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developer-tools

CVParserPro

Parses resume PDFs into structured candidate profiles quickly, but experience totals and education dates need manual review.

PDF upload3 resume testsCSV exportDate hallucinations
TL;DR — our verdictUpdated August 2026 · 10 test artifacts

Fast parser, but not automation-safe yet

Where it wins
  • You want a no-setup parser that accepts uploaded resume PDFs and returns readable candidate profiles quickly.
  • You need strong extraction of contact details, work history text, skills, languages, and certification names.
  • You are okay manually verifying total experience, education dates, CGPA, LinkedIn, and projects before using the data downstream.
Main limitation
  • You need trustworthy total experience or education dates with no manual review.
Pricing (verified plans)
Starter $29/monthGrowth $79/monthScale $199/monthEnterprise Custom pricing
Strongest test artifacts
Profile header extraction →Work-history extraction and date parsing →Education and credential extraction →

Our take

CVParserPro parsed all three uploaded resume PDFs without setup and produced readable candidate profiles with strong contact, work history text, skills, languages, and certification extraction. The recurring problems are the ones that matter most for downstream automation: total experience was wrong on 2 of 3 resumes, CGPA or grade data never appeared, and some education dates were inferred or mislabeled.

Download video: Demo video
Screen recording walkthrough of the app from the research report.

In-Depth Review

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

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AI Demos Team
Expert Reviewer
Verified Review

Feature-by-Feature Breakdown

Resume PDF parsing and profile assembly
Reliable intake
▾
Test Summary
Feature tested: Resume PDF parsing and profile assembly
Result: Passed — Reliable intake

Feature tested: Resume PDF parsing and profile assembly

Result: Passed

Verdict: Reliable intake

Expected behavior: Accepts resume PDFs of varying layout quality and turns them into readable candidate profiles. The tested inputs included clean single-column, multi-column, and messy resumes, and the output assembled contact info, work history, education, skills, languages, and certifications.

Test case: Image → Text prompt

Input type: Image

Input used: Input artifact (Image): INPUT — Input 1 Rugved Nichite clean PDF resume.png

Observed output: Output artifact (Text prompt): Output

Input artifact: Input artifact (Image): INPUT — Input 1 Rugved Nichite clean PDF resume.png

Output artifact: Output artifact (Text prompt): Output

What changed: Image transformed into Text prompt

Test case: Image → Text prompt

Input type: Image

Input used: Input artifact (Image): INPUT — Input 2 multi column resume for priya sharma.png

Observed output: Output artifact (Text prompt): Output

Input artifact: Input artifact (Image): INPUT — Input 2 multi column resume for priya sharma.png

Output artifact: Output artifact (Text prompt): Output

What changed: Image transformed into Text prompt

Test case: Image → Text prompt

Input type: Image

Input used: Input artifact (Image): INPUT — Input 3 Messy resume for JOHN KUMAR.png

Observed output: Output artifact (Text prompt): Output

Input artifact: Input artifact (Image): INPUT — Input 3 Messy resume for JOHN KUMAR.png

Output artifact: Output artifact (Text prompt): Output

What changed: Image transformed into Text prompt

Why it matters / Conclusion: File intake was solid across layouts, but downstream accuracy issues mean the parse still needs review.

Accepts resume PDFs of varying layout quality and turns them into readable candidate profiles. The tested inputs included clean single-column, multi-column, and messy resumes, and the output assembled contact info, work history, education, skills, languages, and certifications.

INPUT
Input artifact for "Resume PDF parsing and profile assembly" test: INPUT, Input 1 Rugved Nichite clean PDF resume.png
↓
OUTPUT
Parsed successfully on first attempt with no errors; the output was presented as a structured candidate profile with contact info, skills, languages, work experience, education, and certifications.
INPUT
Input artifact for "Resume PDF parsing and profile assembly" test: INPUT, Input 2 multi column resume for priya sharma.png
↓
OUTPUT
Accepted the PDF and parsed the multi-column layout successfully without any layout configuration.
INPUT
Input artifact for "Resume PDF parsing and profile assembly" test: INPUT, Input 3 Messy resume for JOHN KUMAR.png
↓
OUTPUT
Accepted the PDF without errors and parsed successfully despite highly inconsistent formatting.
Bottom Line
File intake was solid across layouts, but downstream accuracy issues mean the parse still needs review.
From our researchParse resumes into structured data using an APIearlier research
Profile header extraction
Mixed
▾
Test Summary
Feature tested: Profile header extraction
Result: Partial — Mixed

Feature tested: Profile header extraction

Result: Partial

Verdict: Mixed

Expected behavior: Extracts header-level identity fields from resume profiles, including name, title, email, phone, location, and total experience. The tested outputs showed strong contact-field coverage, with LinkedIn and some experience values less reliable.

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): INPUT — Input 1 Rugved Nichite clean PDF resume.png

Observed output: Output artifact (Image): Profile header extraction shows the candidate name, email, phone, location, and an experience badge reading 11 years, which the report says is inflated for this resume. — p1_11years.png

Input artifact: Input artifact (Image): INPUT — Input 1 Rugved Nichite clean PDF resume.png

Output artifact: Output artifact (Image): Profile header extraction shows the candidate name, email, phone, location, and an experience badge reading 11 years, which the report says is inflated for this resume. — p1_11years.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): INPUT — Input 1 Rugved Nichite clean PDF resume.png

Observed output: Output artifact (Image): The LinkedIn icon area is present, but no LinkedIn URL or handle text appears next to it. — p3_linkedin_missing.png

Input artifact: Input artifact (Image): INPUT — Input 1 Rugved Nichite clean PDF resume.png

Output artifact: Output artifact (Image): The LinkedIn icon area is present, but no LinkedIn URL or handle text appears next to it. — p3_linkedin_missing.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): INPUT — Input 2 multi column resume for priya sharma.png

Observed output: Output artifact (Image): The header is extracted correctly, but the experience badge shows 2 years even though the report says Priya has about 6.5 years of experience. — p4_2years.png

Input artifact: Input artifact (Image): INPUT — Input 2 multi column resume for priya sharma.png

Output artifact: Output artifact (Image): The header is extracted correctly, but the experience badge shows 2 years even though the report says Priya has about 6.5 years of experience. — p4_2years.png

What changed: Image transformed into Image

Test case: Image → Text prompt

Input type: Image

Input used: Input artifact (Image): INPUT — Input 3 Messy resume for JOHN KUMAR.png

Observed output: Output artifact (Text prompt): Output

Input artifact: Input artifact (Image): INPUT — Input 3 Messy resume for JOHN KUMAR.png

Output artifact: Output artifact (Text prompt): Output

What changed: Image transformed into Text prompt

Why it matters / Conclusion: Identity and contact fields were solid, but LinkedIn stayed empty and total experience was inflated or undercounted on 2 of 3 profiles.

Extracts header-level identity fields from resume profiles, including name, title, email, phone, location, and total experience. The tested outputs showed strong contact-field coverage, with LinkedIn and some experience values less reliable.

INPUT
Input artifact for "Profile header extraction" test: INPUT, Input 1 Rugved Nichite clean PDF resume.png
↓
OUTPUT
Output artifact for "Profile header extraction" test: Profile header extraction shows the candidate name, email, phone, location, and an experience badge reading 11 years, which the report says is inflated for this resume., p1_11years.png
Profile header extraction shows the candidate name, email, phone, location, and an experience badge reading 11 years, which the report says is inflated for this resume.
INPUT
Input artifact for "Profile header extraction" test: INPUT, Input 1 Rugved Nichite clean PDF resume.png
↓
OUTPUT
Output artifact for "Profile header extraction" test: The LinkedIn icon area is present, but no LinkedIn URL or handle text appears next to it., p3_linkedin_missing.png
The LinkedIn icon area is present, but no LinkedIn URL or handle text appears next to it.
INPUT
Input artifact for "Profile header extraction" test: INPUT, Input 2 multi column resume for priya sharma.png
↓
OUTPUT
Output artifact for "Profile header extraction" test: The header is extracted correctly, but the experience badge shows 2 years even though the report says Priya has about 6.5 years of experience., p4_2years.png
The header is extracted correctly, but the experience badge shows 2 years even though the report says Priya has about 6.5 years of experience.
INPUT
Input artifact for "Profile header extraction" test: INPUT, Input 3 Messy resume for JOHN KUMAR.png
↓
OUTPUT
The header total experience was correct at 3 years on this resume.
Bottom Line
Identity and contact fields were solid, but LinkedIn stayed empty and total experience was inflated or undercounted on 2 of 3 profiles.
From our researchParse resumes into structured data using an API
Work-history extraction and date parsing
Partially reliable
▾
Test Summary
Feature tested: Work-history extraction and date parsing
Result: Partial — Partially reliable

Feature tested: Work-history extraction and date parsing

Result: Partial

Verdict: Partially reliable

Expected behavior: Preserves work-history entries and bullet descriptions from resumes while also normalizing employment dates. The tested resumes kept role text intact, but month-level ranges and tenure math could be misread or over-inferred.

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): INPUT — Input 1 Rugved Nichite clean PDF resume.png

Observed output: Output artifact (Image): Output — Input 2 multi column resume for priya sharma.png

Input artifact: Input artifact (Image): INPUT — Input 1 Rugved Nichite clean PDF resume.png

Output artifact: Output artifact (Image): Output — Input 2 multi column resume for priya sharma.png

What changed: Image transformed into Image

Test case: Text prompt → Text prompt

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Text prompt): Output

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Text prompt): Output

What changed: Text prompt transformed into Text prompt

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): INPUT — Input 3 Messy resume for JOHN KUMAR.png

Observed output: Output artifact (Image): The work-experience date range was over-specified as 2019-01 to 2021-12 even though the source only stated years. — p7_month_hallucination.png

Input artifact: Input artifact (Image): INPUT — Input 3 Messy resume for JOHN KUMAR.png

Output artifact: Output artifact (Image): The work-experience date range was over-specified as 2019-01 to 2021-12 even though the source only stated years. — p7_month_hallucination.png

What changed: Image transformed into Image

Why it matters / Conclusion: The tool keeps work-history text intact, but it over-infers dates and cannot be trusted for tenure math without review.

Preserves work-history entries and bullet descriptions from resumes while also normalizing employment dates. The tested resumes kept role text intact, but month-level ranges and tenure math could be misread or over-inferred.

INPUT
Input artifact for "Work-history extraction and date parsing" test: INPUT, Input 1 Rugved Nichite clean PDF resume.png
↓
OUTPUT
Output artifact for "Work-history extraction and date parsing" test: Output, Input 2 multi column resume for priya sharma.png
INPUT
Input 2 - Multi-Column Resume (Priya Sharma): work history with Software Engineer - ML at TechCorp India Pvt. Ltd. Pune from June 2021 to present and Junior Data Analyst at DataBridge Solutions Mumbai from August 2019 to May 2021.
↓
OUTPUT
Both work experiences were extracted fully with responsibilities and quantified metrics, but the profile header showed 2 years instead of the resume's actual roughly 6.5 years of experience.
INPUT
Input artifact for "Work-history extraction and date parsing" test: INPUT, Input 3 Messy resume for JOHN KUMAR.png
↓
OUTPUT
Output artifact for "Work-history extraction and date parsing" test: The work-experience date range was over-specified as 2019-01 to 2021-12 even though the source only stated years., p7_month_hallucination.png
The work-experience date range was over-specified as 2019-01 to 2021-12 even though the source only stated years.
Bottom Line
The tool keeps work-history text intact, but it over-infers dates and cannot be trusted for tenure math without review.
From our researchearlier researchParse resumes into structured data using an API
Education and credential extraction
Weak
▾
Test Summary
Feature tested: Education and credential extraction
Result: Failed — Weak

Feature tested: Education and credential extraction

Result: Failed

Verdict: Weak

Expected behavior: Extracts degrees, institutions, certifications, years, and related education details from resume content. Across the tested resumes, degree and university names were readable, while issuer, grade, and some date details could be missing or distorted.

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): INPUT — Input 1 Rugved Nichite clean PDF resume.png

Observed output: Output artifact (Image): The degree and university are visible, but no CGPA or grade value is shown. — p2_cgpa_missing.png

Input artifact: Input artifact (Image): INPUT — Input 1 Rugved Nichite clean PDF resume.png

Output artifact: Output artifact (Image): The degree and university are visible, but no CGPA or grade value is shown. — p2_cgpa_missing.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): INPUT — Input 2 multi column resume for priya sharma.png

Observed output: Output artifact (Image): The education date range is shown as 2015 - 2019, which the report says was inferred from the graduation year. — p5_edu_hallucination.png

Input artifact: Input artifact (Image): INPUT — Input 2 multi column resume for priya sharma.png

Output artifact: Output artifact (Image): The education date range is shown as 2015 - 2019, which the report says was inferred from the graduation year. — p5_edu_hallucination.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): INPUT — Input 3 Messy resume for JOHN KUMAR.png

Observed output: Output artifact (Image): Only one degree-level education record is returned; the 10th and 12th standard entries are missing. — p9_only_one_education.png

Input artifact: Input artifact (Image): INPUT — Input 3 Messy resume for JOHN KUMAR.png

Output artifact: Output artifact (Image): Only one degree-level education record is returned; the 10th and 12th standard entries are missing. — p9_only_one_education.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): INPUT — Input 3 Messy resume for JOHN KUMAR.png

Observed output: Output artifact (Image): The completed degree is labeled 2019 - Present, which is incorrect for a graduation-year-only record. — p8_edu_present_wrong.png

Input artifact: Input artifact (Image): INPUT — Input 3 Messy resume for JOHN KUMAR.png

Output artifact: Output artifact (Image): The completed degree is labeled 2019 - Present, which is incorrect for a graduation-year-only record. — p8_edu_present_wrong.png

What changed: Image transformed into Image

Test case: Image → Text prompt

Input type: Image

Input used: Input artifact (Image): INPUT — Input 1 Rugved Nichite clean PDF resume.png

Observed output: Output artifact (Text prompt): Output

Input artifact: Input artifact (Image): INPUT — Input 1 Rugved Nichite clean PDF resume.png

Output artifact: Output artifact (Text prompt): Output

What changed: Image transformed into Text prompt

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): INPUT — Input 2 multi column resume for priya sharma.png

Observed output: Output artifact (Image): Certification titles and years are present, but the issuing organizations are missing. — p6_cert_orgs_missing.png

Input artifact: Input artifact (Image): INPUT — Input 2 multi column resume for priya sharma.png

Output artifact: Output artifact (Image): Certification titles and years are present, but the issuing organizations are missing. — p6_cert_orgs_missing.png

What changed: Image transformed into Image

Test case: Image → Text prompt

Input type: Image

Input used: Input artifact (Image): INPUT — Input 3 Messy resume for JOHN KUMAR.png

Observed output: Output artifact (Text prompt): Output

Input artifact: Input artifact (Image): INPUT — Input 3 Messy resume for JOHN KUMAR.png

Output artifact: Output artifact (Text prompt): Output

What changed: Image transformed into Text prompt

Why it matters / Conclusion: Degree and university extraction is usable, but grades are missing and education dates are often inferred or mislabeled.

Extracts degrees, institutions, certifications, years, and related education details from resume content. Across the tested resumes, degree and university names were readable, while issuer, grade, and some date details could be missing or distorted.

INPUT
Input artifact for "Education and credential extraction" test: INPUT, Input 1 Rugved Nichite clean PDF resume.png
↓
OUTPUT
Output artifact for "Education and credential extraction" test: The degree and university are visible, but no CGPA or grade value is shown., p2_cgpa_missing.png
The degree and university are visible, but no CGPA or grade value is shown.
INPUT
Input artifact for "Education and credential extraction" test: INPUT, Input 2 multi column resume for priya sharma.png
↓
OUTPUT
Output artifact for "Education and credential extraction" test: The education date range is shown as 2015 - 2019, which the report says was inferred from the graduation year., p5_edu_hallucination.png
The education date range is shown as 2015 - 2019, which the report says was inferred from the graduation year.
INPUT
Input artifact for "Education and credential extraction" test: INPUT, Input 3 Messy resume for JOHN KUMAR.png
↓
OUTPUT
Output artifact for "Education and credential extraction" test: Only one degree-level education record is returned; the 10th and 12th standard entries are missing., p9_only_one_education.png
Only one degree-level education record is returned; the 10th and 12th standard entries are missing.
INPUT
Input artifact for "Education and credential extraction" test: INPUT, Input 3 Messy resume for JOHN KUMAR.png
↓
OUTPUT
Output artifact for "Education and credential extraction" test: The completed degree is labeled 2019 - Present, which is incorrect for a graduation-year-only record., p8_edu_present_wrong.png
The completed degree is labeled 2019 - Present, which is incorrect for a graduation-year-only record.
INPUT
Input artifact for "Education and credential extraction" test: INPUT, Input 1 Rugved Nichite clean PDF resume.png
↓
OUTPUT
Both certifications were extracted with issuing organization names.
INPUT
Input artifact for "Education and credential extraction" test: INPUT, Input 2 multi column resume for priya sharma.png
↓
OUTPUT
Output artifact for "Education and credential extraction" test: Certification titles and years are present, but the issuing organizations are missing., p6_cert_orgs_missing.png
Certification titles and years are present, but the issuing organizations are missing.
INPUT
Input artifact for "Education and credential extraction" test: INPUT, Input 3 Messy resume for JOHN KUMAR.png
↓
OUTPUT
Both certifications were extracted including provider names.
Bottom Line
Degree and university extraction is usable, but grades are missing and education dates are often inferred or mislabeled.
From our researchParse resumes into structured data using an APIearlier research
Skills and language extraction
Strong
▾
Test Summary
Feature tested: Skills and language extraction
Result: Passed — Strong

Feature tested: Skills and language extraction

Result: Passed

Verdict: Strong

Expected behavior: Pulls skill tags and languages from resume content, including dense technical stacks, platform skills, and soft skills. In testing, this section stayed consistent across all three resumes.

Test case: Image → Text prompt

Input type: Image

Input used: Input artifact (Image): INPUT — Input 1 Rugved Nichite clean PDF resume.png

Observed output: Output artifact (Text prompt): Output

Input artifact: Input artifact (Image): INPUT — Input 1 Rugved Nichite clean PDF resume.png

Output artifact: Output artifact (Text prompt): Output

What changed: Image transformed into Text prompt

Test case: Image → Text prompt

Input type: Image

Input used: Input artifact (Image): INPUT — Input 2 multi column resume for priya sharma.png

Observed output: Output artifact (Text prompt): Output

Input artifact: Input artifact (Image): INPUT — Input 2 multi column resume for priya sharma.png

Output artifact: Output artifact (Text prompt): Output

What changed: Image transformed into Text prompt

Test case: Image → Text prompt

Input type: Image

Input used: Input artifact (Image): INPUT — Input 3 Messy resume for JOHN KUMAR.png

Observed output: Output artifact (Text prompt): Output

Input artifact: Input artifact (Image): INPUT — Input 3 Messy resume for JOHN KUMAR.png

Output artifact: Output artifact (Text prompt): Output

What changed: Image transformed into Text prompt

Why it matters / Conclusion: This was the cleanest section in the report and stayed consistent across all three resumes.

Pulls skill tags and languages from resume content, including dense technical stacks, platform skills, and soft skills. In testing, this section stayed consistent across all three resumes.

INPUT
Input artifact for "Skills and language extraction" test: INPUT, Input 1 Rugved Nichite clean PDF resume.png
↓
OUTPUT
All 19 skills were extracted correctly as individual tags, and English was detected as the language.
INPUT
Input artifact for "Skills and language extraction" test: INPUT, Input 2 multi column resume for priya sharma.png
↓
OUTPUT
All 12 skills were extracted correctly as individual tags, and the three languages English, Hindi, and Marathi were extracted.
INPUT
Input artifact for "Skills and language extraction" test: INPUT, Input 3 Messy resume for JOHN KUMAR.png
↓
OUTPUT
All 14 skills were extracted correctly, including the soft skills listed in the source.
Bottom Line
This was the cleanest section in the report and stayed consistent across all three resumes.
From our researchParse resumes into structured data using an API
Candidate summary generation
Useful
▾
Test Summary
Feature tested: Candidate summary generation
Result: Partial — Useful

Feature tested: Candidate summary generation

Result: Partial

Verdict: Useful

Expected behavior: Generates an AI-written candidate summary from the parsed resume or candidate profile. The tested summaries tracked the candidate background well, but they are generated enrichment rather than raw source fields.

Test case: Image → Text prompt

Input type: Image

Input used: Input artifact (Image): INPUT — Input 1 Rugved Nichite clean PDF resume.png

Observed output: Output artifact (Text prompt): Output

Input artifact: Input artifact (Image): INPUT — Input 1 Rugved Nichite clean PDF resume.png

Output artifact: Output artifact (Text prompt): Output

What changed: Image transformed into Text prompt

Test case: Image → Text prompt

Input type: Image

Input used: Input artifact (Image): INPUT — Input 2 multi column resume for priya sharma.png

Observed output: Output artifact (Text prompt): Output

Input artifact: Input artifact (Image): INPUT — Input 2 multi column resume for priya sharma.png

Output artifact: Output artifact (Text prompt): Output

What changed: Image transformed into Text prompt

Test case: Image → Text prompt

Input type: Image

Input used: Input artifact (Image): INPUT — Input 3 Messy resume for JOHN KUMAR.png

Observed output: Output artifact (Text prompt): Output

Input artifact: Input artifact (Image): INPUT — Input 3 Messy resume for JOHN KUMAR.png

Output artifact: Output artifact (Text prompt): Output

What changed: Image transformed into Text prompt

Why it matters / Conclusion: Good for quick context, but treat it as generated enrichment rather than source truth.

Generates an AI-written candidate summary from the parsed resume or candidate profile. The tested summaries tracked the candidate background well, but they are generated enrichment rather than raw source fields.

INPUT
Input artifact for "Candidate summary generation" test: INPUT, Input 1 Rugved Nichite clean PDF resume.png
↓
OUTPUT
The AI summary correctly summarized the candidate as an AI Research Analyst with Python and REST API proficiency.
INPUT
Input artifact for "Candidate summary generation" test: INPUT, Input 2 multi column resume for priya sharma.png
↓
OUTPUT
The AI summary correctly identified the machine-learning background and mentioned Python, TensorFlow, PyTorch, and AWS.
INPUT
Input artifact for "Candidate summary generation" test: INPUT, Input 3 Messy resume for JOHN KUMAR.png
↓
OUTPUT
The AI summary correctly described the software-development background and included the web, database, and teamwork context from the resume.
Bottom Line
Good for quick context, but treat it as generated enrichment rather than source truth.
From our researchParse resumes into structured data using an APIearlier research
Profile export to CSV
Limited
▾
Test Summary
Feature tested: Profile export to CSV
Result: Partial — Limited

Feature tested: Profile export to CSV

Result: Partial

Verdict: Limited

Expected behavior: Exports profiles through a downloadable CSV path using a fixed schema. The tested workflow did not expose direct JSON download or custom field mapping.

Test case: Image → Text prompt

Input type: Image

Input used: Input artifact (Image): INPUT — Input 1 Rugved Nichite clean PDF resume.png

Observed output: Output artifact (Text prompt): Output

Input artifact: Input artifact (Image): INPUT — Input 1 Rugved Nichite clean PDF resume.png

Output artifact: Output artifact (Text prompt): Output

What changed: Image transformed into Text prompt

Test case: Image → Text prompt

Input type: Image

Input used: Input artifact (Image): INPUT — Input 2 multi column resume for priya sharma.png

Observed output: Output artifact (Text prompt): Output

Input artifact: Input artifact (Image): INPUT — Input 2 multi column resume for priya sharma.png

Output artifact: Output artifact (Text prompt): Output

What changed: Image transformed into Text prompt

Why it matters / Conclusion: Useful for review and sharing, but not a strict JSON API workflow.

Exports profiles through a downloadable CSV path using a fixed schema. The tested workflow did not expose direct JSON download or custom field mapping.

INPUT
Input artifact for "Profile export to CSV" test: INPUT, Input 1 Rugved Nichite clean PDF resume.png
↓
OUTPUT
CSV export was available via download link, and there was no direct JSON download from the UI.
INPUT
Input artifact for "Profile export to CSV" test: INPUT, Input 2 multi column resume for priya sharma.png
↓
OUTPUT
The export path remained CSV-based, and the schema stayed fixed rather than user-configurable.
Bottom Line
Useful for review and sharing, but not a strict JSON API workflow.
From our researchParse resumes into structured data using an APIearlier research

Pricing & Access

Plans reported in August 2026; verify independently.

Starter
$29/month
500 resume parses ($0.06/parse), REST API, JSON & CSV export, PDF/DOCX/image support, email support
Growth
$79/month
2,000 parses ($0.04/parse), bulk upload (1000+ files), priority email support
Scale
$199/month
10,000 parses ($0.02/parse), priority support, audit logging
Enterprise
Custom pricing
Custom volume, dedicated support, custom contract terms

The report flagged the pricing page as likely fake/templated, with boilerplate comparison copy and no verifiable company/team info.

✓ Use This If
●You want a no-setup parser that accepts uploaded resume PDFs and returns readable candidate profiles quickly.
●You need strong extraction of contact details, work history text, skills, languages, and certification names.
●You are okay manually verifying total experience, education dates, CGPA, LinkedIn, and projects before using the data downstream.
✕ Skip This If
●You need trustworthy total experience or education dates with no manual review.
●You need CGPA/percentage, LinkedIn URLs, projects, or custom fields in the parsed output.
●You need direct JSON download from the tested UI instead of CSV export.
developer-toolsapisotherOther
Yes. The report says it parsed all three PDFs without setup or errors, including a clean single-column resume, a multi-column resume, and a messy resume with inconsistent formatting.
Contact info, work-history text, skills, languages, and certification names were the strongest sections in the report.
Total experience was wrong on 2 of 3 profiles, CGPA or percentage never appeared, LinkedIn URLs stayed blank, projects were not extracted, and education dates were sometimes inferred.
Yes. The report shows a 2015-2019 education range inferred from a 2019 graduation year, a completed degree labeled 2019-Present, and month values added to a year-only work history.
No direct JSON download was seen in the UI. CSV export was available, and the schema was fixed. The pricing page also advertises JSON & CSV export, but the report flagged that page as unverified.
No. The report says the summary is AI-generated; it was useful, but the original summary text was not preserved verbatim.

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