result: Passed

Expected behavior: Extracta.ai extracts identity and employment-history fields such as name, email, phone, address, employers, titles, dates, locations, and role descriptions. The examples also preserve quantified achievements and nonstandard date ranges.

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Input — Extracta.ai input.1.pdf

Observed output: Output artifact (Image): On the clean resume, the extracted contact block contained name, email, phone, and address, but LinkedIn was missing because it was not defined in the schema. — image.png

Input artifact: Input artifact (PDF document): Input — Extracta.ai input.1.pdf

Output artifact: Output artifact (Image): On the clean resume, the extracted contact block contained name, email, phone, and address, but LinkedIn was missing because it was not defined in the schema. — 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 — Extracta.ai Input.2.pdf

Observed output: Output artifact (Image): The same contact behavior repeated on the multi-column resume: name, email, phone, and address were extracted, and LinkedIn stayed absent for the same schema reason. — image-6.png

Input artifact: Input artifact (PDF document): Input — Extracta.ai Input.2.pdf

Output artifact: Output artifact (Image): The same contact behavior repeated on the multi-column resume: name, email, phone, and address were extracted, and LinkedIn stayed absent for the same schema reason. — image-6.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Input — Extracta.ai input.3.pdf

Observed output: Output artifact (Image): The messy resume still yielded the candidate contact details and work history successfully. — contact_work_history.png

Input artifact: Input artifact (PDF document): Input — Extracta.ai input.3.pdf

Output artifact: Output artifact (Image): The messy resume still yielded the candidate contact details and work history successfully. — contact_work_history.png

What changed: PDF document transformed into Image

Why it matters / Conclusion: Reliable for core candidate data, with empty work-location fields when the source leaves them out.

Extracta.ai extracts identity and employment-history fields such as name, email, phone, address, employers, titles, dates, locations, and role descriptions. The examples also preserve quantified achievements and nonstandard date ranges.

pdf
Extracta.ai input.1.pdf
↓
image
Output artifact for "Contact and Work History Extraction" test: On the clean resume, the extracted contact block contained name, email, phone, and address, but LinkedIn was missing because it was not defined in the schema., image.png
On the clean resume, the extracted contact block contained name, email, phone, and address, but LinkedIn was missing because it was not defined in the schema.
pdf
Extracta.ai Input.2.pdf
↓
image
Output artifact for "Contact and Work History Extraction" test: The same contact behavior repeated on the multi-column resume: name, email, phone, and address were extracted, and LinkedIn stayed absent for the same schema reason., image-6.png
The same contact behavior repeated on the multi-column resume: name, email, phone, and address were extracted, and LinkedIn stayed absent for the same schema reason.
pdf
Extracta.ai input.3.pdf
↓
text
Output artifact for "Contact and Work History Extraction" test: The messy resume still yielded the candidate contact details and work history successfully., contact_work_history.png
The messy resume still yielded the candidate contact details and work history successfully.
Bottom Line
Reliable for core candidate data, with empty work-location fields when the source leaves them out.
From our researchParse resumes into structured data using an API

Pricing & Access

TESTED
Free
$0
50 free pages, no credit card required — sufficient for initial evaluation and testing across multiple resume inputs
Starter
$9/mo
500 pages per month, all extraction features, custom schema support, JSON export
Growth
$29/mo
2,000 pages per month, priority processing, batch uploads, API access
Business
$79/mo
10,000 pages per month, dedicated support, advanced schema options, full API integration

Pricing checked May 2026. We re-check quarterly. Visit extracta.ai for current plans and annual pricing options.

✓ Use This If
●You already know exactly which fields you need and can define the schema up front.
●You want lean JSON with no extra metadata, taxonomy IDs, or unrelated fields.
●You need resume PDFs parsed reliably across clean, two-column, and messy layouts.
●You want consistent output for downstream automation once the schema is fixed.
✕ Skip This If
●You need LinkedIn, summary, objective, hobbies, or projects inferred automatically without defining them first.
●You need CGPA or marks normalized into dedicated numeric fields.
●You want exploratory parsing where the tool should return everything it can find, even if you did not define it.
●You need language extraction to work reliably when the resume has no dedicated languages section.
developer-toolsapistextOther
Yes. The tests show that it only returns fields explicitly defined in the schema, and undefined fields such as LinkedIn, summary, objective, hobbies, and projects were skipped.
Yes. The Priya Sharma resume parsed correctly without any layout configuration, including the right-column sidebar sections.
It leaves missing items as empty placeholders. In the tests, start_date and location could be empty when the resume did not include those values.
Not consistently. CGPA was embedded inside a description string, and '72 percent marks' stayed as raw text instead of becoming a normalized percentage like '72%.'
Skills were returned as arrays and certifications as readable strings. Languages worked when a dedicated languages section existed, but the parser could also mis-map programming languages into the Languages field or return it blank if no languages section was present.

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