Median parse time
Built for real-time application flows, bulk profile imports and high-volume job board onboarding at scale.
The industry-leading resume parser built for technology companies
Affinda turns resumes into structured candidate data that ATS, job board and HR tech products can trust: 50+ fields, over 50 languages, median parse time under 1 second and over 95% accuracy.
Drop a resume in, see the JSON API reference
On mobile, review a sample JSON response instead of uploading a resume.
{
"name": { "raw": "Jordan Nguyen" },
"emails": [{ "value": "[email protected]" }],
"skills": [{ "name": "Python" }, { "name": "Salesforce" }],
"workExperience": [{ "jobTitle": "Product Manager" }]
}Parsing performance, by the numbers
Industry-leading extraction
Verified across core candidate fields under strict scoring. See the benchmark below.
Supports over 50 languages
Structured candidate, experience, education, skill and certification data across global hiring markets.
Results: strict-scoring accuracy by field
| Contact fields | Affinda | Comp 1 | Comp 2 | Comp 3 |
|---|---|---|---|---|
| Date of birth | 99% | 91% | 96% | 95% |
| First name | 97% | 89% | 95% | 76% |
| Last name | 95% | 88% | 91% | 73% |
| Email address | 89% | 87% | 92% | 90% |
| Phone number | 96% | 80% | 80% | 87% |
| Work history fields | Affinda | Comp 1 | Comp 2 | Comp 3 |
|---|---|---|---|---|
| Work organisation | 81% | 55% | 60% | 38% |
| Work title | 82% | 61% | 53% | 55% |
| Work start date | 83% | 69% | 64% | 43% |
| Work end date | 82% | 69% | 65% | 35% |
| Education fields | Affinda | Comp 1 | Comp 2 | Comp 3 |
|---|---|---|---|---|
| Education accreditation | 79% | 56% | 62% | 27% |
| Education organisation | 86% | 70% | 71% | 50% |
| Education end date | 82% | 66% | 65% | 36% |
| Summary results | Affinda | Comp 1 | Comp 2 | Comp 3 |
|---|---|---|---|---|
| Overall (all fields) | 88% | 73% | 59% | 74% |
| Core contact fields | 95%+ | 87% | 91% | 84% |
Accuracy benchmark
How Affinda compares under strict scoring
Affinda achieved over 95% accuracy on core candidate fields – 20% higher than the nearest competitor overall.
We tested against three leading global resume parsers using strict scoring: partial fields, false positives and false negatives all count as zero, and only fully correct predictions pass. The result: the tougher the test, the more Affinda stood out.
Secure resume parsing, built for production
Affinda gives ATS, job board and HR tech teams the security posture needed for candidate documents: certified controls, regional hosting options and clear privacy boundaries.
Certified controls
ISO 27001:2022 certified, SOC 2 Type II and GDPR compliant for enterprise hiring platforms.
Regional hosting options
APAC, EU and US data centres help teams meet local data residency requirements.
No training on customer data
Your customers' resumes and documents are never used to train our models.
Built to fit inside recruitment technology products
Affinda gives product and engineering teams the parser layer they would rather not maintain themselves: accurate extraction, predictable API behaviour, clear JSON and support when production workflows depend on it.
Evaluate output in a sandbox
Upload real sample resumes, inspect the JSON and compare Affinda against your current parser before changing production workflows.
Integrate through a documented API
Use Affinda directly from your ATS, job board or HR tech product with API docs, SDKs and support for production rollout.
Scale with support and controls
Move from testing to production with security, data residency and support controls expected by enterprise recruitment platforms.
Show engineers the request, response and schema
Use a sandbox key to post a resume and receive normalized JSON. This sample shows the integration shape without hiding the output behind a demo form.
curl -X POST https://api.affinda.com/v3/documents --oauth2-bearer <your-api-key> -F "[email protected]" -F "workspace=<workspace-identifier>" -F "documentType=<resume-document-type-identifier>"
from affinda import AffindaAPI
client = AffindaAPI(token="<your-api-key>")
with open("resume.pdf", "rb") as file:
document = client.create_document(file=file)
print(document.data)
{
"data": {
"name": { "raw": "Jordan Nguyen" },
"emails": [{ "value": "[email protected]" }],
"phoneNumbers": [{ "rawText": "+61 400 000 000" }],
"skills": [{ "name": "Python" }, { "name": "Product management" }],
"workExperience": [{ "jobTitle": "Product Manager", "organization": "ExampleCo" }]
}
}Proven in ATS, job board and HR tech products
SEEK, Bayt.com and Minotaur ATS use Affinda where resume parsing quality directly affects customer experience, onboarding and downstream matching workflows.
Michael Zhao, AI Product Manager, SEEK
High accuracy parsing across multiple languages
Improved customer experience thanks to better structured data
Stronger compliance with international data security and software standards
Akram Assaf, Chief Technology Officer, Bayt.com
40%
faster onboarding for jobseekers
6.5M
resumes parsed annually
Enterprise-grade compliance, privacy and security needs met
James (JP) Sutton, Founder and CEO, Minotaur ATS
200,000+
resumes parsed annually
95%
data extraction accuracy
3-4
seconds resume parsing speed
Pricing that scales down as you scale up
Published, consumption-based pricing with no guesswork. Per-document cost drops at every tier, and falls below US$0.01 at tailored volumes in the millions.
See the resume parser in context
A short product walkthrough for teams who want to understand how resume parsing fits into the broader recruitment AI suite.
Frequently asked questions
What is a resume parser and how does it support recruitment workflow automation?
A resume parser turns resumes and CVs into structured candidate data, including contact details, work history, education, skills and certifications. That data can then move into an ATS, matching engine or HR platform instead of being copied manually, making parsing the first step in a broader recruitment automation workflow. Learn more about how resume parsing works.
How does a resume parser API send candidate data into an ATS or HR platform?
A resume parser API receives a resume file and returns structured candidate fields that can be mapped into an ATS, CRM or HR product. The Affinda Resume Parser can sit behind an existing application, while integrations or API-based exports move the data into the system recruiters already use. Where the destination expects a specific structure, data transformations can shape the fields to match.
What structured data can a resume parser API return for ATS and HR-tech workflows?
A resume parser API can return personal information, employment history, education, skills, certifications, languages, locations and role-specific experience. HR-tech teams can use that data to create candidate profiles, support search and filtering, or feed matching workflows through the job description parser – so recruiters screen and shortlist faster, working from consistent fields they can validate before matching to a role. For teams weighing up the development effort, our guide to building or buying an ATS resume reader explains the trade-offs.
What is the difference between resume parsing, CV parsing and candidate matching?
Resume parsing and CV parsing describe the same core task: converting an applicant document into structured candidate data. Candidate matching happens after parsing, when the structured resume data is compared with a job description, skills profile or hiring criteria. Affinda supports both sides of that workflow through its resume parser and job description parser.
Can AI resume parsing handle different resume formats and languages?
Yes. Affinda's AI resume parsing is designed to handle varied layouts, file formats, regional terminology and candidate writing styles more reliably than fixed templates. The important question is whether the output stays consistent enough for ATS and matching workflows, with values checked against your rules where they need confirming. Our guide to OCR resume scanning explains why reading text is only one part of accurate resume parsing.
When should an HR-tech platform use a resume data API instead of building parsing in-house?
An HR-tech platform should use a resume data API when parsing is important to the product but not its core point of difference. Building and maintaining a parser means keeping up with changing formats, fields, languages and edge cases; using a specialist resume parser API lets the product team stay focused on the recruitment workflow, matching experience and customer-facing features.
How is candidate data kept secure and compliant in a resume parsing workflow?
Resumes carry personal information, so candidate data should be processed under recognised security and privacy standards. Affinda is ISO 27001:2022 certified, SOC 2 Type II and GDPR compliant, with enterprise-grade encryption and data residency options, and candidate documents are not used to train models. For HR-tech teams, that means resume parsing can be embedded in a product without taking on a new compliance burden of their own. See Affinda's security posture for the detail.
