--- title: "Parseur" type: "AI Tool" url: "https://aidemos.com/tools/parseur" description: "We turned messy PDF resumes into JSON, CSV, and Excel after one mailbox/template setup—but CGPA, languages, and skills came back flattened or omitted." category: "developer-tools" website: "https://parseur.com" published: "2026-07-13T16:29:31.372972+00:00" updated: "2026-08-09T18:39:45.230547+00:00" evidenceCount: 31 verifiedCount: 16 coverage: "dense" --- # Parseur Template-driven resume parsing that returns predictable JSON after one-time setup. ## TL;DR Verdict **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:** Free Free · Base Volume-based (slider pricing) · Scale Volume-based (lowest cost/page) · Enterprise Custom quote `Template-driven` · `PDF resumes` · `JSON/CSV/Excel` · `Flat fields` **Website:** [Visit Parseur](https://parseur.com) ## Evidence (first-party, tested) *31 tested cells · 16/31 artifact-verified. Cite a cell by its Evidence ID, e.g. `ev:parseur·multi-column-sidebar-resume-priya-sharma·accuracy`.* | Criterion | Scenario | Verdict | Proof | Evidence ID | | --- | --- | --- | --- | --- | | Accuracy | Multi-column sidebar resume — Priya Sharma | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-image-3-726e4e3fba8c.png) | `ev:parseur·multi-column-sidebar-resume-priya-sharma·accuracy` | | Accuracy | Clean single-column resume — Rugved Nichite | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-image-7-c5a350a2e9fe.png) | `ev:parseur·clean-single-column-resume-rugved-nichite·accuracy` | | Accuracy | Messy real-world resume — John Kumar | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-image-4-a76ecb3d1154.png) | `ev:parseur·messy-real-world-resume-john-kumar·accuracy` | | Accuracy | cross-scenario | ◐ mixed | 👁 observed | `ev:parseur·cross·accuracy` | | Automation level | cross-scenario | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-parseur-task1-template-setup-cbce417ccafc.png) | `ev:parseur·cross·automation-level` | | Contact info — name, email, phone, location: exact match | Multi-column sidebar resume — Priya Sharma | ✓ worked | 👁 observed | `ev:parseur·multi-column-sidebar-resume-priya-sharma·contact-info-exact-match` | | Custom field support | cross-scenario | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-parseur-task1-template-setup-cbce417ccafc.png) | `ev:parseur·cross·custom-field-support` | | Export | cross-scenario | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-parseur-task1-export-formats-488f44e5242e.png) | `ev:parseur·cross·export` | | Export format | cross-scenario | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-parseur-task1-export-formats-488f44e5242e.png) | `ev:parseur·cross·export-format` | | Field coverage | Multi-column sidebar resume — Priya Sharma | ◐ mixed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-pa4-cgpa-not-in-template-0842b8e5acd1.png) | `ev:parseur·multi-column-sidebar-resume-priya-sharma·field-coverage` | | Field coverage | Clean single-column resume — Rugved Nichite | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-image-7-c5a350a2e9fe.png) | `ev:parseur·clean-single-column-resume-rugved-nichite·field-coverage` | | Field coverage | Messy real-world resume — John Kumar | ◐ mixed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-parseur-task3-one-education-mock-ccafe2d4a20d.png) | `ev:parseur·messy-real-world-resume-john-kumar·field-coverage` | | Field coverage | cross-scenario | ◐ mixed | 👁 observed | `ev:parseur·cross·field-coverage` | | Free tier viability | cross-scenario | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-image-3-726e4e3fba8c.png) | `ev:parseur·cross·free-tier-viability` | | Input handling | cross-scenario | ◐ mixed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-parseur-task1-template-setup-cbce417ccafc.png) | `ev:parseur·cross·input-handling` | | Input handling | Multi-column sidebar resume — Priya Sharma | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-input2-priya-sharma-multicolumnresume-0e443ffc95c3.pdf) | `ev:parseur·multi-column-sidebar-resume-priya-sharma·input-handling` | | Input handling | Clean single-column resume — Rugved Nichite | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-input1-rugved-nichite-cleanresume-d776e5470f6f.pdf) | `ev:parseur·clean-single-column-resume-rugved-nichite·input-handling` | | Input handling | Messy real-world resume — John Kumar | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-input3-john-kumar-messyresume-75275a848042.pdf) | `ev:parseur·messy-real-world-resume-john-kumar·input-handling` | | Messy resume handling | Messy real-world resume — John Kumar | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-image-4-a76ecb3d1154.png) | `ev:parseur·messy-real-world-resume-john-kumar·messy-resume-handling` | | Multi-column handling | Multi-column sidebar resume — Priya Sharma | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-image-3-726e4e3fba8c.png) | `ev:parseur·multi-column-sidebar-resume-priya-sharma·multi-column-handling` | | Noise in output | cross-scenario | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-parseur-task1-template-setup-cbce417ccafc.png) | `ev:parseur·cross·noise-in-output` | | Output format | cross-scenario | ◐ mixed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-parseur-task1-export-formats-488f44e5242e.png) | `ev:parseur·cross·output-format` | | Output format | Clean single-column resume — Rugved Nichite | ✓ worked | 👁 observed | `ev:parseur·clean-single-column-resume-rugved-nichite·output-format` | | Output quality | Messy real-world resume — John Kumar | ⚠ struggled | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-parseur-task3-skills-flat-mock-bae2975459e2.png) | `ev:parseur·messy-real-world-resume-john-kumar·output-quality` | | Output quality | cross-scenario | ✗ failed | 👁 observed | `ev:parseur·cross·output-quality` | | Output quality | Clean single-column resume — Rugved Nichite | ◐ mixed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-pa2-cgpa-embedded-0ced843edb15.png) | `ev:parseur·clean-single-column-resume-rugved-nichite·output-quality` | | Output quality | Multi-column sidebar resume — Priya Sharma | ✗ failed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/parseur-pa6-work-location-missing-3e10d018df1a.png) | `ev:parseur·multi-column-sidebar-resume-priya-sharma·output-quality` | | Work experience — companies, titles, dates, task completeness | Clean single-column resume — Rugved Nichite | ✓ worked | 👁 observed | `ev:parseur·clean-single-column-resume-rugved-nichite·work-experience-completeness` | | Work experience — companies, titles, dates, task completeness | Multi-column sidebar resume — Priya Sharma | ✓ worked | 👁 observed | `ev:parseur·multi-column-sidebar-resume-priya-sharma·work-experience-completeness` | | Work experience — companies, titles, dates, task completeness | Messy real-world resume — John Kumar | ✓ worked | 👁 observed | `ev:parseur·messy-real-world-resume-john-kumar·work-experience-completeness` | | Work experience — companies, titles, dates, task completeness | cross-scenario | ✓ worked | 👁 observed | `ev:parseur·cross·work-experience-completeness` | > 🧾 = artifact-verified (proof captured) · 👁 = observed (noted, no artifact) · verdicts: worked / mixed / struggled / failed. > **Good fit once the template is defined** > > 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. ## Demo Recording [Video: Parseur demo recording (download MP4)](https://d3epheqghktydj.cloudfront.net/parseur-parsure-tool-demo-video-2e3ba17beb50.mp4) [▶️ Watch (streaming)](https://stream.futuresmart.ai/embed/b2c9eeb8-5c00-4d2c-91c6-e6ff479a31df) - [0:00 Introduction to Document Parsing Tool](https://stream.futuresmart.ai/embed/b2c9eeb8-5c00-4d2c-91c6-e6ff479a31df?t=0) - [2:07 Integration and Workflow Automation](https://stream.futuresmart.ai/embed/b2c9eeb8-5c00-4d2c-91c6-e6ff479a31df?t=127) - [2:48 Custom Field Configuration and Data Export](https://stream.futuresmart.ai/embed/b2c9eeb8-5c00-4d2c-91c6-e6ff479a31df?t=168) - [3:44 Advanced AI Capabilities and Multi-Document Processing](https://stream.futuresmart.ai/embed/b2c9eeb8-5c00-4d2c-91c6-e6ff479a31df?t=224) *Video — Screen recording walkthrough of Parseur's resume parsing workflow.* ## Feature-by-Feature Breakdown ### Mailbox-Based Resume Parsing **Verdict:** Works after setup. 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 ``` **Output:** > **Image** **Input:** **Output:** **Input:** **Output:** **Input:** > **File** **Output:** > **Image** **Bottom line:** Parseur is not zero-setup, but once the mailbox/template exists it reliably ingests varied PDF resumes. ### Schema-Guided Resume Parsing **Verdict:** Strong control, but only mapped fields return. 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. **Input:** > **File** **Output:** > **Image** **Input:** **Output:** **Input:** **Output:** **Input:** **Output:** **Input:** > **File** **Output:** > **Image** **Input:** > **File** **Output:** > **Image** **Input:** > **File** **Output:** > **Image** **Input:** **Output:** **Input:** > **File** **Output:** > **Image** **Input:** **Output:** **Input:** **Output:** **Input:** **Output:** **Input:** **Output:** ``` Both work experiences were extracted with job title, company, start/end dates, and readable contribution strings on the clean resume. ``` **Input:** **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. ### Multi-Format Export **Verdict:** Covers the common export 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 ``` **Output:** > **Image** **Bottom line:** Export coverage is straightforward and covers the common spreadsheet and API-friendly formats. ## Plans as of August 2026 Tested on the free plan. | Plan | Price | Notes | | --- | --- | --- | | Free ★ (tested) | 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.* ## Is It Right For You? **Use it 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 it 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. ## Classification - **Category:** developer-tools - **Subcategory:** apis - **Type:** text - **Built for:** Other ## Frequently Asked Questions **Q: Does Parseur require template setup before it can parse resumes?** Yes. The research shows a one-time mailbox/template setup step before the first parse. After that, the same setup processed resumes automatically. **Q: How did Parseur handle clean, multi-column, and messy resumes?** 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. **Q: Did Parseur extract CGPA reliably?** 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. **Q: Does Parseur return skills and certifications as arrays?** 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. **Q: Can Parseur capture languages and work-location fields automatically?** 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. **Q: What export formats were available?** JSON, CSV, and Excel were available from the Fields view. **Q: Was pricing captured in the research?** 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. ## Similar Tools AI tools similar to Parseur: - [Affinda](https://aidemos.com/tools/affinda) — Best overall resume parsing API here for clean, multi-column, and messy PDFs with rich structured JSON. - [Airparser](https://aidemos.com/tools/airparser) — Parses clean, multi-column, and messy resumes into structured JSON, but email, title, and skill formatting still need validation. - [Extracta.ai](https://aidemos.com/tools/extracta-labs) — Schema-first resume parsing that stays lean and predictable across clean, multi-column, and messy PDFs. - [Skima AI](https://aidemos.com/tools/skima-ai) — Fast PDF resume parsing with dependable core fields and experience-year calculation, but weak structured output and supplemental coverage. - [LlamaParse](https://aidemos.com/tools/llamaparse) — Versatile PDF parsing for Markdown and structured JSON, with strong recovery but some fidelity drift - [HrFlow](https://aidemos.com/tools/hrflow) — HrFlow is an API-first resume parser that covers core fields well, but needs cleanup for phones, casing, and certifications. - [OpenResume](https://aidemos.com/tools/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. - [CVParserPro](https://aidemos.com/tools/cvparserpro) — Parses resume PDFs into structured candidate profiles quickly, but experience totals and education dates need manual review. - [Hireability](https://aidemos.com/tools/hireability) — Strong resume-to-JSON parsing on standard and messy single-column PDFs, but two-column layouts can break the result schema. - [Docparser](https://aidemos.com/tools/docparser) — Template-based resume parsing that works on one fixed layout, but breaks on varied resumes. ## 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](mailto:contact@futuresmart.ai). ### 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](mailto:collaborate@aidemos.com).