Parseur icon
developer-tools

Parseur

Template-driven resume parsing that returns predictable JSON after one-time setup.

Visit Parseur
Template-drivenPDF resumesJSON/CSV/ExcelFlat fields
TL;DR — our verdictUpdated August 2026 · 17 test artifacts

Good fit once the template is defined

Where it wins
  • You can configure a mailbox and template before parsing resumes at scale.
  • You want predictable field names and control over the returned schema.
  • You need JSON, CSV, or Excel export after parsing.
Main limitation
  • You need automatic field discovery with no template design.
Pricing (verified plans)
Free FreeBase Volume-basedScale Volume-basedEnterprise Custom quote
Strongest test artifacts
Mailbox-Based Resume Parsing →Schema-Guided Resume Parsing →Multi-Format Export →

Our take

Parseur handled clean, multi-column, and messy PDF resumes well after a one-time mailbox/template setup, and it exported data in JSON, CSV, and Excel. The recurring tradeoff was schema-gated output: CGPA, languages, work location, and list-like fields such as skills and certifications were either omitted, flattened, or embedded rather than normalized.

Download video: Demo videoWatch video (streaming): Demo video
Screen recording walkthrough of Parseur's resume parsing workflow.

In-Depth Review

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

AD
AI Demos Team
Expert Reviewer
Verified Review

Feature-by-Feature Breakdown

Mailbox-Based Resume Parsing
Works after setup.
▾
Test Summary
Feature tested: Mailbox-Based Resume Parsing
Result: Passed — Works after setup.

Feature tested: Mailbox-Based Resume Parsing

Result: Passed

Verdict: Works after setup.

Expected behavior: Parseur accepts PDF resumes through a mailbox/template workflow, and after setup it can automatically process incoming PDFs while extracting core profile, role, contact, work-history, and education fields from clean, multi-column, and messy resumes.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Template setup screen shows the manual click-and-drag capture step and an empty Fields panel before the first parse. — parseur_task1_template-setup.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Template setup screen shows the manual click-and-drag capture step and an empty Fields panel before the first parse. — parseur_task1_template-setup.png

What changed: Text prompt transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Input — Input2_Priya_Sharma_MultiColumnResume.pdf

Observed output: Output artifact (Image): The multi-column PDF was accepted and marked Processed (AI) after the template was already configured. — image-3.png

Input artifact: Input artifact (PDF document): Input — Input2_Priya_Sharma_MultiColumnResume.pdf

Output artifact: Output artifact (Image): The multi-column PDF was accepted and marked Processed (AI) after the template was already configured. — 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 — Input3_John_Kumar_MessyResume.pdf

Observed output: Output artifact (Image): The messy PDF was also accepted and processed AI, showing that inconsistent formatting did not block ingestion. — image-4.png

Input artifact: Input artifact (PDF document): Input — Input3_John_Kumar_MessyResume.pdf

Output artifact: Output artifact (Image): The messy PDF was also accepted and processed AI, showing that inconsistent formatting did not block ingestion. — 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 — Input1_Rugved_Nichite_CleanResume.pdf

Observed output: Output artifact (Image): The clean resume output includes current_role, contact_location, phone_number, email_address, and linkedin_profile. — image-7.png

Input artifact: Input artifact (PDF document): Input — Input1_Rugved_Nichite_CleanResume.pdf

Output artifact: Output artifact (Image): The clean resume output includes current_role, contact_location, phone_number, email_address, and linkedin_profile. — image-7.png

What changed: PDF document transformed into Image

Why it matters / Conclusion: Parseur is not zero-setup, but once the mailbox/template exists it reliably ingests varied PDF resumes.

Parseur accepts PDF resumes through a mailbox/template workflow, and after setup it can automatically process incoming PDFs while extracting core profile, role, contact, work-history, and education fields from clean, multi-column, and messy resumes.

INPUT
One-time mailbox/template setup before the first resume parse
↓
image
Output artifact for "Mailbox-Based Resume Parsing" test: Template setup screen shows the manual click-and-drag capture step and an empty Fields panel before the first parse., parseur_task1_template-setup.png
Template setup screen shows the manual click-and-drag capture step and an empty Fields panel before the first parse.
INPUT
Input2_Priya_Sharma_MultiColumnResume.pdf
↓
OUTPUT
Output artifact for "Mailbox-Based Resume Parsing" test: The multi-column PDF was accepted and marked Processed (AI) after the template was already configured., image-3.png
The multi-column PDF was accepted and marked Processed (AI) after the template was already configured.
INPUT
Input3_John_Kumar_MessyResume.pdf
↓
OUTPUT
Output artifact for "Mailbox-Based Resume Parsing" test: The messy PDF was also accepted and processed AI, showing that inconsistent formatting did not block ingestion., image-4.png
The messy PDF was also accepted and processed AI, showing that inconsistent formatting did not block ingestion.
file
Input1_Rugved_Nichite_CleanResume.pdf
↓
image
Output artifact for "Mailbox-Based Resume Parsing" test: The clean resume output includes current_role, contact_location, phone_number, email_address, and linkedin_profile., image-7.png
The clean resume output includes current_role, contact_location, phone_number, email_address, and linkedin_profile.
Bottom Line
Parseur is not zero-setup, but once the mailbox/template exists it reliably ingests varied PDF resumes.
From our researchParse resumes into structured data using an APIearlier research
Schema-Guided Resume Parsing
Strong control, but only mapped fields return.
▾
Test Summary
Feature tested: Schema-Guided Resume Parsing
Result: Partial — Strong control, but only mapped fields return.

Feature tested: Schema-Guided Resume Parsing

Result: Partial

Verdict: Strong control, but only mapped fields return.

Expected behavior: Parseur parses PDF resumes against a predefined template/schema, letting you define field names and instructions and shape multi-value sections while extracting structured candidate data from varied resume layouts.

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Input — Input1_Rugved_Nichite_CleanResume.pdf

Observed output: Output artifact (Image): The LinkedIn URL is stored under the underscore-separated key linkedin_profile, showing the template's field-naming convention. — pa3_linkedin_naming.png

Input artifact: Input artifact (PDF document): Input — Input1_Rugved_Nichite_CleanResume.pdf

Output artifact: Output artifact (Image): The LinkedIn URL is stored under the underscore-separated key linkedin_profile, showing the template's field-naming convention. — pa3_linkedin_naming.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): INPUT — Input2_Priya_Sharma_MultiColumnResume.pdf

Observed output: Output artifact (Image): CGPA is visible in the source resume, but the template field list does not include a CGPA field. — parseur_task2_cgpa-not-in-template.png

Input artifact: Input artifact (PDF document): INPUT — Input2_Priya_Sharma_MultiColumnResume.pdf

Output artifact: Output artifact (Image): CGPA is visible in the source resume, but the template field list does not include a CGPA field. — parseur_task2_cgpa-not-in-template.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): INPUT — Input2_Priya_Sharma_MultiColumnResume.pdf

Observed output: Output artifact (Image): Languages are visible in the source resume, but the template field list does not include a languages field. — parseur_task2_languages-not-in-template.png

Input artifact: Input artifact (PDF document): INPUT — Input2_Priya_Sharma_MultiColumnResume.pdf

Output artifact: Output artifact (Image): Languages are visible in the source resume, but the template field list does not include a languages field. — parseur_task2_languages-not-in-template.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): INPUT — Input2_Priya_Sharma_MultiColumnResume.pdf

Observed output: Output artifact (Image): Work-experience locations are visible in the source, but the extracted structure only includes job title, company, dates, and description. — parseur_task2_work-location-missing.png

Input artifact: Input artifact (PDF document): INPUT — Input2_Priya_Sharma_MultiColumnResume.pdf

Output artifact: Output artifact (Image): Work-experience locations are visible in the source, but the extracted structure only includes job title, company, dates, and description. — parseur_task2_work-location-missing.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Input — Input1_Rugved_Nichite_CleanResume.pdf

Observed output: Output artifact (Image): Certifications are returned as one flattened comma-separated string instead of separate structured entries. — pa1_certs_flat_string.png

Input artifact: Input artifact (PDF document): Input — Input1_Rugved_Nichite_CleanResume.pdf

Output artifact: Output artifact (Image): Certifications are returned as one flattened comma-separated string instead of separate structured entries. — pa1_certs_flat_string.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Input — Input1_Rugved_Nichite_CleanResume.pdf

Observed output: Output artifact (Image): CGPA is embedded inside the education string instead of appearing as its own field. — pa2_cgpa_embedded.png

Input artifact: Input artifact (PDF document): Input — Input1_Rugved_Nichite_CleanResume.pdf

Output artifact: Output artifact (Image): CGPA is embedded inside the education string instead of appearing as its own field. — pa2_cgpa_embedded.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Input — Input3_John_Kumar_MessyResume.pdf

Observed output: Output artifact (Image): Skills are returned as one unstructured space-separated string, mixing technical and soft skills without delimiters suitable for arrays. — parseur_task3_skills-flat-mock.png

Input artifact: Input artifact (PDF document): Input — Input3_John_Kumar_MessyResume.pdf

Output artifact: Output artifact (Image): Skills are returned as one unstructured space-separated string, mixing technical and soft skills without delimiters suitable for arrays. — parseur_task3_skills-flat-mock.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): INPUT — Input3_John_Kumar_MessyResume.pdf

Observed output: Output artifact (Image): Only one education record is captured, and earlier school entries are omitted. — parseur_task3_only-one-education-entry-mock.png

Input artifact: Input artifact (PDF document): INPUT — Input3_John_Kumar_MessyResume.pdf

Output artifact: Output artifact (Image): Only one education record is captured, and earlier school entries are omitted. — parseur_task3_only-one-education-entry-mock.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Input — Input3_John_Kumar_MessyResume.pdf

Observed output: Output artifact (Image): The CGPA-related field shows 67, which the note identifies as a percentage degree score rather than a real CGPA. — parseur_task3_cgpa-67-mock.png

Input artifact: Input artifact (PDF document): Input — Input3_John_Kumar_MessyResume.pdf

Output artifact: Output artifact (Image): The CGPA-related field shows 67, which the note identifies as a percentage degree score rather than a real CGPA. — parseur_task3_cgpa-67-mock.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Input — Input2_Priya_Sharma_MultiColumnResume.pdf

Observed output: Output artifact (Image): Multi-column resume output showing parsed core fields and work experience in a processed AI view. — image-3.png

Input artifact: Input artifact (PDF document): Input — Input2_Priya_Sharma_MultiColumnResume.pdf

Output artifact: Output artifact (Image): Multi-column resume output showing parsed core fields and work experience in a processed AI view. — 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 — Input3_John_Kumar_MessyResume.pdf

Observed output: Output artifact (Image): Messy resume output showing the parsed name, contact details, current role, and work experience despite inconsistent formatting. — image-4.png

Input artifact: Input artifact (PDF document): Input — Input3_John_Kumar_MessyResume.pdf

Output artifact: Output artifact (Image): Messy resume output showing the parsed name, contact details, current role, and work experience despite inconsistent formatting. — 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 — Input1_Rugved_Nichite_CleanResume.pdf

Observed output: Output artifact (Image): Clean resume output showing correctly extracted identity and profile fields, including current role, location, phone, email, and LinkedIn URL. — image-7.png

Input artifact: Input artifact (PDF document): Input — Input1_Rugved_Nichite_CleanResume.pdf

Output artifact: Output artifact (Image): Clean resume output showing correctly extracted identity and profile fields, including current role, location, phone, email, and LinkedIn URL. — image-7.png

What changed: PDF document transformed into Image

Test case: PDF document → Text prompt

Input type: PDF document

Input used: Input artifact (PDF document): Input — Input1_Rugved_Nichite_CleanResume.pdf

Observed output: Output artifact (Text prompt): Output

Input artifact: Input artifact (PDF document): Input — Input1_Rugved_Nichite_CleanResume.pdf

Output artifact: Output artifact (Text prompt): Output

What changed: PDF document transformed into Text prompt

Test case: PDF document → Text prompt

Input type: PDF document

Input used: Input artifact (PDF document): Input — Input1_Rugved_Nichite_CleanResume.pdf

Observed output: Output artifact (Text prompt): Output

Input artifact: Input artifact (PDF document): Input — Input1_Rugved_Nichite_CleanResume.pdf

Output artifact: Output artifact (Text prompt): Output

What changed: PDF document transformed into Text prompt

Why it matters / Conclusion: Parseur gives precise control over schema and naming, but anything not mapped in the template stays out of the output.

Parseur parses PDF resumes against a predefined template/schema, letting you define field names and instructions and shape multi-value sections while extracting structured candidate data from varied resume layouts.

file
Input1_Rugved_Nichite_CleanResume.pdf
↓
image
Output artifact for "Schema-Guided Resume Parsing" test: The LinkedIn URL is stored under the underscore-separated key linkedin_profile, showing the template's field-naming convention., pa3_linkedin_naming.png
The LinkedIn URL is stored under the underscore-separated key linkedin_profile, showing the template's field-naming convention.
INPUT
Input2_Priya_Sharma_MultiColumnResume.pdf
↓
OUTPUT
Output artifact for "Schema-Guided Resume Parsing" test: CGPA is visible in the source resume, but the template field list does not include a CGPA field., parseur_task2_cgpa-not-in-template.png
CGPA is visible in the source resume, but the template field list does not include a CGPA field.
INPUT
Input2_Priya_Sharma_MultiColumnResume.pdf
↓
OUTPUT
Output artifact for "Schema-Guided Resume Parsing" test: Languages are visible in the source resume, but the template field list does not include a languages field., parseur_task2_languages-not-in-template.png
Languages are visible in the source resume, but the template field list does not include a languages field.
INPUT
Input2_Priya_Sharma_MultiColumnResume.pdf
↓
OUTPUT
Output artifact for "Schema-Guided Resume Parsing" test: Work-experience locations are visible in the source, but the extracted structure only includes job title, company, dates, and description., parseur_task2_work-location-missing.png
Work-experience locations are visible in the source, but the extracted structure only includes job title, company, dates, and description.
file
Input1_Rugved_Nichite_CleanResume.pdf
↓
image
Output artifact for "Schema-Guided Resume Parsing" test: Certifications are returned as one flattened comma-separated string instead of separate structured entries., pa1_certs_flat_string.png
Certifications are returned as one flattened comma-separated string instead of separate structured entries.
file
Input1_Rugved_Nichite_CleanResume.pdf
↓
image
Output artifact for "Schema-Guided Resume Parsing" test: CGPA is embedded inside the education string instead of appearing as its own field., pa2_cgpa_embedded.png
CGPA is embedded inside the education string instead of appearing as its own field.
file
Input3_John_Kumar_MessyResume.pdf
↓
image
Output artifact for "Schema-Guided Resume Parsing" test: Skills are returned as one unstructured space-separated string, mixing technical and soft skills without delimiters suitable for arrays., parseur_task3_skills-flat-mock.png
Skills are returned as one unstructured space-separated string, mixing technical and soft skills without delimiters suitable for arrays.
INPUT
Input3_John_Kumar_MessyResume.pdf
↓
OUTPUT
Output artifact for "Schema-Guided Resume Parsing" test: Only one education record is captured, and earlier school entries are omitted., parseur_task3_only-one-education-entry-mock.png
Only one education record is captured, and earlier school entries are omitted.
file
Input3_John_Kumar_MessyResume.pdf
↓
image
Output artifact for "Schema-Guided Resume Parsing" test: The CGPA-related field shows 67, which the note identifies as a percentage degree score rather than a real CGPA., parseur_task3_cgpa-67-mock.png
The CGPA-related field shows 67, which the note identifies as a percentage degree score rather than a real CGPA.
INPUT
Input2_Priya_Sharma_MultiColumnResume.pdf
↓
OUTPUT
Output artifact for "Schema-Guided Resume Parsing" test: Multi-column resume output showing parsed core fields and work experience in a processed AI view., image-3.png
Multi-column resume output showing parsed core fields and work experience in a processed AI view.
INPUT
Input3_John_Kumar_MessyResume.pdf
↓
OUTPUT
Output artifact for "Schema-Guided Resume Parsing" test: Messy resume output showing the parsed name, contact details, current role, and work experience despite inconsistent formatting., image-4.png
Messy resume output showing the parsed name, contact details, current role, and work experience despite inconsistent formatting.
INPUT
Input1_Rugved_Nichite_CleanResume.pdf
↓
OUTPUT
Output artifact for "Schema-Guided Resume Parsing" test: Clean resume output showing correctly extracted identity and profile fields, including current role, location, phone, email, and LinkedIn URL., image-7.png
Clean resume output showing correctly extracted identity and profile fields, including current role, location, phone, email, and LinkedIn URL.
INPUT
Input1_Rugved_Nichite_CleanResume.pdf
↓
OUTPUT
Both work experiences were extracted with job title, company, start/end dates, and readable contribution strings on the clean resume.
INPUT
Input1_Rugved_Nichite_CleanResume.pdf
↓
OUTPUT
The skills field preserved grouped labels such as Languages, AI/ML, Cloud, Frameworks, and Databases as one string rather than an array.
Bottom Line
Parseur gives precise control over schema and naming, but anything not mapped in the template stays out of the output.
From our researchParse resumes into structured data using an APIearlier research
Multi-Format Export
Covers the common export formats.
▾
Test Summary
Feature tested: Multi-Format Export
Result: Passed — Covers the common export formats.

Feature tested: Multi-Format Export

Result: Passed

Verdict: Covers the common export formats.

Expected behavior: Parseur lets you download parsed resume data from the Fields view as Excel, CSV, or JSON.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): The Options & Download menu shows Excel File, CSV File, and JSON File under 'Download data as.' — parseur_task1_export-formats.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): The Options & Download menu shows Excel File, CSV File, and JSON File under 'Download data as.' — parseur_task1_export-formats.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Export coverage is straightforward and covers the common spreadsheet and API-friendly formats.

Parseur lets you download parsed resume data from the Fields view as Excel, CSV, or JSON.

INPUT
Parsed resume data in the Fields view
↓
image
Output artifact for "Multi-Format Export" test: The Options & Download menu shows Excel File, CSV File, and JSON File under 'Download data as.', parseur_task1_export-formats.png
The Options & Download menu shows Excel File, CSV File, and JSON File under 'Download data as.'
Bottom Line
Export coverage is straightforward and covers the common spreadsheet and API-friendly formats.
From our researchParse resumes into structured data using an APIearlier research

Plans as of August 2026

Tested on the free plan.

TESTED
Free
Free
20 pages/month, 1 user, 90-day document retention, AI + template parsing engines, unlimited mailboxes/fields
Base
Volume-based (slider pricing)
Up to 3,000 pages/month, 1 user, 1-year retention, AI + template parsing
Scale
Volume-based (lowest cost/page)
Up to 1 million pages/month, up to 100 users, unlimited retention, advanced post-processing
Enterprise
Custom quote
Up to 10 million pages/month, unlimited users, unlimited retention, purchase-order payments, custom terms

1 credit = 1 page processed. A '3 months free' promotion was mentioned on the pricing page at the time of check.

✓ Use This If
●You can configure a mailbox and template before parsing resumes at scale.
●You want predictable field names and control over the returned schema.
●You need JSON, CSV, or Excel export after parsing.
✕ Skip This If
●You need automatic field discovery with no template design.
●You need normalized arrays for skills, certifications, or education history out of the box.
●You need CGPA, languages, or work-location captured without adding fields to the template.
developer-toolsapistextOther
Yes. The research shows a one-time mailbox/template setup step before the first parse. After that, the same setup processed resumes automatically.
It accepted all three PDF resumes and extracted the core fields successfully after setup. The clean and multi-column resumes were strong overall, and the messy resume still parsed core identity and work-experience fields.
No. On the clean resume, CGPA was embedded inside the education string instead of being split into its own field. On the multi-column resume, the tested template did not include a CGPA field at all. The messy-resume mock output also showed a CGPA-related value of 67 that was identified as a percentage score, not a true CGPA.
Not in the tested setup. Certifications were returned as one flattened comma-separated string, and skills were shown as one flat space-separated string in the messy-resume output.
Not with the tested template. The Languages section and work-location details were present in the source resumes, but those fields were absent from the output because the template did not define them.
JSON, CSV, and Excel were available from the Fields view.
Yes. The research recorded a Free plan, Base, Scale, and Enterprise pricing tiers, and it noted that the test was run on the free plan in August 2026.

Banner Preview

How the embed badge will look on your site

Parseur featured on AI Demos

Embed HTML

Copy this code to your website source

<a target="_blank" href="https://aidemos.com/tools/parseur?utm_source=parseur_embed" style="width: 250px; height: 80px; border-radius:4px;" width="250" height="80"> <img src="https://aidemos-website-images.s3.amazonaws.com/featured.png" alt="Parseur | Featured on AI Demos" style="width: 250px; height: 80px; border-radius:4px;" width="250" height="80"> </a>

Quick Integration Guide

  • 1Copy the HTML code block above.
  • 2Paste it into your site's HTML or CMS editor.
  • 3Banner appears instantly on your page.
  • 4Links back to your tool profile here.
Similar Tools

Similar Tools

Discover more AI tools like Parseur to enhance your workflow.

🤖
Affinda
Affinda Review: AI Resume Parser Tested Across Resume Formats (2026)
AI Tool
🤖
Airparser
Parses clean, multi-column, and messy resumes into structured JSON, but email, title, and skill formatting still need validation.
AI Tool
🤖
Extracta.ai
AI Tool
🤖
Skima AI
Fast PDF resume parsing with dependable core fields and experience-year calculation, but weak structured output and supplemental coverage.
AI Tool
🤖
LlamaParse
Reliable PDF-to-Markdown conversion for hybrid reports, with strong hierarchy and table capture but weaker preservation of complex table semantics and embedded visuals.
AI Tool
🤖
HrFlow
HrFlow Review: AI Resume Parsing API Tested (2026)
AI Tool
🤖
OpenResume
Free browser resume parser that is handy for quick manual review on clean PDFs, but brittle field mapping and no JSON export make it unsuitable for API pipelines.
AI Tool
🤖
CVParserPro
Parses resume PDFs into structured candidate profiles quickly, but experience totals and education dates need manual review.
AI Tool
🤖
Hireability
Strong resume-to-JSON parsing on standard and messy single-column PDFs, but two-column layouts can break the result schema.
AI Tool
🤖
Docparser
Template-based resume parsing that works on one fixed layout, but breaks on varied resumes.
AI Tool
🤖
ResumeParser
AI Tool

Comments (0)

Please Log in to join the discussion.

Built by FutureSmart AI — the team behind AI Demos

Need a custom AI solution for this use case?

If you are looking to build a custom resume parsing, document extraction, or structured data extraction system for your business or internal workflow, email us at contact@futuresmart.ai.

Get a custom build

Found something inaccurate or missing? We try to keep our AI research accurate and useful. If you found outdated information, an issue, or have a suggestion, email us at collaborate@aidemos.com.

Back to Top